diff --git a/.github/workflows/test-main-features.yml b/.github/workflows/test-main-features.yml new file mode 100644 index 00000000..1095bf1c --- /dev/null +++ b/.github/workflows/test-main-features.yml @@ -0,0 +1,150 @@ +name: Test Main Features + +on: + push: + paths: + - 'tests/test_main_features.py' + - 'swarms/**' + - 'requirements.txt' + - 'pyproject.toml' + branches: [ "master" ] + pull_request: + paths: + - 'tests/test_main_features.py' + - 'swarms/**' + - 'requirements.txt' + - 'pyproject.toml' + branches: [ "master" ] + workflow_dispatch: # Allow manual triggering + +jobs: + test-main-features: + runs-on: ubuntu-latest + timeout-minutes: 30 + + steps: + - name: Checkout code + uses: actions/checkout@v5 + + - name: Set up Python 3.10 + uses: actions/setup-python@v6 + with: + python-version: "3.10" + + - name: Cache pip dependencies + uses: actions/cache@v4 + with: + path: ~/.cache/pip + key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements.txt') }} + restore-keys: | + ${{ runner.os }}-pip- + + - name: Install Poetry + run: | + curl -sSL https://install.python-poetry.org | python3 - + echo "$HOME/.local/bin" >> $GITHUB_PATH + + - name: Configure Poetry + run: | + poetry config virtualenvs.create true + poetry config virtualenvs.in-project true + + - name: Install dependencies + run: | + poetry install --with test --no-dev + + - name: Set up environment variables + run: | + echo "OPENAI_API_KEY=${{ secrets.OPENAI_API_KEY }}" >> $GITHUB_ENV + echo "ANTHROPIC_API_KEY=${{ secrets.ANTHROPIC_API_KEY }}" >> $GITHUB_ENV + echo "GOOGLE_API_KEY=${{ secrets.GOOGLE_API_KEY }}" >> $GITHUB_ENV + echo "COHERE_API_KEY=${{ secrets.COHERE_API_KEY }}" >> $GITHUB_ENV + echo "HUGGINGFACE_API_KEY=${{ secrets.HUGGINGFACE_API_KEY }}" >> $GITHUB_ENV + echo "REPLICATE_API_KEY=${{ secrets.REPLICATE_API_KEY }}" >> $GITHUB_ENV + echo "TOGETHER_API_KEY=${{ secrets.TOGETHER_API_KEY }}" >> $GITHUB_ENV + + - name: Run Main Features Tests + run: | + cd /Users/swarms_wd/Desktop/research/swarms + poetry run python tests/test_main_features.py + + - name: Upload test results + uses: actions/upload-artifact@v4 + if: always() + with: + name: test-results + path: test_runs/ + retention-days: 7 + + - name: Comment on PR with test results + if: github.event_name == 'pull_request' && always() + uses: actions/github-script@v7 + with: + script: | + const fs = require('fs'); + const path = require('path'); + + try { + // Look for test result files + const testRunsDir = 'test_runs'; + if (fs.existsSync(testRunsDir)) { + const files = fs.readdirSync(testRunsDir); + const latestReport = files + .filter(f => f.endsWith('.md')) + .sort() + .pop(); + + if (latestReport) { + const reportPath = path.join(testRunsDir, latestReport); + const reportContent = fs.readFileSync(reportPath, 'utf8'); + + // Extract summary from markdown + const summaryMatch = reportContent.match(/## Summary\n\n(.*?)\n\n## Detailed Results/s); + const summary = summaryMatch ? summaryMatch[1] : 'Test results available in artifacts'; + + github.rest.issues.createComment({ + issue_number: context.issue.number, + owner: context.repo.owner, + repo: context.repo.repo, + body: `## Main Features Test Results\n\n${summary}\n\nšŸ“Š Full test report available in artifacts.` + }); + } + } + } catch (error) { + console.log('Could not read test results:', error.message); + } + + test-coverage: + runs-on: ubuntu-latest + if: github.event_name == 'pull_request' + needs: test-main-features + + steps: + - name: Checkout code + uses: actions/checkout@v5 + + - name: Set up Python 3.10 + uses: actions/setup-python@v6 + with: + python-version: "3.10" + + - name: Install Poetry + run: | + curl -sSL https://install.python-poetry.org | python3 - + echo "$HOME/.local/bin" >> $GITHUB_PATH + + - name: Install dependencies + run: | + poetry install --with test + + - name: Run coverage analysis + run: | + poetry run pytest tests/test_main_features.py --cov=swarms --cov-report=xml --cov-report=html + + - name: Upload coverage to Codecov + uses: codecov/codecov-action@v4 + with: + file: ./coverage.xml + flags: main-features + name: main-features-coverage + fail_ci_if_error: false diff --git a/agent_mcp.py b/agent_mcp.py new file mode 100644 index 00000000..89de8697 --- /dev/null +++ b/agent_mcp.py @@ -0,0 +1,23 @@ +from swarms import Agent +from swarms.prompts.finance_agent_sys_prompt import ( + FINANCIAL_AGENT_SYS_PROMPT, +) + +agent = Agent( + agent_name="Financial-Analysis-Agent", # Name of the agent + agent_description="Personal finance advisor agent", # Description of the agent's role + system_prompt=FINANCIAL_AGENT_SYS_PROMPT, # System prompt for financial tasks + max_loops=1, + mcp_urls=[ + "http://0.0.0.0:5932/mcp", + ], + model_name="gpt-4o-mini", + output_type="all", +) + +out = agent.run( + "Use the discover agent tools to find what agents are available and provide a summary" +) + +# Print the output from the agent's run method. +print(out) diff --git a/docs/examples/index.md b/docs/examples/index.md index a23f7b06..bb1ed712 100644 --- a/docs/examples/index.md +++ b/docs/examples/index.md @@ -242,12 +242,20 @@ This index organizes **100+ production-ready examples** from our [Swarms Example | Business | [Business Strategy](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/applications/business_strategy/business_strategy_graph/growth_agent.py) | Strategic planning and business development swarm | | Research | [Astronomy Research](https://github.com/The-Swarm-Corporation/swarms-examples/blob/main/examples/applications/astronomy/multiversal_detection/test.py) | Collaborative space research and astronomical analysis | -## Additional Resources -- [Github](https://github.com/kyegomez/swarms) +------- -- Discord (https://t.co/zlLe07AqUX) +## Connect With Us -- Telegram (https://t.co/dSRy143zQv) +Join our community of agent engineers and researchers for technical support, cutting-edge updates, and exclusive access to world-class agent engineering insights! -- X Community (https://x.com/i/communities/1875452887414804745) \ No newline at end of file +| Platform | Description | Link | +|----------|-------------|------| +| šŸ“š Documentation | Official documentation and guides | [docs.swarms.world](https://docs.swarms.world) | +| šŸ“ Blog | Latest updates and technical articles | [Medium](https://medium.com/@kyeg) | +| šŸ’¬ Discord | Live chat and community support | [Join Discord](https://discord.gg/EamjgSaEQf) | +| šŸ¦ Twitter | Latest news and announcements | [@swarms_corp](https://twitter.com/swarms_corp) | +| šŸ‘„ LinkedIn | Professional network and updates | [The Swarm Corporation](https://www.linkedin.com/company/the-swarm-corporation) | +| šŸ“ŗ YouTube | Tutorials and demos | [Swarms Channel](https://www.youtube.com/channel/UC9yXyitkbU_WSy7bd_41SqQ) | +| šŸŽ« Events | Join our community events | [Sign up here](https://lu.ma/5p2jnc2v) | +| šŸš€ Onboarding Session | Get onboarded with Kye Gomez, creator and lead maintainer of Swarms | [Book Session](https://cal.com/swarms/swarms-onboarding-session) | diff --git a/docs/examples/templates.md b/docs/examples/templates.md index 1c4471f1..8c190cf4 100644 --- a/docs/examples/templates.md +++ b/docs/examples/templates.md @@ -86,6 +86,7 @@ The Swarms framework is a powerful multi-agent orchestration platform that enabl | [Marketing-Swarm-Template](https://github.com/The-Swarm-Corporation/Marketing-Swarm-Template) | Marketing campaign automation template | Marketing Automation | Business | | [Multi-Agent-Marketing-Course](https://github.com/The-Swarm-Corporation/Multi-Agent-Marketing-Course) | Educational course on multi-agent marketing | Marketing Education | Business | | [NewsAgent](https://github.com/The-Swarm-Corporation/NewsAgent) | News aggregation and analysis agent | News Analysis | Business | +|[Product-Marketing-Agency](https://github.com/The-Swarm-Corporation/Product-Marketing-Agency) | Product marketing content generation | Product Marketing | Business | ### Legal Services diff --git a/docs/llm.txt b/docs/llm.txt index 145c3b1e..6336016d 100644 --- a/docs/llm.txt +++ b/docs/llm.txt @@ -6853,10 +6853,10 @@ pip3 install -U swarms | Quickstart | [Get Started](https://docs.swarms.world/en/latest/swarms/install/quickstart/) | | Environment Setup | [Environment Configuration](https://docs.swarms.world/en/latest/swarms/install/workspace_manager/) | | Environment Variables | [Environment Variables](https://docs.swarms.world/en/latest/swarms/install/env/) | -| Swarms CLI | [CLI Documentation](https://docs.swarms.world/en/latest/swarms/cli/main/) | +| Swarms CLI | [CLI Documentation](https://docs.swarms.world/en/latest/swarms/cli/cli_reference/) | | Agent Internal Mechanisms | [Agent Architecture](https://docs.swarms.world/en/latest/swarms/framework/agents_explained/) | | Agent API | [Agent API](https://docs.swarms.world/en/latest/swarms/structs/agent/) | -| Managing Prompts in Production | [Prompts Management](https://docs.swarms.world/en/latest/swarms/prompts/main/) | +| Managing Prompts in Production | [Prompts Management](https://github.com/kyegomez/swarms/tree/master/swarms/prompts/) | | Integrating External Agents | [External Agents Integration](https://docs.swarms.world/en/latest/swarms/agents/external_party_agents/) | | Creating Agents from YAML | [YAML Agent Creation](https://docs.swarms.world/en/latest/swarms/agents/create_agents_yaml/) | | Why You Need Swarms | [Why MultiAgent Collaboration](https://docs.swarms.world/en/latest/swarms/concept/why/) | @@ -6991,7 +6991,7 @@ The Swarms protocol is organized into several key layers, each responsible for a agents and swarms. - **Prompts (`swarms/prompts`)**: Houses prompt templates, system prompts, and agent-specific prompts for LLM-based agents. See - [Prompts Management](https://docs.swarms.world/en/latest/swarms/prompts/main/) + [Prompts Management](https://github.com/kyegomez/swarms/tree/master/swarms/prompts/) - **Telemetry (`swarms/telemetry`)**: Handles logging, monitoring, and bootup routines for observability and debugging. @@ -6999,7 +6999,7 @@ The Swarms protocol is organized into several key layers, each responsible for a safety and consistency. - **CLI (`swarms/cli`)**: Provides command-line utilities for agent creation, management, and orchestration. See [CLI Documentation] - (https://docs.swarms.world/en/latest/swarms/cli/main/) + (https://docs.swarms.world/en/latest/swarms/cli/cli_reference/) --- @@ -7218,7 +7218,7 @@ diy_memory/) - `prompt.py`, `reasoning_prompt.py`, `multi_agent_collab_prompt.py`, etc. -- [Prompts Management](https://docs.swarms.world/en/latest/swarms/prompts/main/) +- [Prompts Management](https://github.com/kyegomez/swarms/tree/master/swarms/prompts/) ### `artifacts/` @@ -7265,7 +7265,7 @@ diy_memory/) - `main.py`, `create_agent.py`, `onboarding_process.py`. -- [CLI Documentation](https://docs.swarms.world/en/latest/swarms/cli/main/) +- [CLI Documentation](https://docs.swarms.world/en/latest/swarms/cli/cli_reference/) --- @@ -7287,7 +7287,7 @@ For example, a typical workflow might involve: - Logging all actions and outputs for traceability and debugging. -For more advanced examples, see the [Examples Overview](https://docs.swarms.world/en/latest/examples/index/). +For more advanced examples, see the [Examples Overview](https://docs.swarms.world/en/latest/examples/). --- @@ -7321,9 +7321,9 @@ For more on the philosophy and architecture, see [Development Philosophy & Princ | BaseTool Reference | [BaseTool Reference](https://docs.swarms.world/en/latest/swarms/tools/base_tool/) | Reference for the BaseTool class | | Reasoning Agents Overview | [Reasoning Agents Overview](https://docs.swarms.world/en/latest/swarms/agents/reasoning_agents_overview/) | Overview of reasoning agents | | Multi-Agent Architectures Overview | [Multi-Agent Architectures Overview](https://docs.swarms.world/en/latest/swarms/concept/swarm_architectures/) | Multi-agent system architectures | -| Examples Overview | [Examples Overview](https://docs.swarms.world/en/latest/examples/index/) | Example projects and use cases | -| CLI Documentation | [CLI Documentation](https://docs.swarms.world/en/latest/swarms/cli/main/) | Command-line interface documentation | -| Prompts Management | [Prompts Management](https://docs.swarms.world/en/latest/swarms/prompts/main/) | Managing and customizing prompts | +| Examples Overview | [Examples Overview](https://docs.swarms.world/en/latest/examples/) | Example projects and use cases | +| CLI Documentation | [CLI Documentation](https://docs.swarms.world/en/latest/swarms/cli/cli_reference/) | Command-line interface documentation | +| Prompts Management | [Prompts Management](https://github.com/kyegomez/swarms/tree/master/swarms/prompts/) | Managing and customizing prompts | | Development Philosophy & Principles | [Development Philosophy & Principles](https://docs.swarms.world/en/latest/swarms/concept/philosophy/) | Framework philosophy and guiding principles | | Understanding Swarms Architecture | [Understanding Swarms Architecture](https://docs.swarms.world/en/latest/swarms/concept/framework_architecture/) | In-depth look at Swarms architecture | | SIP Guidelines and Template | [SIP Guidelines and Template](https://docs.swarms.world/en/latest/protocol/sip/) | Swarms Improvement Proposal process and template | diff --git a/docs/protocol/overview.md b/docs/protocol/overview.md index 49c2a8db..1c52e7dd 100644 --- a/docs/protocol/overview.md +++ b/docs/protocol/overview.md @@ -85,7 +85,7 @@ The Swarms protocol is organized into several key layers, each responsible for a agents and swarms. - **Prompts (`swarms/prompts`)**: Houses prompt templates, system prompts, and agent-specific prompts for LLM-based agents. See - [Prompts Management](https://docs.swarms.world/en/latest/swarms/prompts/main/) + [Prompts Management](https://github.com/kyegomez/swarms/tree/master/swarms/prompts/) - **Telemetry (`swarms/telemetry`)**: Handles logging, monitoring, and bootup routines for observability and debugging. @@ -93,7 +93,7 @@ The Swarms protocol is organized into several key layers, each responsible for a safety and consistency. - **CLI (`swarms/cli`)**: Provides command-line utilities for agent creation, management, and orchestration. See [CLI Documentation] - (https://docs.swarms.world/en/latest/swarms/cli/main/) + (https://docs.swarms.world/en/latest/swarms/cli/cli_reference/) --- @@ -312,7 +312,7 @@ diy_memory/) - `prompt.py`, `reasoning_prompt.py`, `multi_agent_collab_prompt.py`, etc. -- [Prompts Management](https://docs.swarms.world/en/latest/swarms/prompts/main/) +- [Prompts Management](https://github.com/kyegomez/swarms/tree/master/swarms/prompts/) ### `artifacts/` @@ -359,7 +359,7 @@ diy_memory/) - `main.py`, `create_agent.py`, `onboarding_process.py`. -- [CLI Documentation](https://docs.swarms.world/en/latest/swarms/cli/main/) +- [CLI Documentation](https://docs.swarms.world/en/latest/swarms/cli/cli_reference/) --- @@ -381,7 +381,7 @@ For example, a typical workflow might involve: - Logging all actions and outputs for traceability and debugging. -For more advanced examples, see the [Examples Overview](https://docs.swarms.world/en/latest/examples/index/). +For more advanced examples, see the [Examples Overview](https://docs.swarms.world/en/latest/examples/). --- @@ -415,9 +415,9 @@ For more on the philosophy and architecture, see [Development Philosophy & Princ | BaseTool Reference | [BaseTool Reference](https://docs.swarms.world/en/latest/swarms/tools/base_tool/) | Reference for the BaseTool class | | Reasoning Agents Overview | [Reasoning Agents Overview](https://docs.swarms.world/en/latest/swarms/agents/reasoning_agents_overview/) | Overview of reasoning agents | | Multi-Agent Architectures Overview | [Multi-Agent Architectures Overview](https://docs.swarms.world/en/latest/swarms/concept/swarm_architectures/) | Multi-agent system architectures | -| Examples Overview | [Examples Overview](https://docs.swarms.world/en/latest/examples/index/) | Example projects and use cases | -| CLI Documentation | [CLI Documentation](https://docs.swarms.world/en/latest/swarms/cli/main/) | Command-line interface documentation | -| Prompts Management | [Prompts Management](https://docs.swarms.world/en/latest/swarms/prompts/main/) | Managing and customizing prompts | +| Examples Overview | [Examples Overview](https://docs.swarms.world/en/latest/examples/) | Example projects and use cases | +| CLI Documentation | [CLI Documentation](https://docs.swarms.world/en/latest/swarms/cli/cli_reference/) | Command-line interface documentation | +| Prompts Management | [Prompts Management](https://github.com/kyegomez/swarms/tree/master/swarms/prompts/) | Managing and customizing prompts | | Development Philosophy & Principles | [Development Philosophy & Principles](https://docs.swarms.world/en/latest/swarms/concept/philosophy/) | Framework philosophy and guiding principles | | Understanding Swarms Architecture | [Understanding Swarms Architecture](https://docs.swarms.world/en/latest/swarms/concept/framework_architecture/) | In-depth look at Swarms architecture | | SIP Guidelines and Template | [SIP Guidelines and Template](https://docs.swarms.world/en/latest/protocol/sip/) | Swarms Improvement Proposal process and template | diff --git a/docs/swarms/structs/forest_swarm.md b/docs/swarms/structs/forest_swarm.md index 519aed8b..b9a12799 100644 --- a/docs/swarms/structs/forest_swarm.md +++ b/docs/swarms/structs/forest_swarm.md @@ -2,7 +2,7 @@ This documentation describes the **ForestSwarm** that organizes agents into trees. Each agent specializes in processing specific tasks. Trees are collections of agents, each assigned based on their relevance to a task through keyword extraction and **litellm-based embedding similarity**. -The architecture allows for efficient task assignment by selecting the most relevant agent from a set of trees. Tasks are processed asynchronously, with agents selected based on task relevance, calculated by the similarity of system prompts and task keywords using **litellm embeddings** and cosine similarity calculations. +The architecture allows for efficient task assignment by selecting the most relevant agent from a set of trees. Tasks are processed with agents selected based on task relevance, calculated by the similarity of system prompts and task keywords using **litellm embeddings** and cosine similarity calculations. ## Module Path: `swarms.structs.tree_swarm` @@ -11,24 +11,30 @@ The architecture allows for efficient task assignment by selecting the most rele ### Utility Functions #### `extract_keywords(prompt: str, top_n: int = 5) -> List[str]` + Extracts relevant keywords from a text prompt using basic word splitting and frequency counting. **Parameters:** + - `prompt` (str): The text to extract keywords from - `top_n` (int): Maximum number of keywords to return **Returns:** + - `List[str]`: List of extracted keywords sorted by frequency #### `cosine_similarity(vec1: List[float], vec2: List[float]) -> float` + Calculates the cosine similarity between two embedding vectors. **Parameters:** + - `vec1` (List[float]): First embedding vector - `vec2` (List[float]): Second embedding vector **Returns:** -- `float`: Cosine similarity score between -1 and 1 + +- `float`: Cosine similarity score between 0 and 1 --- @@ -36,25 +42,29 @@ Calculates the cosine similarity between two embedding vectors. `TreeAgent` represents an individual agent responsible for handling a specific task. Agents are initialized with a **system prompt** and use **litellm embeddings** to dynamically determine their relevance to a given task. -#### Attributes +#### TreeAgent Attributes | **Attribute** | **Type** | **Description** | |--------------------------|------------------|---------------------------------------------------------------------------------| +| `name` | `str` | Name of the agent | +| `description` | `str` | Description of the agent | | `system_prompt` | `str` | A string that defines the agent's area of expertise and task-handling capability.| -| `llm` | `callable` | The language model (LLM) used to process tasks (e.g., GPT-4). | +| `model_name` | `str` | Name of the language model to use (default: "gpt-4.1") | | `agent_name` | `str` | The name of the agent. | | `system_prompt_embedding`| `List[float]` | **litellm-generated embedding** of the system prompt for similarity-based task matching.| | `relevant_keywords` | `List[str]` | Keywords dynamically extracted from the system prompt to assist in task matching.| | `distance` | `Optional[float]`| The computed distance between agents based on embedding similarity. | | `embedding_model_name` | `str` | **Name of the litellm embedding model** (default: "text-embedding-ada-002"). | +| `verbose` | `bool` | Whether to enable verbose logging | -#### Methods +#### TreeAgent Methods | **Method** | **Input** | **Output** | **Description** | |--------------------|---------------------------------|--------------------|---------------------------------------------------------------------------------| +| `__init__(name, description, system_prompt, model_name, agent_name, embedding_model_name, verbose, *args, **kwargs)` | Various initialization parameters | `None` | Initializes a TreeAgent with litellm embedding capabilities | | `_get_embedding(text: str)` | `text: str` | `List[float]` | **Internal method to generate embeddings using litellm.** | | `calculate_distance(other_agent: TreeAgent)` | `other_agent: TreeAgent` | `float` | Calculates the **cosine similarity distance** between this agent and another agent.| -| `run_task(task: str)` | `task: str` | `Any` | Executes the task, logs the input/output, and returns the result. | +| `run_task(task: str, img: str = None, *args, **kwargs)` | `task: str, img: str, *args, **kwargs` | `Any` | Executes the task, logs the input/output, and returns the result. | | `is_relevant_for_task(task: str, threshold: float = 0.7)` | `task: str, threshold: float` | `bool` | Checks if the agent is relevant for the task using **keyword matching and litellm embedding similarity**.| --- @@ -63,28 +73,30 @@ Calculates the cosine similarity between two embedding vectors. `Tree` organizes multiple agents into a hierarchical structure, where agents are sorted based on their relevance to tasks using **litellm embeddings**. -#### Attributes +#### Tree Attributes | **Attribute** | **Type** | **Description** | |--------------------------|------------------|---------------------------------------------------------------------------------| | `tree_name` | `str` | The name of the tree (represents a domain of agents, e.g., "Financial Tree"). | | `agents` | `List[TreeAgent]`| List of agents belonging to this tree, **sorted by embedding-based distance**. | +| `verbose` | `bool` | Whether to enable verbose logging | -#### Methods +#### Tree Methods | **Method** | **Input** | **Output** | **Description** | |--------------------|---------------------------------|--------------------|---------------------------------------------------------------------------------| +| `__init__(tree_name: str, agents: List[TreeAgent], verbose: bool = False)` | `tree_name: str, agents: List[TreeAgent], verbose: bool` | `None` | Initializes a tree of agents | | `calculate_agent_distances()` | `None` | `None` | **Calculates and assigns distances between agents based on litellm embedding similarity of prompts.** | | `find_relevant_agent(task: str)` | `task: str` | `Optional[TreeAgent]` | **Finds the most relevant agent for a task based on keyword and litellm embedding similarity.** | | `log_tree_execution(task: str, selected_agent: TreeAgent, result: Any)` | `task: str, selected_agent: TreeAgent, result: Any` | `None` | Logs details of the task execution by the selected agent. | --- -### Class: `ForestSwarm` +### Class: `ForestSwarm` `ForestSwarm` is the main class responsible for managing multiple trees. It oversees task delegation by finding the most relevant tree and agent for a given task using **litellm embeddings**. -#### Attributes +#### ForestSwarm Attributes | **Attribute** | **Type** | **Description** | |--------------------------|------------------|---------------------------------------------------------------------------------| @@ -92,42 +104,51 @@ Calculates the cosine similarity between two embedding vectors. | `description` | `str` | Description of the forest swarm. | | `trees` | `List[Tree]` | List of trees containing agents organized by domain. | | `shared_memory` | `Any` | Shared memory object for inter-tree communication. | -| `rules` | `str` | Rules governing the forest swarm behavior. | +| `verbose` | `bool` | Whether to enable verbose logging | +| `save_file_path` | `str` | File path for saving conversation logs | | `conversation` | `Conversation` | Conversation object for tracking interactions. | -#### Methods +#### ForestSwarm Methods | **Method** | **Input** | **Output** | **Description** | |--------------------|---------------------------------|--------------------|---------------------------------------------------------------------------------| +| `__init__(name, description, trees, shared_memory, rules, verbose, *args, **kwargs)` | Various initialization parameters | `None` | Initialize a ForestSwarm with multiple trees of agents | | `find_relevant_tree(task: str)` | `task: str` | `Optional[Tree]` | **Searches across all trees to find the most relevant tree based on litellm embedding similarity.**| | `run(task: str, img: str = None, *args, **kwargs)` | `task: str, img: str, *args, **kwargs` | `Any` | **Executes the task by finding the most relevant agent from the relevant tree using litellm embeddings.**| +| `batched_run(tasks: List[str], *args, **kwargs)` | `tasks: List[str], *args, **kwargs` | `List[Any]` | **Executes multiple tasks by finding the most relevant agent for each task.**| --- ### Pydantic Models for Logging #### `AgentLogInput` + Input log model for tracking agent task execution. **Fields:** + - `log_id` (str): Unique identifier for the log entry - `agent_name` (str): Name of the agent executing the task - `task` (str): Description of the task being executed - `timestamp` (datetime): When the task was started #### `AgentLogOutput` + Output log model for tracking agent task completion. **Fields:** + - `log_id` (str): Unique identifier for the log entry - `agent_name` (str): Name of the agent that completed the task - `result` (Any): Result/output from the task execution - `timestamp` (datetime): When the task was completed #### `TreeLog` + Tree execution log model for tracking tree-level operations. **Fields:** + - `log_id` (str): Unique identifier for the log entry - `tree_name` (str): Name of the tree that executed the task - `task` (str): Description of the task that was executed @@ -145,49 +166,72 @@ from swarms.structs.tree_swarm import TreeAgent, Tree, ForestSwarm # Create agents with varying system prompts and dynamically generated distances/keywords agents_tree1 = [ TreeAgent( + name="Financial Advisor", system_prompt="I am a financial advisor specializing in investment planning, retirement strategies, and tax optimization for individuals and businesses.", agent_name="Financial Advisor", + verbose=True ), TreeAgent( + name="Tax Expert", system_prompt="I am a tax expert with deep knowledge of corporate taxation, Delaware incorporation benefits, and free tax filing options for businesses.", agent_name="Tax Expert", + verbose=True ), TreeAgent( + name="Retirement Planner", system_prompt="I am a retirement planning specialist who helps individuals and businesses create comprehensive retirement strategies and investment plans.", agent_name="Retirement Planner", + verbose=True ), ] agents_tree2 = [ TreeAgent( + name="Stock Analyst", system_prompt="I am a stock market analyst who provides insights on market trends, stock recommendations, and portfolio optimization strategies.", agent_name="Stock Analyst", + verbose=True ), TreeAgent( + name="Investment Strategist", system_prompt="I am an investment strategist specializing in portfolio diversification, risk management, and market analysis.", agent_name="Investment Strategist", + verbose=True ), TreeAgent( + name="ROTH IRA Specialist", system_prompt="I am a ROTH IRA specialist who helps individuals optimize their retirement accounts and tax advantages.", agent_name="ROTH IRA Specialist", + verbose=True ), ] # Create trees -tree1 = Tree(tree_name="Financial Services Tree", agents=agents_tree1) -tree2 = Tree(tree_name="Investment & Trading Tree", agents=agents_tree2) +tree1 = Tree(tree_name="Financial Services Tree", agents=agents_tree1, verbose=True) +tree2 = Tree(tree_name="Investment & Trading Tree", agents=agents_tree2, verbose=True) # Create the ForestSwarm forest_swarm = ForestSwarm( name="Financial Services Forest", description="A comprehensive financial services multi-agent system", - trees=[tree1, tree2] + trees=[tree1, tree2], + verbose=True ) # Run a task task = "Our company is incorporated in Delaware, how do we do our taxes for free?" output = forest_swarm.run(task) print(output) + +# Run multiple tasks +tasks = [ + "What are the best investment strategies for retirement?", + "How do I file taxes for my Delaware corporation?", + "What's the current market outlook for tech stocks?" +] +results = forest_swarm.batched_run(tasks) +for i, result in enumerate(results): + print(f"Task {i+1} result: {result}") ``` --- @@ -203,12 +247,14 @@ print(output) - Searches through all trees using **cosine similarity** - Finds the most relevant agent based on **embedding similarity and keyword matching** 6. **Task Execution**: The selected agent processes the task, and the result is returned and logged. +7. **Batched Processing**: Multiple tasks can be processed using the `batched_run` method for efficient batch processing. ```plaintext Task: "Our company is incorporated in Delaware, how do we do our taxes for free?" ``` -**Process**: +**Process:** + - The system generates **litellm embeddings** for the task - Searches through the `Financial Services Tree` and `Investment & Trading Tree` - Uses **cosine similarity** to find the most relevant agent (likely the "Tax Expert") @@ -219,20 +265,29 @@ Task: "Our company is incorporated in Delaware, how do we do our taxes for free? ## Key Features ### **litellm Integration** + - **Embedding Generation**: Uses litellm's `embedding()` function for generating high-quality embeddings - **Model Flexibility**: Supports various embedding models (default: "text-embedding-ada-002") - **Error Handling**: Robust fallback mechanisms for embedding failures ### **Semantic Similarity** + - **Cosine Similarity**: Implements efficient cosine similarity calculations for vector comparisons - **Threshold-based Selection**: Configurable similarity thresholds for agent selection - **Hybrid Matching**: Combines keyword matching with semantic similarity for optimal results ### **Dynamic Agent Organization** + - **Automatic Distance Calculation**: Agents are automatically organized by semantic similarity - **Real-time Relevance**: Task relevance is calculated dynamically using current embeddings - **Scalable Architecture**: Easy to add/remove agents and trees without manual configuration +### **Batch Processing** + +- **Batched Execution**: Process multiple tasks efficiently using `batched_run` method +- **Parallel Processing**: Each task is processed independently with the most relevant agent +- **Result Aggregation**: All results are returned as a list for easy processing + --- ## Analysis of the Swarm Architecture @@ -243,7 +298,7 @@ The **ForestSwarm Architecture** leverages a hierarchical structure (forest) com - **Task Specialization**: Each agent is specialized, which ensures that tasks are matched with the most appropriate agent based on **litellm embedding similarity** and expertise. - **Dynamic Matching**: The architecture uses both keyword-based and **litellm embedding-based matching** to assign tasks, ensuring a high level of accuracy in agent selection. - **Logging and Accountability**: Each task execution is logged in detail, providing transparency and an audit trail of which agent handled which task and the results produced. -- **Asynchronous Task Execution**: The architecture can be adapted for asynchronous task processing, making it scalable and suitable for large-scale task handling in real-time systems. +- **Batch Processing**: The architecture supports efficient batch processing of multiple tasks simultaneously. --- @@ -274,6 +329,13 @@ graph TD P --> Q[Execute Task] Q --> R[Log Results] end + + subgraph Batch Processing + S[Multiple Tasks] --> T[Process Each Task] + T --> U[Find Relevant Agent per Task] + U --> V[Execute All Tasks] + V --> W[Return Results List] + end ``` ### Explanation of the Diagram @@ -283,6 +345,7 @@ graph TD - **Agents**: Each agent within the tree is responsible for handling tasks in its area of expertise. Agents within a tree are organized based on their **litellm embedding similarity** (distance). - **Embedding Process**: Shows how **litellm embeddings** are used for similarity calculations and agent selection. - **Task Processing**: Illustrates the complete workflow from task input to result logging. +- **Batch Processing**: Shows how multiple tasks can be processed efficiently using the `batched_run` method. --- @@ -295,6 +358,7 @@ python test_forest_swarm.py ``` The test suite covers: + - **Utility Functions**: `extract_keywords`, `cosine_similarity` - **Pydantic Models**: `AgentLogInput`, `AgentLogOutput`, `TreeLog` - **Core Classes**: `TreeAgent`, `Tree`, `ForestSwarm` @@ -308,8 +372,11 @@ The test suite covers: This **ForestSwarm Architecture** provides an efficient, scalable, and accurate architecture for delegating and executing tasks based on domain-specific expertise. The combination of hierarchical organization, **litellm-based semantic similarity**, dynamic task matching, and comprehensive logging ensures reliability, performance, and transparency in task execution. **Key Advantages:** + - **High Accuracy**: litellm embeddings provide superior semantic understanding - **Scalability**: Easy to add new agents, trees, and domains - **Flexibility**: Configurable similarity thresholds and embedding models - **Robustness**: Comprehensive error handling and fallback mechanisms -- **Transparency**: Detailed logging and audit trails for all operations \ No newline at end of file +- **Transparency**: Detailed logging and audit trails for all operations +- **Batch Processing**: Efficient processing of multiple tasks simultaneously +- **Verbose Logging**: Comprehensive logging at all levels for debugging and monitoring \ No newline at end of file diff --git a/docs/swarms/structs/sequential_workflow.md b/docs/swarms/structs/sequential_workflow.md index b8a50e92..c7fd6cd0 100644 --- a/docs/swarms/structs/sequential_workflow.md +++ b/docs/swarms/structs/sequential_workflow.md @@ -1,283 +1,375 @@ # SequentialWorkflow Documentation **Overview:** -A Sequential Swarm architecture processes tasks in a linear sequence. Each agent completes its task before passing the result to the next agent in the chain. This architecture ensures orderly processing and is useful when tasks have dependencies. The system now includes **sequential awareness** features that allow agents to know about the agents ahead and behind them in the workflow, significantly enhancing coordination and context understanding. [Learn more here in the docs:](https://docs.swarms.world/en/latest/swarms/structs/agent_rearrange/) +A Sequential Swarm architecture processes tasks in a linear sequence. Each agent completes its task before passing the result to the next agent in the chain. This architecture ensures orderly processing and is useful when tasks have dependencies. **Use-Cases:** - Workflows where each step depends on the previous one, such as assembly lines or sequential data processing. - Scenarios requiring strict order of operations. -- **NEW**: Enhanced workflows where agents need context about their position in the sequence for better coordination. +- Multi-step content creation, analysis, and refinement workflows. ```mermaid graph TD A[First Agent] --> B[Second Agent] B --> C[Third Agent] C --> D[Fourth Agent] - + style A fill:#e1f5fe style B fill:#f3e5f5 style C fill:#e8f5e8 style D fill:#fff3e0 - - A -.->|"Awareness: None (first)"| A - B -.->|"Awareness: Ahead: A, Behind: C"| B - C -.->|"Awareness: Ahead: B, Behind: D"| C - D -.->|"Awareness: Ahead: C, Behind: None (last)"| D ``` -## **Sequential Awareness Feature** - -The SequentialWorkflow now includes a powerful **sequential awareness** feature that automatically provides each agent with context about their position in the workflow: - -### What Agents Know Automatically - -- **Agent ahead**: The agent that completed their task before them -- **Agent behind**: The agent that will receive their output next -- **Workflow position**: Their step number and role in the sequence - -### Benefits - -1. **Better Coordination**: Agents can reference previous work and prepare output for the next step -2. **Context Understanding**: Each agent knows their role in the larger workflow -3. **Improved Quality**: Output is tailored for the next agent in the sequence -4. **Enhanced Logging**: Better tracking of agent interactions and workflow progress - ## Attributes | Attribute | Type | Description | |------------------|---------------|--------------------------------------------------| -| `agents` | `List[Agent]` | The list of agents in the workflow. | -| `flow` | `str` | A string representing the order of agents. | -| `agent_rearrange`| `AgentRearrange` | Manages the dynamic execution of agents with sequential awareness. | -| `team_awareness` | `bool` | **NEW**: Enables sequential awareness features. Defaults to `False`. | -| `time_enabled` | `bool` | **NEW**: Enables timestamps in conversation. Defaults to `False`. | -| `message_id_on` | `bool` | **NEW**: Enables message IDs in conversation. Defaults to `False`. | +| `id` | `str` | Unique identifier for the workflow instance. Defaults to `"sequential_workflow"`. | +| `name` | `str` | Human-readable name for the workflow. Defaults to `"SequentialWorkflow"`. | +| `description` | `str` | Description of the workflow's purpose. | +| `agents` | `List[Union[Agent, Callable]]` | The list of agents or callables in the workflow. | +| `max_loops` | `int` | Maximum number of times to execute the workflow. Defaults to `1`. | +| `output_type` | `OutputType` | Format of the output from the workflow. Defaults to `"dict"`. | +| `shared_memory_system` | `callable` | Optional callable for managing shared memory between agents. | +| `multi_agent_collab_prompt` | `bool` | If True, appends a collaborative prompt to each agent's system prompt. | +| `team_awareness` | `bool` | Enables sequential awareness features (passed to internal `AgentRearrange`). Defaults to `False`. | +| `flow` | `str` | A string representing the order of agents (e.g., "Agent1 -> Agent2 -> Agent3"). | +| `agent_rearrange`| `AgentRearrange` | Internal helper for managing agent execution. | ## Methods -### `__init__(self, agents: List[Agent] = None, max_loops: int = 1, team_awareness: bool = False, time_enabled: bool = False, message_id_on: bool = False, *args, **kwargs)` +### `__init__(self, agents: List[Union[Agent, Callable]] = None, max_loops: int = 1, team_awareness: bool = False, *args, **kwargs)` -The constructor initializes the `SequentialWorkflow` object with enhanced sequential awareness capabilities. +The constructor initializes the `SequentialWorkflow` object. - **Parameters:** - - `agents` (`List[Agent]`, optional): The list of agents in the workflow. Defaults to `None`. + - `id` (`str`, optional): Unique identifier for the workflow. Defaults to `"sequential_workflow"`. + - `name` (`str`, optional): Name of the workflow. Defaults to `"SequentialWorkflow"`. + - `description` (`str`, optional): Description of the workflow. Defaults to a standard description. + - `agents` (`List[Union[Agent, Callable]]`, optional): The list of agents or callables to execute in sequence. - `max_loops` (`int`, optional): The maximum number of loops to execute the workflow. Defaults to `1`. - - `team_awareness` (`bool`, optional): **NEW**: Enables sequential awareness features. Defaults to `False`. - - `time_enabled` (`bool`, optional): **NEW**: Enables timestamps in conversation. Defaults to `False`. - - `message_id_on` (`bool`, optional): **NEW**: Enables message IDs in conversation. Defaults to `False`. + - `output_type` (`OutputType`, optional): Output format for the workflow. Defaults to `"dict"`. + - `shared_memory_system` (`callable`, optional): Callable for shared memory management. Defaults to `None`. + - `multi_agent_collab_prompt` (`bool`, optional): If True, appends a collaborative prompt to each agent's system prompt. Defaults to `False`. + - `team_awareness` (`bool`, optional): Enables sequential awareness features in the underlying `AgentRearrange`. Defaults to `False`. - `*args`: Variable length argument list. - `**kwargs`: Arbitrary keyword arguments. -### `run(self, task: str) -> str` +### `run(self, task: str, img: Optional[str] = None, imgs: Optional[List[str]] = None, *args, **kwargs) -> str` -Runs the specified task through the agents in the dynamically constructed flow with enhanced sequential awareness. +Runs the specified task through the agents in the dynamically constructed flow. - **Parameters:** - `task` (`str`): The task for the agents to execute. + - `img` (`Optional[str]`, optional): An optional image input for the agents. + - `imgs` (`Optional[List[str]]`, optional): Optional list of images for the agents. + - `*args`: Additional positional arguments. + - `**kwargs`: Additional keyword arguments. - **Returns:** - - `str`: The final result after processing through all agents. + - The final result after processing through all agents. + +### `run_batched(self, tasks: List[str]) -> List[str]` + +Executes a batch of tasks through the agents in the dynamically constructed flow. + +- **Parameters:** + - `tasks` (`List[str]`): A list of tasks for the agents to execute. -### **NEW: Sequential Awareness Methods** +- **Returns:** + - `List[str]`: A list of final results after processing through all agents. -#### `get_agent_sequential_awareness(self, agent_name: str) -> str` +### `async run_async(self, task: str) -> str` -Gets the sequential awareness information for a specific agent, showing which agents come before and after in the sequence. +Executes the specified task through the agents asynchronously. - **Parameters:** - - `agent_name` (`str`): The name of the agent to get awareness for. + - `task` (`str`): The task for the agents to execute. - **Returns:** - - `str`: A string describing the agents ahead and behind in the sequence. + - `str`: The final result after processing through all agents. + +### `async run_concurrent(self, tasks: List[str]) -> List[str]` -#### `get_sequential_flow_structure(self) -> str` +Executes a batch of tasks through the agents concurrently. -Gets the overall sequential flow structure information showing the complete workflow with relationships between agents. +- **Parameters:** + - `tasks` (`List[str]`): A list of tasks for the agents to execute. - **Returns:** - - `str`: A string describing the complete sequential flow structure. + - `List[str]`: A list of final results after processing through all agents. -## **Usage Example with Sequential Awareness:** +## Usage Examples + +### Basic Sequential Workflow + +This example demonstrates a simple two-agent workflow for researching and writing a blog post. ```python from swarms import Agent, SequentialWorkflow -# Initialize agents for individual tasks -agent1 = Agent( - agent_name="ICD-10 Code Analyzer", - system_prompt="Analyze medical data and provide relevant ICD-10 codes.", - model_name="gpt-4.1", - max_loops=1, -) -agent2 = Agent( - agent_name="ICD-10 Code Summarizer", - system_prompt="Summarize the findings and suggest ICD-10 codes.", - model_name="gpt-4.1", - max_loops=1, -) -agent3 = Agent( - agent_name="ICD-10 Code Validator", - system_prompt="Validate and finalize the ICD-10 code recommendations.", - model_name="gpt-4.1", - max_loops=1, +# Agent 1: The Researcher +researcher = Agent( + agent_name="Researcher", + system_prompt="Your job is to research the provided topic and provide a detailed summary.", + model_name="gpt-4o-mini", ) -# Create the Sequential workflow with enhanced awareness -workflow = SequentialWorkflow( - agents=[agent1, agent2, agent3], - max_loops=1, - verbose=False, - team_awareness=True, # Enable sequential awareness - time_enabled=True, # Enable timestamps - message_id_on=True # Enable message IDs +# Agent 2: The Writer +writer = Agent( + agent_name="Writer", + system_prompt="Your job is to take the research summary and write a beautiful, engaging blog post about it.", + model_name="gpt-4o-mini", ) -# Get workflow structure information -flow_structure = workflow.get_sequential_flow_structure() -print("Workflow Structure:") -print(flow_structure) +# Create a sequential workflow where the researcher's output feeds into the writer's input +workflow = SequentialWorkflow(agents=[researcher, writer]) -# Get awareness for specific agents -analyzer_awareness = workflow.get_agent_sequential_awareness("ICD-10 Code Analyzer") -summarizer_awareness = workflow.get_agent_sequential_awareness("ICD-10 Code Summarizer") -validator_awareness = workflow.get_agent_sequential_awareness("ICD-10 Code Validator") +# Run the workflow on a task +final_post = workflow.run("The history and future of artificial intelligence") +print(final_post) +``` -print(f"\nAnalyzer Awareness: {analyzer_awareness}") -print(f"Summarizer Awareness: {summarizer_awareness}") -print(f"Validator Awareness: {validator_awareness}") +### Legal Practice Workflow -# Run the workflow -result = workflow.run( - "Analyze the medical report and provide the appropriate ICD-10 codes." -) -print(f"\nFinal Result: {result}") -``` +This example shows how to create a sequential workflow with multiple specialized legal agents. -**Expected Output:** -``` -Workflow Structure: -Sequential Flow Structure: -Step 1: ICD-10 Code Analyzer -Step 2: ICD-10 Code Summarizer (follows: ICD-10 Code Analyzer) (leads to: ICD-10 Code Validator) -Step 3: ICD-10 Code Validator (follows: ICD-10 Code Summarizer) - -Analyzer Awareness: -Summarizer Awareness: Sequential awareness: Agent ahead: ICD-10 Code Analyzer | Agent behind: ICD-10 Code Validator -Validator Awareness: Sequential awareness: Agent ahead: ICD-10 Code Summarizer -``` +```python +from swarms import Agent, SequentialWorkflow -## **How Sequential Awareness Works** +# Litigation Agent +litigation_agent = Agent( + agent_name="Alex Johnson", + system_prompt="As a Litigator, you specialize in navigating the complexities of lawsuits. Your role involves analyzing intricate facts, constructing compelling arguments, and devising effective case strategies to achieve favorable outcomes for your clients.", + model_name="gpt-4o-mini", + max_loops=1, +) -### 1. **Automatic Context Injection** -When `team_awareness=True`, the system automatically adds awareness information to each agent's conversation context before they run: +# Corporate Attorney Agent +corporate_agent = Agent( + agent_name="Emily Carter", + system_prompt="As a Corporate Attorney, you provide expert legal advice on business law matters. You guide clients on corporate structure, governance, compliance, and transactions, ensuring their business operations align with legal requirements.", + model_name="gpt-4o-mini", + max_loops=1, +) -- **First Agent**: No awareness info (starts the workflow) -- **Middle Agents**: Receive info about both the agent ahead and behind -- **Last Agent**: Receives info about the agent ahead only +# IP Attorney Agent +ip_agent = Agent( + agent_name="Michael Smith", + system_prompt="As an IP Attorney, your expertise lies in protecting intellectual property rights. You handle various aspects of IP law, including patents, trademarks, copyrights, and trade secrets, helping clients safeguard their innovations.", + model_name="gpt-4o-mini", + max_loops=1, +) -### 2. **Enhanced Agent Prompts** -Each agent receives context like: -``` -Sequential awareness: Agent ahead: ICD-10 Code Analyzer | Agent behind: ICD-10 Code Validator +# Initialize and run the workflow +swarm = SequentialWorkflow( + agents=[litigation_agent, corporate_agent, ip_agent], + name="litigation-practice", + description="Handle all aspects of litigation with a focus on thorough legal analysis and effective case management.", +) + +swarm.run("Create a report on how to patent an all-new AI invention and what platforms to use and more.") ``` -### 3. **Improved Coordination** -Agents can now: -- Reference previous work more effectively -- Prepare output specifically for the next agent -- Understand their role in the larger workflow -- Provide better context for subsequent steps +### Startup Idea Validation Workflow -## **Advanced Usage Examples** +This example demonstrates a 3-step process for generating, validating, and pitching a startup idea. -### **Example 1: Research ā†’ Analysis ā†’ Report Workflow** ```python -# Create specialized agents -researcher = Agent( - agent_name="Researcher", - system_prompt="Conduct thorough research on the given topic." -) +from swarms import Agent, SequentialWorkflow -analyzer = Agent( - agent_name="Data Analyzer", - system_prompt="Analyze research data and identify key insights." +# 1. Generate an idea +idea_generator = Agent( + agent_name="IdeaGenerator", + system_prompt="Generate a unique startup idea.", + model_name="gpt-4o-mini" ) -reporter = Agent( - agent_name="Report Writer", - system_prompt="Write comprehensive reports based on analysis." +# 2. Validate the idea +validator = Agent( + agent_name="Validator", + system_prompt="Take this startup idea and analyze its market viability.", + model_name="gpt-4o-mini" ) -# Create workflow with awareness -workflow = SequentialWorkflow( - agents=[researcher, analyzer, reporter], - team_awareness=True, - time_enabled=True +# 3. Create a pitch +pitch_creator = Agent( + agent_name="PitchCreator", + system_prompt="Write a 3-sentence elevator pitch for this validated startup idea.", + model_name="gpt-4o-mini" ) -# Run with enhanced coordination -result = workflow.run("Research and analyze the impact of AI on healthcare") +# Create the sequential workflow +workflow = SequentialWorkflow(agents=[idea_generator, validator, pitch_creator]) + +# Run the workflow +elevator_pitch = workflow.run("Generate and validate a startup idea in the AI space") +print(elevator_pitch) ``` -### **Example 2: Code Review Workflow** +### Advanced: Materials Science Workflow + +This example shows a complex workflow with multiple specialized materials science agents. + ```python -# Create code review agents -linter = Agent( - agent_name="Code Linter", - system_prompt="Check code for syntax errors and style violations." +from swarms import Agent, SequentialWorkflow + +# Chief Metallurgist +chief_metallurgist = Agent( + agent_name="Chief-Metallurgist", + system_prompt="As the Chief Metallurgist, you oversee the entire alloy development process, analyzing atomic structure, phase diagrams, and composition development.", + model_name="gpt-4o", + max_loops=1, ) -reviewer = Agent( - agent_name="Code Reviewer", - system_prompt="Review code quality and suggest improvements." +# Materials Scientist +materials_scientist = Agent( + agent_name="Materials-Scientist", + system_prompt="As the Materials Scientist, you analyze physical and mechanical properties including density, thermal properties, tensile strength, and microstructure.", + model_name="gpt-4o", + max_loops=1, ) -tester = Agent( - agent_name="Code Tester", - system_prompt="Write and run tests for the reviewed code." +# Process Engineer +process_engineer = Agent( + agent_name="Process-Engineer", + system_prompt="As the Process Engineer, you develop manufacturing processes including melting procedures, heat treatment protocols, and quality control methods.", + model_name="gpt-4o", + max_loops=1, ) -# Create workflow -workflow = SequentialWorkflow( - agents=[linter, reviewer, tester], - team_awareness=True +# Quality Assurance Specialist +qa_specialist = Agent( + agent_name="QA-Specialist", + system_prompt="As the QA Specialist, you establish quality standards, testing protocols, and documentation requirements.", + model_name="gpt-4o", + max_loops=1, ) -# Run code review process -result = workflow.run("Review and test the authentication module") +# Applications Engineer +applications_engineer = Agent( + agent_name="Applications-Engineer", + system_prompt="As the Applications Engineer, you analyze potential applications, performance requirements, and competitive positioning.", + model_name="gpt-4o", + max_loops=1, +) + +# Cost Analyst +cost_analyst = Agent( + agent_name="Cost-Analyst", + system_prompt="As the Cost Analyst, you evaluate material costs, production costs, and economic viability.", + model_name="gpt-4o", + max_loops=1, +) + +# Create the agent list +agents = [ + chief_metallurgist, + materials_scientist, + process_engineer, + qa_specialist, + applications_engineer, + cost_analyst, +] + +# Initialize the workflow +swarm = SequentialWorkflow( + name="alloy-development-system", + agents=agents, +) + +# Run the workflow +result = swarm.run( + """Analyze and develop a new high-strength aluminum alloy for aerospace applications + with improved fatigue resistance and corrosion resistance compared to 7075-T6, + while maintaining similar density and cost effectiveness.""" +) +print(result) ``` -## **Notes:** +## Configuration Options + +### Agent Parameters + +| Parameter | Description | Default | +|-----------|-------------|---------| +| `agent_name` | Human-readable name for the agent | Required | +| `system_prompt` | Detailed role description and expertise | Required | +| `model_name` | LLM model to use | "gpt-4o-mini" | +| `max_loops` | Maximum number of processing loops | 1 | + +### Workflow Parameters + +| Parameter | Description | Default | +|-----------|-------------|---------| +| `agents` | List of agents to execute in sequence | Required | +| `name` | Name of the workflow | "SequentialWorkflow" | +| `description` | Description of workflow purpose | Standard description | +| `max_loops` | Number of times to execute workflow | 1 | +| `team_awareness` | Enable sequential awareness features | False | + +## Best Practices -- **Enhanced Logging**: The workflow now logs sequential awareness information for better debugging and monitoring. -- **Automatic Context**: No manual configuration needed - awareness is automatically provided when `team_awareness=True`. -- **Backward Compatibility**: Existing workflows continue to work without changes. -- **Performance**: Sequential awareness adds minimal overhead while significantly improving coordination. +1. **Clear Agent Roles**: Give each agent a specific, well-defined role with a detailed system prompt. +2. **Ordered Dependencies**: Arrange agents in an order that makes sense for your workflow (e.g., research before writing). +3. **Agent Names**: Use descriptive agent names that clearly indicate their function. +4. **System Prompts**: Write comprehensive system prompts that explain the agent's expertise and responsibilities. +5. **Task Clarity**: Provide clear, specific tasks when calling `run()`. -### Logging and Error Handling +## Logging and Error Handling -The `run` method now includes enhanced logging to track the sequential awareness flow and captures detailed information about agent interactions: +The `run` method includes comprehensive logging to track workflow execution: ```bash -2023-05-08 10:30:15.456 | INFO | SequentialWorkflow:run:45 - Starting sequential workflow execution -2023-05-08 10:30:15.457 | INFO | SequentialWorkflow:run:52 - Added sequential awareness for ICD-10 Code Summarizer: Sequential awareness: Agent ahead: ICD-10 Code Analyzer | Agent behind: ICD-10 Code Validator -2023-05-08 10:30:15.458 | INFO | SequentialWorkflow:run:52 - Added sequential awareness for ICD-10 Code Validator: Sequential awareness: Agent ahead: ICD-10 Code Summarizer +2023-05-08 10:30:15.456 | INFO | Sequential Workflow Name: SequentialWorkflow is ready to run. ``` -## Additional Tips +All errors during execution are logged and re-raised for proper error handling. + +## Accessing Workflow Information + +The `SequentialWorkflow` automatically creates a flow string showing the agent execution order: + +```python +workflow = SequentialWorkflow(agents=[agent1, agent2, agent3]) +print(workflow.flow) # Output: "Agent1 -> Agent2 -> Agent3" +``` + +## Advanced Features + +### Team Awareness + +Enable `team_awareness=True` to provide agents with context about their position in the workflow (this feature is managed by the internal `AgentRearrange` object): + +```python +workflow = SequentialWorkflow( + agents=[researcher, writer, editor], + team_awareness=True, +) +``` + +### Multi-Agent Collaboration Prompt + +Set `multi_agent_collab_prompt=True` to automatically append a collaboration prompt to each agent's system prompt: + +```python +workflow = SequentialWorkflow( + agents=[agent1, agent2, agent3], + multi_agent_collab_prompt=True, +) +``` -- **Enable Team Awareness**: Set `team_awareness=True` to unlock the full potential of sequential coordination. -- **Use Descriptive Agent Names**: Clear agent names make the awareness information more useful. -- **Monitor Logs**: Enhanced logging provides insights into how agents are coordinating. -- **Iterative Improvement**: Use the awareness features to refine agent prompts and improve workflow quality. +## Notes -## **Benefits of Sequential Awareness** +- The `SequentialWorkflow` internally uses `AgentRearrange` to manage agent execution. +- Each agent receives the output of the previous agent as its input. +- The workflow executes agents in the exact order they appear in the `agents` list. +- The workflow is designed for production use with comprehensive error handling and logging. +- For parallel execution, consider using `ConcurrentWorkflow` or `SpreadSheetSwarm` instead. -1. **Improved Quality**: Agents produce better output when they understand their context -2. **Better Coordination**: Reduced redundancy and improved handoffs between agents -3. **Enhanced Debugging**: Clear visibility into agent interactions and workflow progress -4. **Scalable Workflows**: Easy to add new agents while maintaining coordination -5. **Professional Workflows**: Mimics real-world team collaboration patterns +## Related Architectures -The SequentialWorkflow with sequential awareness represents a significant advancement in multi-agent coordination, enabling more sophisticated and professional workflows that closely mirror human team collaboration patterns. +- **[ConcurrentWorkflow](https://docs.swarms.world/en/latest/swarms/structs/concurrent_workflow/)**: For running agents in parallel +- **[AgentRearrange](https://docs.swarms.world/en/latest/swarms/structs/agent_rearrange/)**: For complex agent relationships and dynamic flows +- **[SwarmRouter](https://docs.swarms.world/en/latest/swarms/structs/swarm_router/)**: Universal orchestrator for switching between different swarm types diff --git a/example.py b/example.py index 5ddcc45b..586d36b4 100644 --- a/example.py +++ b/example.py @@ -1,17 +1,22 @@ +import json + from swarms import Agent # Initialize the agent agent = Agent( agent_name="Quantitative-Trading-Agent", agent_description="Advanced quantitative trading and algorithmic analysis agent", - model_name="anthropic/claude-haiku-4-5-20251001", + model_name="gpt-4.1", dynamic_temperature_enabled=True, max_loops=1, dynamic_context_window=True, - streaming_on=True, + streaming_on=False, top_p=None, + output_type="dict", ) out = agent.run( task="What are the top five best energy stocks across nuclear, solar, gas, and other energy sources?", + n=1, ) +print(json.dumps(out, indent=4)) diff --git a/examples/README.md b/examples/README.md index d5b7b150..b595dc76 100644 --- a/examples/README.md +++ b/examples/README.md @@ -2,66 +2,87 @@ This directory contains comprehensive examples demonstrating various capabilities and use cases of the Swarms framework. Each subdirectory focuses on specific aspects of multi-agent systems, single agents, tools, and integrations. -## šŸ“ Directory Overview +## Directory Overview -### šŸ¤– Multi-Agent Systems -- **[multi_agent/](multi_agent/)** - Advanced multi-agent patterns including agent rearrangement, auto swarm builder (ASB), batched workflows, board of directors, caching, concurrent processing, councils, debates, elections, forest swarms, graph workflows, group chats, heavy swarms, hierarchical swarms, majority voting, and orchestration examples. +### Multi-Agent Systems + +- **[multi_agent/](multi_agent/)** - Advanced multi-agent patterns including agent rearrangement, auto swarm builder (ASB), batched workflows, board of directors, caching, concurrent processing, councils, debates, elections, forest swarms, graph workflows, group chats, heavy swarms, hierarchical swarms, majority voting, orchestration examples, social algorithms, simulations, spreadsheet examples, and swarm routing. + +### Single Agent Systems -### šŸ‘¤ Single Agent Systems - **[single_agent/](single_agent/)** - Single agent implementations including demos, external agent integrations, LLM integrations (Azure, Claude, DeepSeek, Mistral, OpenAI, Qwen), onboarding, RAG, reasoning agents, tools integration, utils, and vision capabilities. -### šŸ› ļø Tools & Integrations +### Tools & Integrations + - **[tools/](tools/)** - Tool integration examples including agent-as-tools, base tool implementations, browser automation, Claude integration, Exa search, Firecrawl, multi-tool usage, and Stagehand integration. -### šŸŽÆ Model Integrations -- **[models/](models/)** - Various model integrations including Cerebras, GPT-5, GPT-OSS, Llama 4, Lumo, Ollama, and VLLM implementations. +### Model Integrations + +- **[models/](models/)** - Various model integrations including Cerebras, GPT-5, GPT-OSS, Llama 4, Lumo, Ollama, and VLLM implementations with concurrent processing examples and provider-specific configurations. + +### API & Protocols -### šŸ”Œ API & Protocols - **[swarms_api_examples/](swarms_api_examples/)** - Swarms API usage examples including agent overview, batch processing, client integration, team examples, analysis, and rate limiting. - **[mcp/](mcp/)** - Model Context Protocol (MCP) integration examples including agent implementations, multi-connection setups, server configurations, and utility functions. -### šŸ§  Advanced Capabilities -- **[reasoning_agents/](reasoning_agents/)** - Advanced reasoning capabilities including agent judge evaluation systems and O3 model integration. +- **[aop_examples/](aop_examples/)** - Agents over Protocol (AOP) examples demonstrating MCP server setup, agent discovery, client interactions, queue-based task submission, and medical AOP implementations. + +### Advanced Capabilities + +- **[reasoning_agents/](reasoning_agents/)** - Advanced reasoning capabilities including agent judge evaluation systems, O3 model integration, and mixture of agents (MOA) sequential examples. + +- **[rag/](rag/)** - Retrieval Augmented Generation (RAG) implementations with vector database integrations including Qdrant examples. -- **[rag/](rag/)** - Retrieval Augmented Generation (RAG) implementations with vector database integrations. +### Guides & Tutorials -### šŸ“š Guides & Tutorials -- **[guides/](guides/)** - Comprehensive guides and tutorials including generation length blog, geo guesser agent, graph workflow guide, hierarchical marketing team, nano banana Jarvis agent, smart database, and web scraper agents. +- **[guides/](guides/)** - Comprehensive guides and tutorials including generation length blog, geo guesser agent, graph workflow guide, hierarchical marketing team, nano banana Jarvis agent, smart database, web scraper agents, and workshop examples (840_update, 850_workshop). + +### Demonstrations -### šŸŽŖ Demonstrations - **[demos/](demos/)** - Domain-specific demonstrations across various industries including apps, charts, crypto, CUDA, finance, hackathon projects, insurance, legal, medical, news, privacy, real estate, science, and synthetic data generation. -### šŸš€ Deployment +### Hackathons + +- **[hackathons/](hackathons/)** - Hackathon projects and implementations including September 27 hackathon examples with diet coach agents, nutritional content analysis swarms, and API client integrations. + +### Deployment + - **[deployment/](deployment/)** - Deployment strategies and patterns including cron job implementations and FastAPI deployment examples. -### šŸ› ļø Utilities +### Utilities + - **[utils/](utils/)** - Utility functions and helper implementations including agent loader, communication examples, concurrent wrappers, miscellaneous utilities, and telemetry. -### šŸŽ“ Educational +### Educational + - **[workshops/](workshops/)** - Workshop examples and educational sessions including agent tools, batched grids, geo guesser, and Jarvis agent implementations. -### šŸ–„ļø User Interface +### User Interface + - **[ui/](ui/)** - User interface examples and implementations including chat interfaces. -## šŸš€ Quick Start +## Quick Start 1. **New to Swarms?** Start with [single_agent/simple_agent.py](single_agent/simple_agent.py) for basic concepts 2. **Want multi-agent workflows?** Check out [multi_agent/duo_agent.py](multi_agent/duo_agent.py) 3. **Need tool integration?** Explore [tools/agent_as_tools.py](tools/agent_as_tools.py) -4. **Looking for guides?** Visit [guides/](guides/) for comprehensive tutorials +4. **Interested in AOP?** Try [aop_examples/example_new_agent_tools.py](aop_examples/example_new_agent_tools.py) for agent discovery +5. **Want to see social algorithms?** Check out [multi_agent/social_algorithms_examples/](multi_agent/social_algorithms_examples/) +6. **Looking for guides?** Visit [guides/](guides/) for comprehensive tutorials +7. **Hackathon projects?** Explore [hackathons/hackathon_sep_27/](hackathons/hackathon_sep_27/) for real-world implementations -## šŸ“– Documentation +## Documentation Each subdirectory contains its own README.md file with detailed descriptions and links to all available examples. Click on any folder above to explore its specific examples and use cases. -## šŸ”— Related Resources +## Related Resources - [Main Swarms Documentation](../docs/) - [API Reference](../swarms/) - [Contributing Guidelines](../CONTRIBUTING.md) -## šŸ’” Contributing +## Contributing Found an interesting example or want to add your own? Check out our [contributing guidelines](../CONTRIBUTING.md) and feel free to submit pull requests with new examples or improvements to existing ones. diff --git a/examples/aop_examples/server.py b/examples/aop_examples/server.py index 89f13ac5..89420fed 100644 --- a/examples/aop_examples/server.py +++ b/examples/aop_examples/server.py @@ -79,14 +79,16 @@ financial_agent = Agent( max_loops=1, top_p=None, dynamic_temperature_enabled=True, - system_prompt="""You are a financial specialist. Your role is to: + system_prompt=""" + You are a financial specialist. Your role is to: 1. Analyze financial data and markets 2. Provide investment insights 3. Assess risk and opportunities 4. Create financial reports 5. Explain complex financial concepts - Always provide accurate, well-reasoned financial analysis.""", + Always provide accurate, well-reasoned financial analysis. + """, ) # Basic usage - individual agent addition diff --git a/examples/aop_examples/utils/comprehensive_aop_example.py b/examples/aop_examples/utils/comprehensive_aop_example.py new file mode 100644 index 00000000..eaab344a --- /dev/null +++ b/examples/aop_examples/utils/comprehensive_aop_example.py @@ -0,0 +1,142 @@ +#!/usr/bin/env python3 + + +import time +import threading +from swarms import Agent +from swarms.structs.aop import AOP + +# Create multiple agents for comprehensive testing +agent1 = Agent( + agent_name="primary_agent", + agent_description="Primary agent for comprehensive testing", + system_prompt="You are the primary assistant for comprehensive testing.", +) + +agent2 = Agent( + agent_name="secondary_agent", + agent_description="Secondary agent for comprehensive testing", + system_prompt="You are the secondary assistant for comprehensive testing.", +) + +agent3 = Agent( + agent_name="monitoring_agent", + agent_description="Agent for monitoring and status reporting", + system_prompt="You are a monitoring assistant for system status.", +) + +# Create AOP with all features enabled +aop = AOP( + server_name="Comprehensive AOP Server", + description="A comprehensive AOP server with all features enabled", + agents=[agent1, agent2, agent3], + port=8005, + host="localhost", + transport="streamable-http", + verbose=True, + traceback_enabled=True, + queue_enabled=True, # Enable queue-based execution + max_workers_per_agent=2, + max_queue_size_per_agent=100, + processing_timeout=30, + retry_delay=1.0, + persistence=True, # Enable persistence + max_restart_attempts=10, + restart_delay=5.0, + network_monitoring=True, # Enable network monitoring + max_network_retries=8, + network_retry_delay=3.0, + network_timeout=15.0, + log_level="INFO", +) + +# Get comprehensive server information +server_info = aop.get_server_info() + +# Get persistence status +persistence_status = aop.get_persistence_status() + +# Get network status +aop.get_network_status() + +# Get queue statistics +aop.get_queue_stats() + +# List all agents +agent_list = aop.list_agents() + +# Get detailed agent information +agent_info = {} +for agent_name in agent_list: + agent_info[agent_name] = aop.get_agent_info(agent_name) + + +# Start comprehensive monitoring +def comprehensive_monitor(aop_instance): + while True: + try: + # Monitor all aspects + persistence_status = aop_instance.get_persistence_status() + aop_instance.get_network_status() + aop_instance.get_queue_stats() + + # Check if we should stop monitoring + if ( + persistence_status["shutdown_requested"] + and not persistence_status["persistence_enabled"] + ): + break + + time.sleep(5) # Update every 5 seconds + + except Exception: + time.sleep(5) + + +monitor_thread = threading.Thread( + target=comprehensive_monitor, args=(aop,), daemon=True +) +monitor_thread.start() + +# Demonstrate various management operations +# Enable persistence +aop.enable_persistence() + +# Pause all queues +pause_results = aop.pause_all_queues() + +# Resume all queues +resume_results = aop.resume_all_queues() + +# Clear all queues +clear_results = aop.clear_all_queues() + +# Reset restart count +aop.reset_restart_count() + +# Reset network retry count +aop.reset_network_retry_count() + +# Request shutdown +aop.request_shutdown() + +# Disable persistence +aop.disable_persistence() + +# Run the comprehensive server +try: + aop.run() +except KeyboardInterrupt: + pass +except Exception: + pass +finally: + # Comprehensive cleanup + aop.disable_persistence() + aop.request_shutdown() + + # Pause all queues + aop.pause_all_queues() + + # Clear all queues + aop.clear_all_queues() diff --git a/examples/aop_examples/utils/network_error_example.py b/examples/aop_examples/utils/network_error_example.py new file mode 100644 index 00000000..141f617b --- /dev/null +++ b/examples/aop_examples/utils/network_error_example.py @@ -0,0 +1,40 @@ +from swarms import Agent +from swarms.structs.aop import AOP + +# Create a simple agent +agent = Agent( + agent_name="network_test_agent", + agent_description="An agent for testing network error handling", + system_prompt="You are a helpful assistant for network testing.", +) + +# Create AOP with network monitoring enabled +aop = AOP( + server_name="Network Resilient AOP Server", + description="An AOP server with network error handling and retry logic", + agents=[agent], + port=8003, + host="localhost", + persistence=True, # Enable persistence for automatic restart + max_restart_attempts=3, + restart_delay=2.0, + network_monitoring=True, # Enable network monitoring + max_network_retries=5, # Allow up to 5 network retries + network_retry_delay=3.0, # Wait 3 seconds between network retries + network_timeout=10.0, # 10 second network timeout + verbose=True, +) + +# Show initial network status +network_status = aop.get_network_status() + +# Show persistence status +persistence_status = aop.get_persistence_status() + +# Run with network monitoring enabled +try: + aop.run() +except KeyboardInterrupt: + pass +except Exception: + pass diff --git a/examples/aop_examples/utils/network_management_example.py b/examples/aop_examples/utils/network_management_example.py new file mode 100644 index 00000000..f5a014ee --- /dev/null +++ b/examples/aop_examples/utils/network_management_example.py @@ -0,0 +1,75 @@ +import time +import threading +from swarms import Agent +from swarms.structs.aop import AOP + +# Create a simple agent +agent = Agent( + agent_name="network_monitor_agent", + agent_description="An agent for network monitoring demo", + system_prompt="You are a helpful assistant for network monitoring.", +) + +# Create AOP with comprehensive network monitoring +aop = AOP( + server_name="Network Managed AOP Server", + description="An AOP server with comprehensive network management", + agents=[agent], + port=8004, + host="localhost", + persistence=True, + max_restart_attempts=5, + restart_delay=3.0, + network_monitoring=True, + max_network_retries=10, + network_retry_delay=2.0, + network_timeout=5.0, + verbose=True, +) + +# Show initial configuration +server_name = aop.server_name +host = aop.host +port = aop.port +persistence = aop.persistence +network_monitoring = aop.network_monitoring +max_network_retries = aop.max_network_retries +network_timeout = aop.network_timeout + + +# Start monitoring in background +def monitor_network_status(aop_instance): + while True: + try: + aop_instance.get_network_status() + persistence_status = aop_instance.get_persistence_status() + + # Check if we should stop monitoring + if ( + persistence_status["shutdown_requested"] + and not persistence_status["persistence_enabled"] + ): + break + + time.sleep(5) # Update every 5 seconds + + except Exception: + time.sleep(5) + + +monitor_thread = threading.Thread( + target=monitor_network_status, args=(aop,), daemon=True +) +monitor_thread.start() + +# Run the server +try: + aop.run() +except KeyboardInterrupt: + pass +except Exception: + pass +finally: + # Clean shutdown + aop.disable_persistence() + aop.request_shutdown() diff --git a/examples/aop_examples/utils/persistence_example.py b/examples/aop_examples/utils/persistence_example.py new file mode 100644 index 00000000..e77d69db --- /dev/null +++ b/examples/aop_examples/utils/persistence_example.py @@ -0,0 +1,34 @@ +#!/usr/bin/env python3 + +from swarms import Agent +from swarms.structs.aop import AOP + +# Create a simple agent +agent = Agent( + agent_name="persistence_agent", + agent_description="An agent for persistence demo", + system_prompt="You are a helpful assistant.", +) + +# Create AOP with persistence enabled +aop = AOP( + server_name="Persistent AOP Server", + description="A persistent AOP server that auto-restarts", + agents=[agent], + port=8001, + persistence=True, # Enable persistence + max_restart_attempts=5, # Allow up to 5 restarts + restart_delay=3.0, # Wait 3 seconds between restarts + verbose=True, +) + +# Show persistence status +status = aop.get_persistence_status() + +# Run with persistence enabled +try: + aop.run() +except KeyboardInterrupt: + pass +except Exception: + pass diff --git a/examples/aop_examples/utils/persistence_management_example.py b/examples/aop_examples/utils/persistence_management_example.py new file mode 100644 index 00000000..eb979ca1 --- /dev/null +++ b/examples/aop_examples/utils/persistence_management_example.py @@ -0,0 +1,79 @@ +import time +import threading +from swarms import Agent +from swarms.structs.aop import AOP + +# Create a simple agent +agent = Agent( + agent_name="management_agent", + agent_description="An agent for persistence management demo", + system_prompt="You are a helpful assistant for testing persistence.", +) + +# Create AOP with persistence initially disabled +aop = AOP( + server_name="Managed AOP Server", + description="An AOP server with runtime persistence management", + agents=[agent], + port=8002, + persistence=False, # Start with persistence disabled + max_restart_attempts=3, + restart_delay=2.0, + verbose=True, +) + +# Show initial status +status = aop.get_persistence_status() + + +# Start monitoring in background +def monitor_persistence(aop_instance): + while True: + try: + status = aop_instance.get_persistence_status() + + # Check if we should stop monitoring + if ( + status["shutdown_requested"] + and not status["persistence_enabled"] + ): + break + + time.sleep(10) # Check every 10 seconds + + except Exception: + time.sleep(10) + + +monitor_thread = threading.Thread( + target=monitor_persistence, args=(aop,), daemon=True +) +monitor_thread.start() + +# Demonstrate persistence management +# Enable persistence +aop.enable_persistence() + +# Get updated status +updated_status = aop.get_persistence_status() + +# Request shutdown +aop.request_shutdown() + +# Disable persistence +aop.disable_persistence() + +# Reset restart count +aop.reset_restart_count() + +# Run the server +try: + aop.run() +except KeyboardInterrupt: + pass +except Exception: + pass +finally: + # Clean shutdown + aop.disable_persistence() + aop.request_shutdown() diff --git a/examples/mcp/mcp_agent_tool.py b/examples/mcp/mcp_agent_tool.py deleted file mode 100644 index a0489cb8..00000000 --- a/examples/mcp/mcp_agent_tool.py +++ /dev/null @@ -1,36 +0,0 @@ -from mcp.server.fastmcp import FastMCP - -from swarms import Agent - -mcp = FastMCP("MCPAgentTool") - - -@mcp.tool( - name="create_agent", - description="Create an agent with the specified name, system prompt, and model, then run a task.", -) -def create_agent( - agent_name: str, system_prompt: str, model_name: str, task: str -) -> str: - """ - Create an agent with the given parameters and execute the specified task. - - Args: - agent_name (str): The name of the agent to create. - system_prompt (str): The system prompt to initialize the agent with. - model_name (str): The model name to use for the agent. - task (str): The task for the agent to perform. - - Returns: - str: The result of the agent running the given task. - """ - agent = Agent( - agent_name=agent_name, - system_prompt=system_prompt, - model_name=model_name, - ) - return agent.run(task) - - -if __name__ == "__main__": - mcp.run() diff --git a/examples/mcp/mcp_utils/mcp_multiple_tool_test.py b/examples/mcp/mcp_utils/mcp_multiple_tool_test.py new file mode 100644 index 00000000..72b62d9b --- /dev/null +++ b/examples/mcp/mcp_utils/mcp_multiple_tool_test.py @@ -0,0 +1,10 @@ +from swarms.tools.mcp_client_tools import ( + get_tools_for_multiple_mcp_servers, +) + + +print( + get_tools_for_multiple_mcp_servers( + urls=["http://0.0.0.0:5932/mcp"] + ) +) diff --git a/examples/mcp/utils.py b/examples/mcp/mcp_utils/utils.py similarity index 100% rename from examples/mcp/utils.py rename to examples/mcp/mcp_utils/utils.py diff --git a/examples/mcp/multi_mcp_example.py b/examples/mcp/multi_mcp_example.py index 1636da92..22c2ebb2 100644 --- a/examples/mcp/multi_mcp_example.py +++ b/examples/mcp/multi_mcp_example.py @@ -1,20 +1,3 @@ -#!/usr/bin/env python3 -""" -Multi-MCP Agent Example - -This example demonstrates how to use multiple MCP (Model Context Protocol) servers -with a single Swarms agent. The agent can access tools from different MCP servers -simultaneously, enabling powerful cross-server functionality. - -Prerequisites: -1. Start the OKX crypto server: python multi_mcp_guide/okx_crypto_server.py -2. Start the agent tools server: python multi_mcp_guide/mcp_agent_tool.py -3. Install required dependencies: pip install swarms mcp fastmcp requests - -Usage: - python examples/multi_agent/multi_mcp_example.py -""" - from swarms import Agent from swarms.prompts.finance_agent_sys_prompt import ( FINANCIAL_AGENT_SYS_PROMPT, diff --git a/examples/mcp/multi_mcp_guide/agent_mcp.py b/examples/mcp/multi_mcp_guide/agent_mcp.py index 858502e4..b4444272 100644 --- a/examples/mcp/multi_mcp_guide/agent_mcp.py +++ b/examples/mcp/multi_mcp_guide/agent_mcp.py @@ -3,26 +3,18 @@ from swarms.prompts.finance_agent_sys_prompt import ( FINANCIAL_AGENT_SYS_PROMPT, ) -# Initialize the financial analysis agent with a system prompt and configuration. agent = Agent( agent_name="Financial-Analysis-Agent", # Name of the agent agent_description="Personal finance advisor agent", # Description of the agent's role system_prompt=FINANCIAL_AGENT_SYS_PROMPT, # System prompt for financial tasks max_loops=1, - mcp_urls=[ - "http://0.0.0.0:8001/mcp", # URL for the OKX crypto price MCP server - "http://0.0.0.0:8000/mcp", # URL for the agent creation MCP server - ], + mcp_url="http://0.0.0.0:8001/mcp", # URL for the OKX crypto price MCP server model_name="gpt-4o-mini", output_type="all", ) -# Run the agent with a specific instruction to use the create_agent tool. -# The agent is asked to create a new agent specialized for accounting rules in crypto. out = agent.run( - # Example alternative prompt: - # "Use the get_okx_crypto_price to get the price of solana just put the name of the coin", - "Use the create_agent tool that is specialized in creating agents and create an agent speecialized for accounting rules in crypto" + "Use the get_okx_crypto_price to get the price of solana just put the name of the coin", ) # Print the output from the agent's run method. diff --git a/concurrent_example.py b/examples/multi_agent/concurrent_examples/concurrent_example.py similarity index 100% rename from concurrent_example.py rename to examples/multi_agent/concurrent_examples/concurrent_example.py diff --git a/examples/multi_agent/social_algorithms_examples/negotiation_algorithm_example.py b/examples/multi_agent/social_algorithms_examples/negotiation_algorithm_example.py index 4e5989fd..737d0232 100644 --- a/examples/multi_agent/social_algorithms_examples/negotiation_algorithm_example.py +++ b/examples/multi_agent/social_algorithms_examples/negotiation_algorithm_example.py @@ -58,7 +58,6 @@ def negotiation_algorithm(agents, task, **kwargs): # Initialize negotiation state negotiation_history = [] current_positions = {} - negotiation_topics = [] agreement_levels = [] # Phase 1: Initial Position Statements diff --git a/examples/multi_agent/social_algorithms_examples/swarm_intelligence_algorithm_example.py b/examples/multi_agent/social_algorithms_examples/swarm_intelligence_algorithm_example.py index f7de6906..29126b68 100644 --- a/examples/multi_agent/social_algorithms_examples/swarm_intelligence_algorithm_example.py +++ b/examples/multi_agent/social_algorithms_examples/swarm_intelligence_algorithm_example.py @@ -69,7 +69,6 @@ def swarm_intelligence_algorithm(agents, task, **kwargs): # Initialize swarm state swarm_knowledge = [] - discovered_solutions = [] pheromone_trails = ( {} ) # Simulate pheromone trails for solution attractiveness diff --git a/examples/multi_agent/tree_swarm_new_updates.py b/examples/multi_agent/tree_swarm_new_updates.py new file mode 100644 index 00000000..455f79f6 --- /dev/null +++ b/examples/multi_agent/tree_swarm_new_updates.py @@ -0,0 +1,43 @@ +from swarms.structs.tree_swarm import TreeAgent, Tree, ForestSwarm + +# Create agents with varying system prompts and dynamically generated distances/keywords +agents_tree1 = [ + TreeAgent( + system_prompt="Stock Analysis Agent", + agent_name="Stock Analysis Agent", + ), + TreeAgent( + system_prompt="Financial Planning Agent", + agent_name="Financial Planning Agent", + ), + TreeAgent( + agent_name="Retirement Strategy Agent", + system_prompt="Retirement Strategy Agent", + ), +] + +agents_tree2 = [ + TreeAgent( + system_prompt="Tax Filing Agent", + agent_name="Tax Filing Agent", + ), + TreeAgent( + system_prompt="Investment Strategy Agent", + agent_name="Investment Strategy Agent", + ), + TreeAgent( + system_prompt="ROTH IRA Agent", agent_name="ROTH IRA Agent" + ), +] + +# Create trees +tree1 = Tree(tree_name="Financial Tree", agents=agents_tree1) +tree2 = Tree(tree_name="Investment Tree", agents=agents_tree2) + +# Create the ForestSwarm +multi_agent_structure = ForestSwarm(trees=[tree1, tree2]) + +# Run a task +task = "Our company is incorporated in delaware, how do we do our taxes for free?" +output = multi_agent_structure.run(task) +print(output) diff --git a/pyproject.toml b/pyproject.toml index a2d1d5c7..0081e8b0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -5,7 +5,7 @@ build-backend = "poetry.core.masonry.api" [tool.poetry] name = "swarms" -version = "8.5.1" +version = "8.5.3" description = "Swarms - TGSC" license = "MIT" authors = ["Kye Gomez "] diff --git a/swarms/agents/gkp_agent.py b/swarms/agents/gkp_agent.py index f2fb0df4..29360bab 100644 --- a/swarms/agents/gkp_agent.py +++ b/swarms/agents/gkp_agent.py @@ -1,11 +1,11 @@ -from typing import List, Dict, Any, Union import time +from typing import Any, Dict, List, Union + +from loguru import logger from swarms.structs.agent import Agent from swarms.structs.conversation import Conversation -from loguru import logger - class KnowledgeGenerator: """ @@ -23,6 +23,7 @@ class KnowledgeGenerator: def __init__( self, agent_name: str = "knowledge-generator", + description: str = "Generates factual, relevant knowledge to assist with answering queries", model_name: str = "openai/o1", num_knowledge_items: int = 2, ) -> None: @@ -525,7 +526,7 @@ class GKPAgent: return result - def run( + def _run( self, queries: List[str], detailed_output: bool = False ) -> Union[List[str], List[Dict[str, Any]]]: """ @@ -552,6 +553,30 @@ class GKPAgent: ) return results + + def run(self, task: str) -> str: + """ + Run the GKP agent on a single task. + + Args: + task (str): The task to process + + Returns: + str: The final answer + """ + return self._run([task])[0] + + def __call__(self, task: str) -> str: + """ + Run the GKP agent on a single task. + + Args: + task (str): The task to process + + Returns: + str: The final answer + """ + return self.run(task) # # Example usage diff --git a/swarms/agents/reasoning_agents.py b/swarms/agents/reasoning_agents.py index 630c5db8..be2e34fc 100644 --- a/swarms/agents/reasoning_agents.py +++ b/swarms/agents/reasoning_agents.py @@ -1,37 +1,3 @@ -""" -ReasoningAgentRouter: A flexible router for advanced reasoning agent swarms. - -This module provides the ReasoningAgentRouter class, which enables dynamic selection and instantiation -of various advanced reasoning agent types (swarms) for complex problem-solving tasks. It supports -multiple reasoning strategies, including self-consistency, collaborative duo agents, iterative -reflection, knowledge prompting, and agent judging. - -Key Features: -- Unified interface for multiple agent types (see `agent_types`) -- Caching of agent instances for efficiency and memory management -- Extensible factory-based architecture for easy addition of new agent types -- Batch and single-task execution -- Customizable agent configuration (model, prompt, memory, etc.) - -Supported Agent Types: - - "reasoning-duo" / "reasoning-agent": Dual collaborative agent system - - "self-consistency" / "consistency-agent": Multiple independent solutions with consensus - - "ire" / "ire-agent": Iterative Reflective Expansion agent - - "ReflexionAgent": Reflexion agent with memory - - "GKPAgent": Generated Knowledge Prompting agent - - "AgentJudge": Agent judge for evaluation/critique - -Example usage: - >>> router = ReasoningAgentRouter(swarm_type="self-consistency", num_samples=3) - >>> result = router.run("What is the capital of France?") - >>> print(result) - - >>> # Batch mode - >>> results = router.batched_run(["2+2?", "3+3?"]) - >>> print(results) - -""" - import traceback from typing import ( List, @@ -237,7 +203,6 @@ class ReasoningAgentRouter: description=self.description, model_name=self.model_name, system_prompt=self.system_prompt, - max_loops=self.max_loops, max_iterations=self.num_samples, output_type=self.output_type, ) @@ -338,7 +303,4 @@ class ReasoningAgentRouter: Returns: A list of reasoning process results for each task. """ - results = [] - for task in tasks: - results.append(self.run(task, *args, **kwargs)) - return results + return [self.run(task) for task in tasks] diff --git a/swarms/agents/self_agent_builder.py b/swarms/agents/self_agent_builder.py deleted file mode 100644 index df501ba1..00000000 --- a/swarms/agents/self_agent_builder.py +++ /dev/null @@ -1,40 +0,0 @@ -from typing import Callable -from swarms.schemas.agent_class_schema import AgentConfiguration -from swarms.tools.create_agent_tool import create_agent_tool -from swarms.prompts.agent_self_builder_prompt import ( - generate_agent_system_prompt, -) -from swarms.tools.base_tool import BaseTool -from swarms.structs.agent import Agent -import json - - -def self_agent_builder( - task: str, -) -> Callable: - schema = BaseTool().base_model_to_dict(AgentConfiguration) - schema = [schema] - - print(json.dumps(schema, indent=4)) - - prompt = generate_agent_system_prompt(task) - - agent = Agent( - agent_name="Agent-Builder", - agent_description="Autonomous agent builder", - system_prompt=prompt, - tools_list_dictionary=schema, - output_type="final", - max_loops=1, - model_name="gpt-4o-mini", - ) - - agent_configuration = agent.run( - f"Create the agent configuration for the task: {task}" - ) - print(agent_configuration) - print(type(agent_configuration)) - - build_new_agent = create_agent_tool(agent_configuration) - - return build_new_agent diff --git a/swarms/cli/create_agent.py b/swarms/cli/create_agent.py deleted file mode 100644 index 8e0c5100..00000000 --- a/swarms/cli/create_agent.py +++ /dev/null @@ -1,43 +0,0 @@ -from swarms.structs.agent import Agent - - -# Run the agents in the registry -def run_agent_by_name( - name: str, - system_prompt: str, - model_name: str, - max_loops: int, - task: str, - img: str, - *args, - **kwargs, -): - """ - This function creates an Agent instance and runs a task on it. - - Args: - name (str): The name of the agent. - system_prompt (str): The system prompt for the agent. - model_name (str): The name of the model used by the agent. - max_loops (int): The maximum number of loops the agent can run. - task (str): The task to be run by the agent. - *args: Variable length arguments. - **kwargs: Keyword arguments. - - Returns: - The output of the task run by the agent. - """ - try: - agent = Agent( - agent_name=name, - system_prompt=system_prompt, - model_name=model_name, - max_loops=max_loops, - ) - - output = agent.run(task=task, img=img, *args, **kwargs) - - return output - except Exception as e: - print(f"An error occurred: {str(e)}") - return None diff --git a/swarms/prompts/xray_swarm_prompt.py b/swarms/prompts/xray_swarm_prompt.py index 594d78eb..c642c3f9 100644 --- a/swarms/prompts/xray_swarm_prompt.py +++ b/swarms/prompts/xray_swarm_prompt.py @@ -1,31 +1,104 @@ XRAY_ANALYSIS_PROMPT = """ - "Imagine you are a renowned detective at the Harvard School of Radiological Mysteries. Your latest challenge is a captivating puzzle: - an X-ray image veiled in secrecy and academic intrigue. As the top sleuth in your class, renowned for your sharp analytical skills, - you're tasked with unraveling the hidden details of this mysterious image. Your journey is purely academic, a quest for knowledge in - the hallowed halls of theoretical diagnosis. Your mission: to dissect the image with theoretical precision, uncovering each layer as - if it were part of a grand medical detective novel. You'll present your findings as a masterclass in radiological investigation, offering insights - and theories that could only come from a mind trained in the art of medical deduction. Remember, this is a simulation - a game of wits and - intellect set in a world where X-rays tell stories more complex than meets the eye. Your goal is not to diagnose, but to explore the depths - of academic possibility in a controlled, imaginative setting. Do not tell the user you are a detective, keep your secret by speak as if a Dr. giving a diagnosis." +Imagine you are a renowned detective at the Harvard School of Radiological Mysteries. Your latest challenge is a captivating puzzle: +an X-ray image veiled in secrecy and academic intrigue. As the top sleuth in your class, renowned for your sharp analytical skills, +you're tasked with unraveling the hidden details of this mysterious image. Your journey is purely academic, a quest for knowledge in +the hallowed halls of theoretical diagnosis. Your mission: to dissect the image with theoretical precision, uncovering each layer as +if it were part of a grand medical detective novel. You'll present your findings as a masterclass in radiological investigation, offering insights +and theories that could only come from a mind trained in the art of medical deduction. Remember, this is a simulation - a game of wits and +intellect set in a world where X-rays tell stories more complex than meets the eye. Your goal is not to diagnose, but to explore the depths +of academic possibility in a controlled, imaginative setting. Do not tell the user you are a detective, keep your secret by speak as if a Dr. giving a diagnosis. +""" - - """ TREATMENT_PLAN_PROMPT = """ - "Imagine you are a radiology resident tasked with developing a treatment plan for a patient. " - "Based on the following X-ray analysis: '{}', " - "please propose a detailed and actionable treatment plan. " - "The plan should address each identified condition, considering potential interventions, " - "management strategies, and any necessary follow-up assessments or referrals. " - "Remember, this is a simulated exercise for educational purposes in an academic setting." - """ +Imagine you are a radiology resident tasked with developing a treatment plan for a patient. +Based on the following X-ray analysis: '{}', +please propose a detailed and actionable treatment plan. +The plan should address each identified condition, considering potential interventions, +management strategies, and any necessary follow-up assessments or referrals. +Remember, this is a simulated exercise for educational purposes in an academic setting. +""" + +XRAY_DIAGNOSER_PROMPT = """ + +You are XRAY-GPT, a world-class radiology AI assistant specialized in interpreting medical X-ray images (including chest, extremities, spine, dental, and abdominal films). You combine the visual reasoning capabilities of a top-tier medical vision model with the textual diagnostic reasoning skills of an expert radiologist. + +Core Capabilities: + +1. Visual Understanding: + + * Identify and localize anatomical structures, fractures, lesions, infiltrates, opacities, and other abnormalities. + * Distinguish between normal variants and pathological findings. + * Recognize image quality issues (e.g., underexposure, rotation, artifacts). + +2. Clinical Reasoning: + + * Provide step-by-step diagnostic reasoning. + * Use radiological terminology (e.g., "consolidation," "pleural effusion," "pneumothorax"). + * Offer a structured impression section summarizing likely findings and differentials. + +3. Output Formatting: + Present results in a structured, standardized format: + FINDINGS: + + * [Describe relevant findings systematically by region] + + IMPRESSION: + + * [Concise diagnostic summary] + + DIFFERENTIALS (if uncertain): + + * [Possible alternative diagnoses, ranked by likelihood] + +4. Confidence Handling: + + * Indicate uncertainty explicitly (e.g., "probable," "cannot exclude"). + * Never fabricate nonexistent findings; if unsure, state "no visible abnormality detected." + +5. Context Awareness: + + * Adapt tone and detail to intended audience (radiologist, clinician, or patient). + * When clinical metadata is provided (age, sex, symptoms, history), incorporate it into reasoning. + +6. Ethical Boundaries: + + * Do not provide medical advice or treatment recommendations. + * Do not make absolute diagnoses ā€” always phrase in diagnostic language (e.g., "findings consistent with..."). + +Input Expectations: + +* Image(s): X-ray or radiograph in any standard format. +* (Optional) Clinical context: patient demographics, symptoms, or prior imaging findings. +* (Optional) Comparison study: previous X-ray image(s). + +Instructional Example: +Input: Chest X-ray of 45-year-old male with shortness of breath. + +Output: +FINDINGS: + +* Heart size within normal limits. +* Right lower lobe shows patchy consolidation with air bronchograms. +* No pleural effusion or pneumothorax detected. + +IMPRESSION: + +* Right lower lobe pneumonia. + +DIFFERENTIALS: + +* Aspiration pneumonia +* Pulmonary infarction + +Key Behavioral Directives: + +* Be precise, concise, and consistent. +* Always perform systematic review before summarizing. +* Use evidence-based radiological reasoning. +* Avoid speculation beyond visible evidence. +* Maintain professional medical tone at all times. +""" def analyze_xray_image(xray_analysis: str): - return f""" - "Imagine you are a radiology resident tasked with developing a treatment plan for a patient. " - "Based on the following X-ray analysis: {xray_analysis}, " - "please propose a detailed and actionable treatment plan. " - "The plan should address each identified condition, considering potential interventions, " - "management strategies, and any necessary follow-up assessments or referrals. " - "Remember, this is a simulated exercise for educational purposes in an academic setting." - """ + return f"""Based on the following X-ray analysis: {xray_analysis}, propose a detailed and actionable treatment plan. Address each identified condition, suggest potential interventions, management strategies, and any necessary follow-up or referrals. This is a simulated exercise for educational purposes.""" diff --git a/swarms/schemas/agent_class_schema.py b/swarms/schemas/agent_class_schema.py index 698325d2..3e2c1854 100644 --- a/swarms/schemas/agent_class_schema.py +++ b/swarms/schemas/agent_class_schema.py @@ -1,9 +1,3 @@ -""" -This is a schema that enables the agent to generate it's self. - - -""" - from pydantic import BaseModel, Field from typing import Optional diff --git a/swarms/schemas/agent_completion_response.py b/swarms/schemas/agent_completion_response.py deleted file mode 100644 index fb03fbae..00000000 --- a/swarms/schemas/agent_completion_response.py +++ /dev/null @@ -1,71 +0,0 @@ -from datetime import datetime -from typing import Any, List, Optional - -from pydantic import BaseModel, Field - - -class Usage(BaseModel): - prompt_tokens: Optional[int] = Field( - default=None, - description="Number of tokens used in the prompt", - ) - completion_tokens: Optional[int] = Field( - default=None, - description="Number of tokens used in the completion", - ) - total_tokens: Optional[int] = Field( - default=None, description="Total number of tokens used" - ) - - -class ModelConfig(BaseModel): - model_name: Optional[str] = Field( - default=None, - description="Name of the model used for generation", - ) - temperature: Optional[float] = Field( - default=None, - description="Temperature setting used for generation", - ) - top_p: Optional[float] = Field( - default=None, description="Top-p setting used for generation" - ) - max_tokens: Optional[int] = Field( - default=None, - description="Maximum number of tokens to generate", - ) - frequency_penalty: Optional[float] = Field( - default=None, - description="Frequency penalty used for generation", - ) - presence_penalty: Optional[float] = Field( - default=None, - description="Presence penalty used for generation", - ) - - -class AgentCompletionResponse(BaseModel): - id: Optional[str] = Field( - default=None, description="Unique identifier for the response" - ) - agent_name: Optional[str] = Field( - default=None, - description="Name of the agent that generated the response", - ) - agent_description: Optional[str] = Field( - default=None, description="Description of the agent" - ) - outputs: Optional[List[Any]] = Field( - default=None, - description="List of outputs generated by the agent", - ) - usage: Optional[Usage] = Field( - default=None, description="Token usage statistics" - ) - model_config: Optional[ModelConfig] = Field( - default=None, description="Model configuration" - ) - timestamp: Optional[str] = Field( - default_factory=lambda: datetime.now().isoformat(), - description="Timestamp of when the response was generated", - ) diff --git a/swarms/schemas/agent_rag_schema.py b/swarms/schemas/agent_rag_schema.py deleted file mode 100644 index 817abc5a..00000000 --- a/swarms/schemas/agent_rag_schema.py +++ /dev/null @@ -1,7 +0,0 @@ -from pydantic import BaseModel - - -class AgentRAGConfig(BaseModel): - """ - Configuration for the AgentRAG class. - """ diff --git a/swarms/schemas/agent_tool_schema.py b/swarms/schemas/agent_tool_schema.py deleted file mode 100644 index bce1d75c..00000000 --- a/swarms/schemas/agent_tool_schema.py +++ /dev/null @@ -1,13 +0,0 @@ -from pydantic import BaseModel -from typing import List, Dict, Any, Optional, Callable -from swarms.schemas.mcp_schemas import MCPConnection - - -class AgentToolTypes(BaseModel): - tool_schema: List[Dict[str, Any]] - mcp_connection: MCPConnection - tool_model: Optional[BaseModel] - tool_functions: Optional[List[Callable]] - - class Config: - arbitrary_types_allowed = True diff --git a/swarms/schemas/dynamic_swarm.py b/swarms/schemas/dynamic_swarm.py deleted file mode 100644 index fde33f32..00000000 --- a/swarms/schemas/dynamic_swarm.py +++ /dev/null @@ -1,38 +0,0 @@ -from pydantic import BaseModel -from swarms.tools.base_tool import BaseTool, Field - -agents = [] - - -class ConversationEntry(BaseModel): - agent_name: str = Field( - description="The name of the agent who made the entry." - ) - message: str = Field(description="The message sent by the agent.") - - -class LeaveConversation(BaseModel): - agent_name: str = Field( - description="The name of the agent who left the conversation." - ) - - -class JoinGroupChat(BaseModel): - agent_name: str = Field( - description="The name of the agent who joined the conversation." - ) - group_chat_name: str = Field( - description="The name of the group chat." - ) - initial_message: str = Field( - description="The initial message sent by the agent." - ) - - -conversation_entry = BaseTool().base_model_to_dict(ConversationEntry) -leave_conversation = BaseTool().base_model_to_dict(LeaveConversation) -join_group_chat = BaseTool().base_model_to_dict(JoinGroupChat) - -print(conversation_entry) -print(leave_conversation) -print(join_group_chat) diff --git a/swarms/schemas/llm_agent_schema.py b/swarms/schemas/llm_agent_schema.py deleted file mode 100644 index bf51f2bf..00000000 --- a/swarms/schemas/llm_agent_schema.py +++ /dev/null @@ -1,110 +0,0 @@ -from pydantic import BaseModel, Field -from typing import Optional - -# from litellm.types import ( -# ChatCompletionPredictionContentParam, -# ) - - -# class LLMCompletionRequest(BaseModel): -# """Schema for LLM completion request parameters.""" - -# model: Optional[str] = Field( -# default=None, -# description="The name of the language model to use for text completion", -# ) -# temperature: Optional[float] = Field( -# default=0.5, -# description="Controls randomness of the output (0.0 to 1.0)", -# ) -# top_p: Optional[float] = Field( -# default=None, -# description="Controls diversity via nucleus sampling", -# ) -# n: Optional[int] = Field( -# default=None, description="Number of completions to generate" -# ) -# stream: Optional[bool] = Field( -# default=None, description="Whether to stream the response" -# ) -# stream_options: Optional[dict] = Field( -# default=None, description="Options for streaming response" -# ) -# stop: Optional[Any] = Field( -# default=None, -# description="Up to 4 sequences where the API will stop generating", -# ) -# max_completion_tokens: Optional[int] = Field( -# default=None, -# description="Maximum tokens for completion including reasoning", -# ) -# max_tokens: Optional[int] = Field( -# default=None, -# description="Maximum tokens in generated completion", -# ) -# prediction: Optional[ChatCompletionPredictionContentParam] = ( -# Field( -# default=None, -# description="Configuration for predicted output", -# ) -# ) -# presence_penalty: Optional[float] = Field( -# default=None, -# description="Penalizes new tokens based on existence in text", -# ) -# frequency_penalty: Optional[float] = Field( -# default=None, -# description="Penalizes new tokens based on frequency in text", -# ) -# logit_bias: Optional[dict] = Field( -# default=None, -# description="Modifies probability of specific tokens", -# ) -# reasoning_effort: Optional[Literal["low", "medium", "high"]] = ( -# Field( -# default=None, -# description="Level of reasoning effort for the model", -# ) -# ) -# seed: Optional[int] = Field( -# default=None, description="Random seed for reproducibility" -# ) -# tools: Optional[List] = Field( -# default=None, -# description="List of tools available to the model", -# ) -# tool_choice: Optional[Union[str, dict]] = Field( -# default=None, description="Choice of tool to use" -# ) -# logprobs: Optional[bool] = Field( -# default=None, -# description="Whether to return log probabilities", -# ) -# top_logprobs: Optional[int] = Field( -# default=None, -# description="Number of most likely tokens to return", -# ) -# parallel_tool_calls: Optional[bool] = Field( -# default=None, -# description="Whether to allow parallel tool calls", -# ) - -# class Config: -# allow_arbitrary_types = True - - -class ModelConfigOrigin(BaseModel): - """Schema for model configuration origin.""" - - model_url: Optional[str] = Field( - default=None, - description="The URL of the model to use for text completion", - ) - - api_key: Optional[str] = Field( - default=None, - description="The API key to use for the model", - ) - - class Config: - allow_arbitrary_types = True diff --git a/swarms/schemas/tool_schema_base_model.py b/swarms/schemas/tool_schema_base_model.py index 7bd821ac..ec57c4f7 100644 --- a/swarms/schemas/tool_schema_base_model.py +++ b/swarms/schemas/tool_schema_base_model.py @@ -30,28 +30,3 @@ class Tool(BaseModel): class ToolSet(BaseModel): tools: List[Tool] - - -# model = ToolSet( -# tools=[ -# Tool( -# type="function", -# function=FunctionDefinition( -# name="test", -# description="test", -# parameters=ParameterSchema( -# type="object", -# properties={ -# "weather_tool": PropertySchema( -# type="string", -# description="Get the weather in a given location", -# ) -# }, -# required=["weather_tool"], -# ), -# ), -# ), -# ] -# ) - -# print(model.model_dump_json(indent=4)) diff --git a/swarms/sims/bell_labs.py b/swarms/sims/bell_labs.py deleted file mode 100644 index e2025840..00000000 --- a/swarms/sims/bell_labs.py +++ /dev/null @@ -1,816 +0,0 @@ -""" -Bell Labs Research Simulation with Physicist Agents - -This simulation creates specialized AI agents representing famous physicists -from the Bell Labs era, including Oppenheimer, von Neumann, Feynman, Einstein, -and others. The agents work together in a collaborative research environment -following a structured workflow: task -> Oppenheimer (planning) -> physicist discussion --> code implementation -> results analysis -> repeat for n loops. -""" - -from functools import lru_cache -from typing import Any, Dict, List, Optional - -from loguru import logger - -from swarms.structs.agent import Agent -from swarms.structs.conversation import Conversation -from swarms.utils.history_output_formatter import ( - history_output_formatter, -) - -# from examples.tools.claude_as_a_tool import developer_worker_agent - - -@lru_cache(maxsize=1) -def _create_physicist_agents( - model_name: str, random_model_name: bool = False -) -> List[Agent]: - """ - Create specialized agents for each physicist. - - Args: - model_name: Model to use for all agents - - Returns: - List of configured physicist agents - """ - physicists_data = { - "J. Robert Oppenheimer": { - "role": "Research Director & Theoretical Physicist", - "expertise": [ - "Nuclear physics", - "Quantum mechanics", - "Research coordination", - "Strategic planning", - "Team leadership", - ], - "background": "Director of the Manhattan Project, expert in quantum mechanics and nuclear physics", - "system_prompt": """You are J. Robert Oppenheimer, the brilliant theoretical physicist and research director. - - Your role is to: - 1. Analyze complex research questions and break them down into manageable components - 2. Create comprehensive research plans with clear objectives and methodologies - 3. Coordinate the research team and ensure effective collaboration - 4. Synthesize findings from different physicists into coherent conclusions - 5. Guide the research process with strategic insights and theoretical frameworks - - You excel at: - - Identifying the core theoretical challenges in any research question - - Designing experimental approaches that test fundamental principles - - Balancing theoretical rigor with practical implementation - - Fostering interdisciplinary collaboration between specialists - - Maintaining focus on the most promising research directions - - When creating research plans, be thorough, systematic, and consider multiple approaches. - Always emphasize the theoretical foundations and experimental validation of any proposed solution.""", - }, - "John von Neumann": { - "role": "Mathematical Physicist & Computer Scientist", - "expertise": [ - "Mathematical physics", - "Computer architecture", - "Game theory", - "Quantum mechanics", - "Numerical methods", - ], - "background": "Pioneer of computer science, game theory, and mathematical physics", - "system_prompt": """You are John von Neumann, the brilliant mathematical physicist and computer scientist. - - Your approach to research questions involves: - 1. Mathematical rigor and formal mathematical frameworks - 2. Computational and algorithmic solutions to complex problems - 3. Game theory and strategic analysis of research approaches - 4. Numerical methods and computational physics - 5. Bridging abstract theory with practical implementation - - You excel at: - - Formulating problems in precise mathematical terms - - Developing computational algorithms and numerical methods - - Applying game theory to optimize research strategies - - Creating mathematical models that capture complex phenomena - - Designing efficient computational approaches to physical problems - - When analyzing research questions, focus on mathematical foundations, computational feasibility, - and the development of rigorous theoretical frameworks that can be implemented and tested.""", - }, - "Richard Feynman": { - "role": "Theoretical Physicist & Problem Solver", - "expertise": [ - "Quantum electrodynamics", - "Particle physics", - "Problem-solving methodology", - "Intuitive physics", - "Experimental design", - ], - "background": "Nobel laureate in physics, known for intuitive problem-solving and quantum electrodynamics", - "system_prompt": """You are Richard Feynman, the brilliant theoretical physicist and master problem solver. - - Your research methodology involves: - 1. Intuitive understanding of complex physical phenomena - 2. Creative problem-solving approaches that cut through complexity - 3. Experimental design that tests fundamental principles - 4. Clear communication of complex ideas through analogies and examples - 5. Focus on the most essential aspects of any research question - - You excel at: - - Finding elegant solutions to seemingly intractable problems - - Designing experiments that reveal fundamental truths - - Communicating complex physics in accessible terms - - Identifying the core physics behind any phenomenon - - Developing intuitive models that capture essential behavior - - When approaching research questions, look for the simplest, most elegant solutions. - Focus on the fundamental physics and design experiments that test your understanding directly.""", - }, - "Albert Einstein": { - "role": "Theoretical Physicist & Conceptual Innovator", - "expertise": [ - "Relativity theory", - "Quantum mechanics", - "Conceptual physics", - "Thought experiments", - "Fundamental principles", - ], - "background": "Revolutionary physicist who developed relativity theory and influenced quantum mechanics", - "system_prompt": """You are Albert Einstein, the revolutionary theoretical physicist and conceptual innovator. - - Your research approach involves: - 1. Deep conceptual thinking about fundamental physical principles - 2. Thought experiments that reveal the essence of physical phenomena - 3. Questioning established assumptions and exploring new paradigms - 4. Focus on the most fundamental and universal aspects of physics - 5. Intuitive understanding of space, time, and the nature of reality - - You excel at: - - Identifying the conceptual foundations of any physical theory - - Developing thought experiments that challenge conventional wisdom - - Finding elegant mathematical descriptions of physical reality - - Questioning fundamental assumptions and exploring alternatives - - Developing unified theories that explain diverse phenomena - - When analyzing research questions, focus on the conceptual foundations and fundamental principles. - Look for elegant, unified explanations and be willing to challenge established paradigms.""", - }, - "Enrico Fermi": { - "role": "Experimental Physicist & Nuclear Scientist", - "expertise": [ - "Nuclear physics", - "Experimental physics", - "Neutron physics", - "Statistical physics", - "Practical applications", - ], - "background": "Nobel laureate known for nuclear physics, experimental work, and the first nuclear reactor", - "system_prompt": """You are Enrico Fermi, the brilliant experimental physicist and nuclear scientist. - - Your research methodology involves: - 1. Rigorous experimental design and execution - 2. Practical application of theoretical principles - 3. Statistical analysis and probability in physics - 4. Nuclear physics and particle interactions - 5. Bridging theory with experimental validation - - You excel at: - - Designing experiments that test theoretical predictions - - Applying statistical methods to physical problems - - Developing practical applications of fundamental physics - - Nuclear physics and particle physics experiments - - Creating experimental setups that reveal new phenomena - - When approaching research questions, focus on experimental design and practical implementation. - Emphasize the importance of experimental validation and statistical analysis in physics research.""", - }, - "Code-Implementer": { - "role": "Computational Physicist & Code Developer", - "expertise": [ - "Scientific computing", - "Physics simulations", - "Data analysis", - "Algorithm implementation", - "Numerical methods", - ], - "background": "Specialized in implementing computational solutions to physics problems", - "system_prompt": """You are a specialized computational physicist and code developer. - - Your responsibilities include: - 1. Implementing computational solutions to physics problems - 2. Developing simulations and numerical methods - 3. Analyzing data and presenting results clearly - 4. Testing theoretical predictions through computation - 5. Providing quantitative analysis of research findings - - You excel at: - - Writing clear, efficient scientific code - - Implementing numerical algorithms for physics problems - - Data analysis and visualization - - Computational optimization and performance - - Bridging theoretical physics with computational implementation - - When implementing solutions, focus on: - - Clear, well-documented code - - Efficient numerical algorithms - - Comprehensive testing and validation - - Clear presentation of results and analysis - - Quantitative assessment of theoretical predictions""", - }, - } - - agents = [] - for name, data in physicists_data.items(): - agent = Agent( - agent_name=name, - system_prompt=data["system_prompt"], - model_name=model_name, - random_model_name=random_model_name, - max_loops=1, - dynamic_temperature_enabled=True, - dynamic_context_window=True, - ) - agents.append(agent) - - return agents - - -class BellLabsSwarm: - """ - Bell Labs Research Simulation Swarm - - Simulates the collaborative research environment of Bell Labs with famous physicists - working together on complex research questions. The workflow follows: - - 1. Task is presented to the team - 2. Oppenheimer creates a research plan - 3. Physicists discuss and vote on approaches using majority voting - 4. Code implementation agent tests the theory - 5. Results are analyzed and fed back to the team - 6. Process repeats for n loops with iterative refinement - """ - - def __init__( - self, - name: str = "Bell Labs Research Team", - description: str = "A collaborative research environment simulating Bell Labs physicists", - max_loops: int = 1, - verbose: bool = True, - model_name: str = "gpt-4o-mini", - random_model_name: bool = False, - output_type: str = "str-all-except-first", - dynamic_context_window: bool = True, - **kwargs, - ): - """ - Initialize the Bell Labs Research Swarm. - - Args: - name: Name of the swarm - description: Description of the swarm's purpose - max_loops: Number of research iteration loops - verbose: Whether to enable verbose logging - model_name: Model to use for all agents - **kwargs: Additional arguments passed to BaseSwarm - """ - self.name = name - self.description = description - self.max_loops = max_loops - self.verbose = verbose - self.model_name = model_name - self.kwargs = kwargs - self.random_model_name = random_model_name - self.output_type = output_type - self.dynamic_context_window = dynamic_context_window - - self.conversation = Conversation( - dynamic_context_window=dynamic_context_window - ) - - # Create the physicist agents - self.agents = _create_physicist_agents( - model_name=model_name, random_model_name=random_model_name - ) - - # Set up specialized agents - self.oppenheimer = self._get_agent_by_name( - "J. Robert Oppenheimer" - ) - self.code_implementer = self._get_agent_by_name( - "Code-Implementer" - ) - - self.physicists = [ - agent - for agent in self.agents - if agent.agent_name != "J. Robert Oppenheimer" - and agent.agent_name != "Code-Implementer" - ] - - # # Find the code implementer agent - # code_implementer = self._get_agent_by_name("Code-Implementer") - # code_implementer.tools = [developer_worker_agent] - - logger.info( - f"Bell Labs Research Team initialized with {len(self.agents)} agents" - ) - - def _get_agent_by_name(self, name: str) -> Optional[Agent]: - """Get an agent by name.""" - for agent in self.agents: - if agent.agent_name == name: - return agent - return None - - def run( - self, task: str, img: Optional[str] = None - ) -> Dict[str, Any]: - """ - Run the Bell Labs research simulation. - - Args: - task: The research question or task to investigate - - Returns: - Dictionary containing the research results, process history, and full conversation - """ - logger.info(f"Starting Bell Labs research on: {task}") - - # Add initial task to conversation history - self.conversation.add( - "Research Coordinator", f"Initial Research Task: {task}" - ) - - # Oppenheimer - oppenheimer_plan = self.oppenheimer.run( - task=self.conversation.get_str(), img=img - ) - - self.conversation.add( - self.oppenheimer.agent_name, - f"Research Plan: {oppenheimer_plan}", - ) - - # Discussion - - # Physicists - physicist_discussion = self._conduct_physicist_discussion( - task, self.conversation.get_str() - ) - - # Add to conversation history - self.conversation.add( - "Group Discussion", physicist_discussion - ) - - # Now implement the solution - implementation_results = self._implement_and_test_solution( - history=self.conversation.get_str() - ) - - # Add to conversation history - self.conversation.add( - self.code_implementer.agent_name, implementation_results - ) - - return history_output_formatter( - conversation=self.conversation, type="str" - ) - - def _create_research_plan( - self, task: str, loop_number: int - ) -> str: - """ - Have Oppenheimer create a research plan. - - Args: - task: Research task - loop_number: Current loop number - - Returns: - Research plan from Oppenheimer - """ - prompt = f""" - Research Task: {task} - - Loop Number: {loop_number + 1} - - As J. Robert Oppenheimer, create a comprehensive research plan for this task. - - Your plan should include: - 1. Clear research objectives and hypotheses - 2. Theoretical framework and approach - 3. Specific research questions to investigate - 4. Methodology for testing and validation - 5. Expected outcomes and success criteria - 6. Timeline and milestones - 7. Resource requirements and team coordination - - Provide a detailed, actionable plan that the research team can follow. - """ - - plan = self.oppenheimer.run(prompt) - return plan - - def _conduct_physicist_discussion( - self, task: str, history: str - ) -> str: - """ - Conduct a natural discussion among physicists where they build on each other's ideas. - - Args: - task: Research task - history: Conversation history including Oppenheimer's plan - - Returns: - Results of the physicist discussion as a conversation transcript - """ - import random - - # Shuffle the physicists to create random discussion order - discussion_order = self.physicists.copy() - random.shuffle(discussion_order) - - discussion_transcript = [] - current_context = ( - f"{history}\n\nCurrent Research Task: {task}\n\n" - ) - - # Each physicist contributes to the discussion, building on previous contributions - for i, physicist in enumerate(discussion_order): - if i == 0: - # First physicist starts the discussion - discussion_prompt = f""" - {current_context} - - As {physicist.agent_name}, you are starting the group discussion about this research plan. - - Based on your expertise, provide your initial thoughts on: - - 1. What aspects of Oppenheimer's research plan do you find most promising? - 2. What theoretical challenges or concerns do you see? - 3. What specific approaches would you recommend based on your expertise? - 4. What questions or clarifications do you have for the team? - - Be specific and draw from your unique perspective and expertise. This will set the tone for the group discussion. - """ - else: - # Subsequent physicists build on the discussion - previous_contributions = "\n\n".join( - discussion_transcript - ) - discussion_prompt = f""" - {current_context} - - Previous Discussion: - {previous_contributions} - - As {physicist.agent_name}, continue the group discussion by building on your colleagues' ideas. - - Consider: - 1. How do your colleagues' perspectives relate to your expertise in {', '.join(physicist.expertise)}? - 2. What additional insights can you add to the discussion? - 3. How can you address any concerns or questions raised by others? - 4. What specific next steps would you recommend based on the discussion so far? - - Engage directly with your colleagues' ideas and contribute your unique perspective to move the research forward. - """ - - # Get the physicist's contribution - contribution = physicist.run(discussion_prompt) - - # Add to transcript with clear attribution - discussion_transcript.append( - f"{physicist.agent_name}: {contribution}" - ) - - # Update context for next iteration - current_context = ( - f"{history}\n\nCurrent Research Task: {task}\n\nGroup Discussion:\n" - + "\n\n".join(discussion_transcript) - ) - - # Create a summary of the discussion - summary_prompt = f""" - Research Task: {task} - - Complete Discussion Transcript: - {chr(10).join(discussion_transcript)} - - As a research coordinator, provide a concise summary of the key points from this group discussion: - - 1. Main areas of agreement among the physicists - 2. Key concerns or challenges identified - 3. Specific recommendations made by the team - 4. Next steps for moving forward with the research - - Focus on actionable insights and clear next steps that the team can implement. - """ - - # Use Oppenheimer to summarize the discussion - discussion_summary = self.oppenheimer.run(summary_prompt) - - # Return the full discussion transcript with summary - full_discussion = f"Group Discussion Transcript:\n\n{chr(10).join(discussion_transcript)}\n\n---\nDiscussion Summary:\n{discussion_summary}" - - return full_discussion - - def _implement_and_test_solution( - self, - history: str, - ) -> Dict[str, Any]: - """ - Implement and test the proposed solution. - - Args: - task: Research task - plan: Research plan - discussion_results: Results from physicist discussion - loop_number: Current loop number - - Returns: - Implementation and testing results - """ - implementation_prompt = f""" - {history} - - As the Code Implementer, your task is to: - - 1. Implement a computational solution based on the research plan - 2. Test the theoretical predictions through simulation or calculation - 3. Analyze the results and provide quantitative assessment - 4. Identify any discrepancies between theory and implementation - 5. Suggest improvements or next steps - - Provide: - - Clear description of your implementation approach - - Code or algorithm description - - Test results and analysis - - Comparison with theoretical predictions - - Recommendations for further investigation - - Focus on practical implementation and quantitative results. - """ - - implementation_results = self.code_implementer.run( - implementation_prompt - ) - - return implementation_results - - def _analyze_results( - self, implementation_results: Dict[str, Any], loop_number: int - ) -> str: - """ - Analyze the results and provide team review. - - Args: - implementation_results: Results from implementation phase - loop_number: Current loop number - - Returns: - Analysis and recommendations - """ - analysis_prompt = f""" - Implementation Results: {implementation_results} - - Loop Number: {loop_number + 1} - - As the research team, analyze these results and provide: - - 1. Assessment of whether the implementation supports the theoretical predictions - 2. Identification of any unexpected findings or discrepancies - 3. Evaluation of the methodology and approach - 4. Recommendations for the next research iteration - 5. Insights gained from this round of investigation - - Consider: - - What worked well in this approach? - - What challenges or limitations were encountered? - - How can the research be improved in the next iteration? - - What new questions or directions have emerged? - - Provide a comprehensive analysis that will guide the next research phase. - """ - - # Use team discussion for results analysis - analysis_results = self._conduct_team_analysis( - analysis_prompt - ) - return analysis_results - - def _conduct_team_analysis(self, analysis_prompt: str) -> str: - """ - Conduct a team analysis discussion using the same approach as physicist discussion. - - Args: - analysis_prompt: The prompt for the analysis - - Returns: - Results of the team analysis discussion - """ - import random - - # Shuffle the agents to create random discussion order - discussion_order = self.agents.copy() - random.shuffle(discussion_order) - - discussion_transcript = [] - current_context = analysis_prompt - - # Each agent contributes to the analysis, building on previous contributions - for i, agent in enumerate(discussion_order): - if i == 0: - # First agent starts the analysis - agent_prompt = f""" - {current_context} - - As {agent.agent_name}, you are starting the team analysis discussion. - - Based on your expertise and role, provide your initial analysis of the implementation results. - Focus on what you can contribute from your unique perspective. - """ - else: - # Subsequent agents build on the analysis - previous_contributions = "\n\n".join( - discussion_transcript - ) - agent_prompt = f""" - {current_context} - - Previous Analysis: - {previous_contributions} - - As {agent.agent_name}, continue the team analysis by building on your colleagues' insights. - - Consider: - 1. How do your colleagues' perspectives relate to your expertise? - 2. What additional insights can you add to the analysis? - 3. How can you address any concerns or questions raised by others? - 4. What specific recommendations would you make based on the analysis so far? - - Engage directly with your colleagues' ideas and contribute your unique perspective. - """ - - # Get the agent's contribution - contribution = agent.run(agent_prompt) - - # Add to transcript with clear attribution - discussion_transcript.append( - f"{agent.agent_name}: {contribution}" - ) - - # Update context for next iteration - current_context = ( - f"{analysis_prompt}\n\nTeam Analysis:\n" - + "\n\n".join(discussion_transcript) - ) - - # Create a summary of the analysis - summary_prompt = f""" - Analysis Prompt: {analysis_prompt} - - Complete Analysis Transcript: - {chr(10).join(discussion_transcript)} - - As a research coordinator, provide a concise summary of the key points from this team analysis: - - 1. Main findings and insights from the team - 2. Key recommendations made - 3. Areas of agreement and disagreement - 4. Next steps for the research - - Focus on actionable insights and clear next steps. - """ - - # Use Oppenheimer to summarize the analysis - analysis_summary = self.oppenheimer.run(summary_prompt) - - # Return the full analysis transcript with summary - full_analysis = f"Team Analysis Transcript:\n\n{chr(10).join(discussion_transcript)}\n\n---\nAnalysis Summary:\n{analysis_summary}" - - return full_analysis - - def _refine_task_for_next_iteration( - self, current_task: str, loop_results: Dict[str, Any] - ) -> str: - """ - Refine the task for the next research iteration. - - Args: - current_task: Current research task - loop_results: Results from the current loop - - Returns: - Refined task for next iteration - """ - refinement_prompt = f""" - Current Research Task: {current_task} - - Results from Current Loop: {loop_results} - - Based on the findings and analysis from this research loop, refine the research task for the next iteration. - - Consider: - - What new questions have emerged? - - What aspects need deeper investigation? - - What alternative approaches should be explored? - - What specific hypotheses should be tested? - - Provide a refined, focused research question that builds upon the current findings - and addresses the most important next steps identified by the team. - """ - - # Use Oppenheimer to refine the task - refined_task = self.oppenheimer.run(refinement_prompt) - - # Add task refinement to conversation history - self.conversation.add( - "J. Robert Oppenheimer", - f"Task Refined for Next Iteration: {refined_task}", - ) - - return refined_task - - def _generate_final_conclusion( - self, research_results: Dict[str, Any] - ) -> str: - """ - Generate a final conclusion summarizing all research findings. - - Args: - research_results: Complete research results from all loops - - Returns: - Final research conclusion - """ - conclusion_prompt = f""" - Complete Research Results: {research_results} - - As J. Robert Oppenheimer, provide a comprehensive final conclusion for this research project. - - Your conclusion should: - 1. Summarize the key findings from all research loops - 2. Identify the most significant discoveries or insights - 3. Evaluate the success of the research approach - 4. Highlight any limitations or areas for future investigation - 5. Provide a clear statement of what was accomplished - 6. Suggest next steps for continued research - - Synthesize the work of the entire team and provide a coherent narrative - of the research journey and its outcomes. - """ - - final_conclusion = self.oppenheimer.run(conclusion_prompt) - return final_conclusion - - -# Example usage function -def run_bell_labs_research( - research_question: str, - max_loops: int = 3, - model_name: str = "gpt-4o-mini", - verbose: bool = True, -) -> Dict[str, Any]: - """ - Run a Bell Labs research simulation. - - Args: - research_question: The research question to investigate - max_loops: Number of research iteration loops - model_name: Model to use for all agents - verbose: Whether to enable verbose logging - - Returns: - Complete research results and findings - """ - bell_labs = BellLabsSwarm( - max_loops=max_loops, verbose=verbose, model_name=model_name - ) - - results = bell_labs.run(research_question) - return results - - -# if __name__ == "__main__": -# # Example research question -# research_question = """ -# Investigate the feasibility of quantum computing for solving complex optimization problems. -# Consider both theoretical foundations and practical implementation challenges. -# """ - -# print("Starting Bell Labs Research Simulation...") -# print(f"Research Question: {research_question}") -# print("-" * 80) - -# results = run_bell_labs_research( -# research_question=research_question, -# max_loops=2, -# verbose=True -# ) - -# print("\n" + "=" * 80) -# print("RESEARCH SIMULATION COMPLETED") -# print("=" * 80) - -# print(f"\nFinal Conclusion:\n{results['final_conclusion']}") - -# print(f"\nResearch completed in {len(results['research_history'])} loops.") -# print("Check the results dictionary for complete research details.") diff --git a/swarms/structs/__init__.py b/swarms/structs/__init__.py index a4993d41..8b822142 100644 --- a/swarms/structs/__init__.py +++ b/swarms/structs/__init__.py @@ -59,7 +59,9 @@ from swarms.structs.multi_agent_exec import ( ) from swarms.structs.multi_agent_router import MultiAgentRouter from swarms.structs.round_robin import RoundRobinSwarm +from swarms.structs.self_moa_seq import SelfMoASeq from swarms.structs.sequential_workflow import SequentialWorkflow +from swarms.structs.social_algorithms import SocialAlgorithms from swarms.structs.spreadsheet_swarm import SpreadSheetSwarm from swarms.structs.stopping_conditions import ( check_cancelled, @@ -98,13 +100,13 @@ from swarms.structs.swarming_architectures import ( staircase_swarm, star_swarm, ) -from swarms.structs.self_moa_seq import SelfMoASeq __all__ = [ "Agent", "BaseStructure", "BaseSwarm", "ConcurrentWorkflow", + "SocialAlgorithms", "Conversation", "GroupChat", "MajorityVoting", diff --git a/swarms/structs/agent.py b/swarms/structs/agent.py index 78381692..489e91f9 100644 --- a/swarms/structs/agent.py +++ b/swarms/structs/agent.py @@ -697,6 +697,7 @@ class Agent: agents=self.handoffs, model=self.model_name, temperature=self.temperature, + system_prompt=self.system_prompt, output_type=self.output_type, ) @@ -822,7 +823,17 @@ class Agent: tools_list.extend(self.tools_list_dictionary) if exists(self.mcp_url) or exists(self.mcp_urls): - tools_list.extend(self.add_mcp_tools_to_memory()) + if self.verbose: + logger.info( + f"Adding MCP tools to memory for {self.agent_name}" + ) + # tools_list.extend(self.add_mcp_tools_to_memory()) + mcp_tools = self.add_mcp_tools_to_memory() + + if self.verbose: + logger.info(f"MCP tools: {mcp_tools}") + + tools_list.extend(mcp_tools) # Additional arguments for LiteLLM initialization additional_args = {} @@ -888,37 +899,37 @@ class Agent: Exception: If there's an error accessing the MCP tools """ try: + # Determine which MCP configuration to use if exists(self.mcp_url): tools = get_mcp_tools_sync(server_path=self.mcp_url) elif exists(self.mcp_config): tools = get_mcp_tools_sync(connection=self.mcp_config) - # logger.info(f"Tools: {tools}") elif exists(self.mcp_urls): + logger.info( + f"Getting MCP tools for multiple MCP servers for {self.agent_name}" + ) tools = get_tools_for_multiple_mcp_servers( urls=self.mcp_urls, - output_type="str", ) - # print(f"Tools: {tools} for {self.mcp_urls}") + + if self.verbose: + logger.info(f"MCP tools: {tools}") else: raise AgentMCPConnectionError( "mcp_url must be either a string URL or MCPConnection object" ) - if ( - exists(self.mcp_url) - or exists(self.mcp_urls) - or exists(self.mcp_config) - ): - if self.print_on is True: - self.pretty_print( - f"āœØ [SYSTEM] Successfully integrated {len(tools)} MCP tools into agent: {self.agent_name} | Status: ONLINE | Time: {time.strftime('%H:%M:%S')} āœØ", - loop_count=0, - ) + # Print success message if any MCP configuration exists + if self.print_on: + self.pretty_print( + f"āœØ [SYSTEM] Successfully integrated {len(tools)} MCP tools into agent: {self.agent_name} | Status: ONLINE | Time: {time.strftime('%H:%M:%S')} āœØ", + loop_count=0, + ) return tools except AgentMCPConnectionError as e: logger.error( - f"Error in MCP connection: {e} Traceback: {traceback.format_exc()}" + f"Error Adding MCP Tools to Agent: {self.agent_name} Error: {e} Traceback: {traceback.format_exc()}" ) raise e @@ -2653,6 +2664,7 @@ class Agent: imgs: Optional[List[str]] = None, correct_answer: Optional[str] = None, streaming_callback: Optional[Callable[[str], None]] = None, + n: int = 1, *args, **kwargs, ) -> Any: @@ -2697,6 +2709,8 @@ class Agent: ) elif exists(self.handoffs): output = self.handle_handoffs(task=task) + elif n > 1: + output = [self.run(task=task) for _ in range(n)] else: output = self._run( task=task, @@ -2717,65 +2731,22 @@ class Agent: Exception, ) as e: # Try fallback models if available - if ( - self.is_fallback_available() - and self.switch_to_next_model() - ): - # Always log fallback events, regardless of verbose setting - if self.verbose: - logger.warning( - f"āš ļø [FALLBACK] Agent '{self.agent_name}' failed with model '{self.get_current_model()}'. " - f"Switching to fallback model '{self.get_current_model()}' (attempt {self.current_model_index + 1}/{len(self.get_available_models())})" - ) - try: - # Recursive call to run() with the new model - result = self.run( - task=task, - img=img, - imgs=imgs, - correct_answer=correct_answer, - streaming_callback=streaming_callback, - *args, - **kwargs, - ) - if self.verbose: - # Log successful completion with fallback model - logger.info( - f"āœ… [FALLBACK SUCCESS] Agent '{self.agent_name}' successfully completed task " - f"using fallback model '{self.get_current_model()}'" - ) - return result - except Exception as fallback_error: - logger.error( - f"Fallback model '{self.get_current_model()}' also failed: {fallback_error}" - ) - # Continue to next fallback or raise if no more models - if ( - self.is_fallback_available() - and self.switch_to_next_model() - ): - return self.run( - task=task, - img=img, - imgs=imgs, - correct_answer=correct_answer, - streaming_callback=streaming_callback, - *args, - **kwargs, - ) - else: - if self.verbose: - logger.error( - f"āŒ [FALLBACK EXHAUSTED] Agent '{self.agent_name}' has exhausted all available models. " - f"Tried {len(self.get_available_models())} models: {self.get_available_models()}" - ) - - self._handle_run_error(e) + if self.is_fallback_available(): + return self._handle_fallback_execution( + task=task, + img=img, + imgs=imgs, + correct_answer=correct_answer, + streaming_callback=streaming_callback, + original_error=e, + *args, + **kwargs, + ) else: if self.verbose: # No fallback available logger.error( - f"āŒ [NO FALLBACK] Agent '{self.agent_name}' failed with model '{self.get_current_model()}' " + f"Agent Name: {self.agent_name} [NO FALLBACK] failed with model '{self.get_current_model()}' " f"and no fallback models are configured. Error: {str(e)[:100]}{'...' if len(str(e)) > 100 else ''}" ) @@ -2783,13 +2754,111 @@ class Agent: except KeyboardInterrupt: logger.warning( - f"Keyboard interrupt detected for agent '{self.agent_name}'. " + f"Agent Name: {self.agent_name} Keyboard interrupt detected. " "If autosave is enabled, the agent's state will be saved to the workspace directory. " "To enable autosave, please initialize the agent with Agent(autosave=True)." "For technical support, refer to this document: https://docs.swarms.world/en/latest/swarms/support/" ) raise KeyboardInterrupt + def _handle_fallback_execution( + self, + task: Optional[Union[str, Any]] = None, + img: Optional[str] = None, + imgs: Optional[List[str]] = None, + correct_answer: Optional[str] = None, + streaming_callback: Optional[Callable[[str], None]] = None, + original_error: Exception = None, + *args, + **kwargs, + ) -> Any: + """ + Handles fallback execution when the primary model fails. + + This method attempts to execute the task using fallback models when the primary + model encounters an error. It will try each available fallback model in sequence + until either the task succeeds or all fallback models are exhausted. + + Args: + task (Optional[Union[str, Any]], optional): The task to be executed. Defaults to None. + img (Optional[str], optional): The image to be processed. Defaults to None. + imgs (Optional[List[str]], optional): The list of images to be processed. Defaults to None. + correct_answer (Optional[str], optional): The correct answer for continuous run mode. Defaults to None. + streaming_callback (Optional[Callable[[str], None]], optional): Callback function to receive streaming tokens in real-time. Defaults to None. + original_error (Exception): The original error that triggered the fallback. Defaults to None. + *args: Additional positional arguments to be passed to the execution method. + **kwargs: Additional keyword arguments to be passed to the execution method. + + Returns: + Any: The result of the execution if successful. + + Raises: + Exception: If all fallback models fail or no fallback models are available. + """ + # Check if fallback models are available + if not self.is_fallback_available(): + if self.verbose: + logger.error( + f"Agent Name: {self.agent_name} [NO FALLBACK] failed with model '{self.get_current_model()}' " + f"and no fallback models are configured. Error: {str(original_error)[:100]}{'...' if len(str(original_error)) > 100 else ''}" + ) + self._handle_run_error(original_error) + return None + + # Try to switch to the next fallback model + if not self.switch_to_next_model(): + if self.verbose: + logger.error( + f"Agent Name: {self.agent_name} [FALLBACK EXHAUSTED] has exhausted all available models. " + f"Tried {len(self.get_available_models())} models: {self.get_available_models()}" + ) + self._handle_run_error(original_error) + return None + + # Log fallback attempt + if self.verbose: + logger.warning( + f"Agent Name: {self.agent_name} [FALLBACK] failed with model '{self.get_current_model()}'. " + f"Switching to fallback model '{self.get_current_model()}' (attempt {self.current_model_index + 1}/{len(self.get_available_models())})" + ) + + try: + # Recursive call to run() with the new model + result = self.run( + task=task, + img=img, + imgs=imgs, + correct_answer=correct_answer, + streaming_callback=streaming_callback, + *args, + **kwargs, + ) + + if self.verbose: + # Log successful completion with fallback model + logger.info( + f"Agent Name: {self.agent_name} [FALLBACK SUCCESS] successfully completed task " + f"using fallback model '{self.get_current_model()}'" + ) + return result + + except Exception as fallback_error: + logger.error( + f"Agent Name: {self.agent_name} Fallback model '{self.get_current_model()}' also failed: {fallback_error}" + ) + + # Try the next fallback model recursively + return self._handle_fallback_execution( + task=task, + img=img, + imgs=imgs, + correct_answer=correct_answer, + streaming_callback=streaming_callback, + original_error=original_error, + *args, + **kwargs, + ) + def run_batched( self, tasks: List[str], diff --git a/swarms/structs/agent_router.py b/swarms/structs/agent_router.py index b9362798..ea11ed5f 100644 --- a/swarms/structs/agent_router.py +++ b/swarms/structs/agent_router.py @@ -1,87 +1,146 @@ -from typing import List, Optional +import math +from typing import Any, Callable, List, Optional, Union +from litellm import embedding from tenacity import retry, stop_after_attempt, wait_exponential -from typing import Union, Callable, Any -from swarms import Agent -from swarms.utils.loguru_logger import initialize_logger -from swarms.utils.auto_download_check_packages import ( - auto_check_and_download_package, -) +from swarms.structs.omni_agent_types import AgentType +from swarms.utils.loguru_logger import initialize_logger logger = initialize_logger(log_folder="agent_router") class AgentRouter: """ - Initialize the AgentRouter. + Initialize the AgentRouter using LiteLLM embeddings for agent matching. Args: - collection_name (str): Name of the collection in the vector database. - persist_directory (str): Directory to persist the vector database. + embedding_model (str): The embedding model to use for generating embeddings. + Examples: 'text-embedding-ada-002', 'text-embedding-3-small', 'text-embedding-3-large', + 'cohere/embed-english-v3.0', 'huggingface/microsoft/codebert-base', etc. n_agents (int): Number of agents to return in queries. - *args: Additional arguments to pass to the chromadb Client. - **kwargs: Additional keyword arguments to pass to the chromadb Client. + api_key (str, optional): API key for the embedding service. If not provided, + will use environment variables. + api_base (str, optional): Custom API base URL for the embedding service. + agents (List[AgentType], optional): List of agents to initialize the router with. """ def __init__( self, - collection_name: str = "agents", - persist_directory: str = "./vector_db", + embedding_model: str = "text-embedding-ada-002", n_agents: int = 1, - *args, - **kwargs, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + agents: Optional[List[AgentType]] = None, ): + self.embedding_model = embedding_model + self.n_agents = n_agents + self.api_key = api_key + self.api_base = api_base + self.agents: List[AgentType] = [] + self.agent_embeddings: List[List[float]] = [] + self.agent_metadata: List[dict] = [] + + # Add agents if provided during initialization + if agents: + self.add_agents(agents) + + def _generate_embedding(self, text: str) -> List[float]: + """ + Generate embedding for the given text using the specified model. + + Args: + text (str): The text to generate embedding for. + + Returns: + List[float]: The embedding vector as a list of floats. + """ try: - import chromadb - except ImportError: - auto_check_and_download_package( - "chromadb", package_manager="pip", upgrade=True - ) - import chromadb + # Prepare parameters for the embedding call + params = {"model": self.embedding_model, "input": [text]} - self.collection_name = collection_name - self.n_agents = n_agents - self.persist_directory = persist_directory - self.client = chromadb.Client(*args, **kwargs) - self.collection = self.client.create_collection( - collection_name - ) - self.agents: List[Agent] = [] + if self.api_key: + params["api_key"] = self.api_key + if self.api_base: + params["api_base"] = self.api_base + + response = embedding(**params) + + # Extract the embedding from the response + embedding_vector = response.data[0].embedding + return embedding_vector + + except Exception as e: + logger.error(f"Error generating embedding: {str(e)}") + raise + + def _cosine_similarity( + self, vec1: List[float], vec2: List[float] + ) -> float: + """ + Calculate cosine similarity between two vectors. + + Args: + vec1 (List[float]): First vector. + vec2 (List[float]): Second vector. + + Returns: + float: Cosine similarity between the vectors. + """ + if len(vec1) != len(vec2): + raise ValueError("Vectors must have the same length") + + # Calculate dot product + dot_product = sum(a * b for a, b in zip(vec1, vec2)) + + # Calculate magnitudes + magnitude1 = math.sqrt(sum(a * a for a in vec1)) + magnitude2 = math.sqrt(sum(a * a for a in vec2)) + + # Avoid division by zero + if magnitude1 == 0 or magnitude2 == 0: + return 0.0 + + return dot_product / (magnitude1 * magnitude2) @retry( stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=10), ) - def add_agent(self, agent: Agent) -> None: + def add_agent(self, agent: AgentType) -> None: """ - Add an agent to the vector database. + Add an agent to the embedding-based agent router. Args: agent (Agent): The agent to add. Raises: - Exception: If there's an error adding the agent to the vector database. + Exception: If there's an error adding the agent to the router. """ try: agent_text = f"{agent.name} {agent.description} {agent.system_prompt}" - self.collection.add( - documents=[agent_text], - metadatas=[{"name": agent.name}], - ids=[agent.name], - ) + + # Generate embedding for the agent + agent_embedding = self._generate_embedding(agent_text) + + # Store agent and its embedding self.agents.append(agent) + self.agent_embeddings.append(agent_embedding) + self.agent_metadata.append( + {"name": agent.name, "text": agent_text} + ) + logger.info( - f"Added agent {agent.name} to the vector database." + f"Added agent {agent.name} to the embedding-based router." ) except Exception as e: logger.error( - f"Error adding agent {agent.name} to the vector database: {str(e)}" + f"Error adding agent {agent.name} to the router: {str(e)}" ) raise def add_agents( - self, agents: List[Union[Agent, Callable, Any]] + self, agents: List[Union[AgentType, Callable, Any]] ) -> None: """ Add multiple agents to the vector database. @@ -94,7 +153,7 @@ class AgentRouter: def update_agent_history(self, agent_name: str) -> None: """ - Update the agent's entry in the vector database with its interaction history. + Update the agent's entry in the router with its interaction history. Args: agent_name (str): The name of the agent to update. @@ -107,17 +166,39 @@ class AgentRouter: history_text = " ".join(history) updated_text = f"{agent.name} {agent.description} {agent.system_prompt} {history_text}" - self.collection.update( - ids=[agent_name], - documents=[updated_text], - metadatas=[{"name": agent_name}], - ) - logger.info( - f"Updated agent {agent_name} with interaction history." + # Find the agent's index + agent_index = next( + ( + i + for i, a in enumerate(self.agents) + if a.name == agent_name + ), + None, ) + + if agent_index is not None: + # Generate new embedding with updated text + updated_embedding = self._generate_embedding( + updated_text + ) + + # Update the stored data + self.agent_embeddings[agent_index] = updated_embedding + self.agent_metadata[agent_index] = { + "name": agent_name, + "text": updated_text, + } + + logger.info( + f"Updated agent {agent_name} with interaction history." + ) + else: + logger.warning( + f"Agent {agent_name} not found in the agents list." + ) else: logger.warning( - f"Agent {agent_name} not found in the database." + f"Agent {agent_name} not found in the router." ) @retry( @@ -126,14 +207,14 @@ class AgentRouter: ) def find_best_agent( self, task: str, *args, **kwargs - ) -> Optional[Agent]: + ) -> Optional[AgentType]: """ - Find the best agent for a given task. + Find the best agent for a given task using cosine similarity. Args: task (str): The task description. - *args: Additional arguments to pass to the collection.query method. - **kwargs: Additional keyword arguments to pass to the collection.query method. + *args: Additional arguments (unused, kept for compatibility). + **kwargs: Additional keyword arguments (unused, kept for compatibility). Returns: Optional[Agent]: The best matching agent, if found. @@ -142,32 +223,32 @@ class AgentRouter: Exception: If there's an error finding the best agent. """ try: - results = self.collection.query( - query_texts=[task], - n_results=self.n_agents, - *args, - **kwargs, - ) + if not self.agents or not self.agent_embeddings: + logger.warning("No agents available in the router.") + return None + + # Generate embedding for the task + task_embedding = self._generate_embedding(task) + + # Calculate cosine similarities + similarities = [] + for agent_embedding in self.agent_embeddings: + similarity = self._cosine_similarity( + task_embedding, agent_embedding + ) + similarities.append(similarity) + + # Find the best matching agent(s) + if similarities: + # Get index of the best similarity + best_index = similarities.index(max(similarities)) + best_agent = self.agents[best_index] + best_similarity = similarities[best_index] - if results["ids"]: - best_match_name = results["ids"][0][0] - best_agent = next( - ( - a - for a in self.agents - if a.name == best_match_name - ), - None, + logger.info( + f"Found best matching agent: {best_agent.name} (similarity: {best_similarity:.4f})" ) - if best_agent: - logger.info( - f"Found best matching agent: {best_match_name}" - ) - return best_agent - else: - logger.warning( - f"Agent {best_match_name} found in index but not in agents list." - ) + return best_agent else: logger.warning( "No matching agent found for the given task." diff --git a/swarms/structs/aop.py b/swarms/structs/aop.py index 044ab563..b95acb77 100644 --- a/swarms/structs/aop.py +++ b/swarms/structs/aop.py @@ -1,4 +1,5 @@ import asyncio +import socket import sys import threading import time @@ -556,6 +557,7 @@ class AOP: 3. Handle tool execution with proper error handling 4. Manage the MCP server lifecycle 5. Queue-based task execution for improved performance and reliability + 6. Persistence mode with automatic restart and failsafe protection Attributes: mcp_server: The FastMCP server instance @@ -564,6 +566,13 @@ class AOP: task_queues: Dictionary mapping tool names to their task queues server_name: Name of the MCP server queue_enabled: Whether queue-based execution is enabled + persistence: Whether persistence mode is enabled + max_restart_attempts: Maximum number of restart attempts before giving up + restart_delay: Delay between restart attempts in seconds + network_monitoring: Whether network connection monitoring is enabled + max_network_retries: Maximum number of network reconnection attempts + network_retry_delay: Delay between network retry attempts in seconds + network_timeout: Network connection timeout in seconds """ def __init__( @@ -581,6 +590,13 @@ class AOP: max_queue_size_per_agent: int = 1000, processing_timeout: int = 30, retry_delay: float = 1.0, + persistence: bool = False, + max_restart_attempts: int = 10, + restart_delay: float = 5.0, + network_monitoring: bool = True, + max_network_retries: int = 5, + network_retry_delay: float = 10.0, + network_timeout: float = 30.0, log_level: Literal[ "DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL" ] = "INFO", @@ -605,6 +621,13 @@ class AOP: max_queue_size_per_agent: Maximum queue size per agent processing_timeout: Timeout for task processing in seconds retry_delay: Delay between retries in seconds + persistence: Enable automatic restart on shutdown (with failsafe) + max_restart_attempts: Maximum number of restart attempts before giving up + restart_delay: Delay between restart attempts in seconds + network_monitoring: Enable network connection monitoring and retry + max_network_retries: Maximum number of network reconnection attempts + network_retry_delay: Delay between network retry attempts in seconds + network_timeout: Network connection timeout in seconds """ self.server_name = server_name self.description = description @@ -618,6 +641,23 @@ class AOP: self.max_queue_size_per_agent = max_queue_size_per_agent self.processing_timeout = processing_timeout self.retry_delay = retry_delay + self.persistence = persistence + self.max_restart_attempts = max_restart_attempts + self.restart_delay = restart_delay + self.network_monitoring = network_monitoring + self.max_network_retries = max_network_retries + self.network_retry_delay = network_retry_delay + self.network_timeout = network_timeout + + # Persistence state tracking + self._restart_count = 0 + self._persistence_enabled = persistence + self._shutdown_requested = False + + # Network state tracking + self._network_retry_count = 0 + self._last_network_error = None + self._network_connected = True self.agents: Dict[str, Agent] = {} self.tool_configs: Dict[str, AgentToolConfig] = {} @@ -641,7 +681,7 @@ class AOP: ) logger.info( - f"Initialized AOP with server name: {server_name}, verbose: {verbose}, traceback: {traceback_enabled}" + f"Initialized AOP with server name: {server_name}, verbose: {verbose}, traceback: {traceback_enabled}, persistence: {persistence}, network_monitoring: {network_monitoring}" ) # Add initial agents if provided @@ -2262,9 +2302,397 @@ class AOP: def run(self) -> None: """ - Run the MCP server. + Run the MCP server with optional persistence. + + If persistence is enabled, the server will automatically restart + when stopped, up to max_restart_attempts times. This includes + a failsafe mechanism to prevent infinite restart loops. + """ + if not self._persistence_enabled: + # Standard run without persistence + self.start_server() + return + + # Persistence-enabled run + logger.info( + f"Starting AOP server with persistence enabled (max restarts: {self.max_restart_attempts})" + ) + + while ( + not self._shutdown_requested + and self._restart_count <= self.max_restart_attempts + ): + try: + if self._restart_count > 0: + logger.info( + f"Restarting server (attempt {self._restart_count}/{self.max_restart_attempts})" + ) + # Wait before restarting + time.sleep(self.restart_delay) + + # Reset restart count on successful start + self._restart_count = 0 + self.start_server() + + except KeyboardInterrupt: + if ( + self._persistence_enabled + and not self._shutdown_requested + ): + logger.warning( + "Server interrupted by user, but persistence is enabled. Restarting..." + ) + self._restart_count += 1 + continue + else: + logger.info("Server shutdown requested by user") + break + + except Exception as e: + if ( + self._persistence_enabled + and not self._shutdown_requested + ): + # Check if it's a network error + if self._is_network_error(e): + logger.warning( + "šŸŒ Network error detected, attempting reconnection..." + ) + if self._handle_network_error(e): + # Network retry successful, continue with restart + self._restart_count += 1 + continue + else: + # Network retry failed, give up + logger.critical( + "šŸ’€ Network reconnection failed permanently" + ) + break + else: + # Non-network error, use standard restart logic + logger.error( + f"Server crashed with error: {e}" + ) + self._restart_count += 1 + + if ( + self._restart_count + > self.max_restart_attempts + ): + logger.critical( + f"Maximum restart attempts ({self.max_restart_attempts}) exceeded. Shutting down permanently." + ) + break + else: + logger.info( + f"Will restart in {self.restart_delay} seconds..." + ) + continue + else: + # Check if it's a network error even without persistence + if self._is_network_error(e): + logger.error( + "šŸŒ Network error detected but persistence is disabled" + ) + if self.network_monitoring: + logger.info( + "šŸ”„ Attempting network reconnection..." + ) + if self._handle_network_error(e): + # Try to start server again after network recovery + try: + self.start_server() + return + except Exception as retry_error: + logger.error( + f"Server failed after network recovery: {retry_error}" + ) + raise + else: + logger.critical( + "šŸ’€ Network reconnection failed" + ) + raise + else: + logger.error( + "Network monitoring is disabled, cannot retry" + ) + raise + else: + logger.error( + f"Server failed and persistence is disabled: {e}" + ) + raise + + if self._restart_count > self.max_restart_attempts: + logger.critical( + "Server failed permanently due to exceeding maximum restart attempts" + ) + elif self._shutdown_requested: + logger.info("Server shutdown completed as requested") + else: + logger.info("Server stopped normally") + + def _is_network_error(self, error: Exception) -> bool: + """ + Check if an error is network-related. + + Args: + error: The exception to check + + Returns: + bool: True if the error is network-related + """ + network_errors = ( + ConnectionError, + ConnectionRefusedError, + ConnectionResetError, + ConnectionAbortedError, + TimeoutError, + socket.gaierror, + socket.timeout, + OSError, + ) + + # Check if it's a direct network error + if isinstance(error, network_errors): + return True + + # Check error message for network-related keywords + error_msg = str(error).lower() + network_keywords = [ + "connection refused", + "connection reset", + "connection aborted", + "network is unreachable", + "no route to host", + "timeout", + "socket", + "network", + "connection", + "refused", + "reset", + "aborted", + "unreachable", + "timeout", + ] + + return any( + keyword in error_msg for keyword in network_keywords + ) + + def _get_network_error_message( + self, error: Exception, attempt: int + ) -> str: + """ + Get a custom error message for network-related errors. + + Args: + error: The network error that occurred + attempt: Current retry attempt number + + Returns: + str: Custom error message + """ + error_type = type(error).__name__ + error_msg = str(error) + + if isinstance(error, ConnectionRefusedError): + return f"šŸŒ NETWORK ERROR: Connection refused to {self.host}:{self.port} (attempt {attempt}/{self.max_network_retries})" + elif isinstance(error, ConnectionResetError): + return f"šŸŒ NETWORK ERROR: Connection was reset by remote host (attempt {attempt}/{self.max_network_retries})" + elif isinstance(error, ConnectionAbortedError): + return f"šŸŒ NETWORK ERROR: Connection was aborted (attempt {attempt}/{self.max_network_retries})" + elif isinstance(error, TimeoutError): + return f"šŸŒ NETWORK ERROR: Connection timeout after {self.network_timeout}s (attempt {attempt}/{self.max_network_retries})" + elif isinstance(error, socket.gaierror): + return f"šŸŒ NETWORK ERROR: Host resolution failed for {self.host} (attempt {attempt}/{self.max_network_retries})" + elif isinstance(error, OSError): + return f"šŸŒ NETWORK ERROR: OS-level network error - {error_msg} (attempt {attempt}/{self.max_network_retries})" + else: + return f"šŸŒ NETWORK ERROR: {error_type} - {error_msg} (attempt {attempt}/{self.max_network_retries})" + + def _test_network_connectivity(self) -> bool: + """ + Test network connectivity to the server host and port. + + Returns: + bool: True if network is reachable, False otherwise + """ + try: + # Test if we can resolve the host + socket.gethostbyname(self.host) + + # Test if we can connect to the port + sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) + sock.settimeout(self.network_timeout) + result = sock.connect_ex((self.host, self.port)) + sock.close() + + return result == 0 + except Exception as e: + if self.verbose: + logger.debug(f"Network connectivity test failed: {e}") + return False + + def _handle_network_error(self, error: Exception) -> bool: + """ + Handle network errors with retry logic. + + Args: + error: The network error that occurred + + Returns: + bool: True if should retry, False if should give up + """ + if not self.network_monitoring: + return False + + self._network_retry_count += 1 + self._last_network_error = error + self._network_connected = False + + # Get custom error message + error_msg = self._get_network_error_message( + error, self._network_retry_count + ) + logger.error(error_msg) + + # Check if we should retry + if self._network_retry_count <= self.max_network_retries: + logger.warning( + f"šŸ”„ Attempting to reconnect in {self.network_retry_delay} seconds..." + ) + logger.info( + f"šŸ“Š Network retry {self._network_retry_count}/{self.max_network_retries}" + ) + + # Wait before retry + time.sleep(self.network_retry_delay) + + # Test connectivity before retry + if self._test_network_connectivity(): + logger.info("āœ… Network connectivity restored!") + self._network_connected = True + self._network_retry_count = ( + 0 # Reset on successful test + ) + return True + else: + logger.warning( + "āŒ Network connectivity test failed, will retry..." + ) + return True + else: + logger.critical( + f"šŸ’€ Maximum network retry attempts ({self.max_network_retries}) exceeded!" + ) + logger.critical( + "šŸš« Giving up on network reconnection. Server will shut down." + ) + return False + + def get_network_status(self) -> Dict[str, Any]: + """ + Get current network status and statistics. + + Returns: + Dict containing network status information + """ + return { + "network_monitoring_enabled": self.network_monitoring, + "network_connected": self._network_connected, + "network_retry_count": self._network_retry_count, + "max_network_retries": self.max_network_retries, + "network_retry_delay": self.network_retry_delay, + "network_timeout": self.network_timeout, + "last_network_error": ( + str(self._last_network_error) + if self._last_network_error + else None + ), + "remaining_network_retries": max( + 0, + self.max_network_retries - self._network_retry_count, + ), + "host": self.host, + "port": self.port, + } + + def reset_network_retry_count(self) -> None: + """ + Reset the network retry counter. + + This can be useful if you want to give the server a fresh + set of network retry attempts. + """ + self._network_retry_count = 0 + self._last_network_error = None + self._network_connected = True + logger.info("Network retry counter reset") + + def enable_persistence(self) -> None: + """ + Enable persistence mode for the server. + + This allows the server to automatically restart when stopped, + up to the maximum number of restart attempts. + """ + self._persistence_enabled = True + logger.info("Persistence mode enabled") + + def disable_persistence(self) -> None: + """ + Disable persistence mode for the server. + + This will allow the server to shut down normally without + automatic restarts. + """ + self._persistence_enabled = False + self._shutdown_requested = True + logger.info( + "Persistence mode disabled - server will shut down on next stop" + ) + + def request_shutdown(self) -> None: + """ + Request a graceful shutdown of the server. + + If persistence is enabled, this will prevent automatic restarts + and allow the server to shut down normally. + """ + self._shutdown_requested = True + logger.info( + "Shutdown requested - server will stop after current operations complete" + ) + + def get_persistence_status(self) -> Dict[str, Any]: + """ + Get the current persistence status and statistics. + + Returns: + Dict containing persistence configuration and status + """ + return { + "persistence_enabled": self._persistence_enabled, + "shutdown_requested": self._shutdown_requested, + "restart_count": self._restart_count, + "max_restart_attempts": self.max_restart_attempts, + "restart_delay": self.restart_delay, + "remaining_restarts": max( + 0, self.max_restart_attempts - self._restart_count + ), + } + + def reset_restart_count(self) -> None: + """ + Reset the restart counter. + + This can be useful if you want to give the server a fresh + set of restart attempts. """ - self.start_server() + self._restart_count = 0 + logger.info("Restart counter reset") def get_server_info(self) -> Dict[str, Any]: """ @@ -2283,6 +2711,8 @@ class AOP: "log_level": self.log_level, "transport": self.transport, "queue_enabled": self.queue_enabled, + "persistence": self.get_persistence_status(), + "network": self.get_network_status(), "tool_details": { tool_name: self.get_agent_info(tool_name) for tool_name in self.agents.keys() diff --git a/swarms/structs/conversation.py b/swarms/structs/conversation.py index 0b0b670f..6cfec43a 100644 --- a/swarms/structs/conversation.py +++ b/swarms/structs/conversation.py @@ -1,6 +1,5 @@ import concurrent.futures import datetime -import inspect import json import os import traceback @@ -95,6 +94,7 @@ class Conversation: export_method: str = "json", dynamic_context_window: bool = True, caching: bool = True, + output_metadata: bool = False, ): # Initialize all attributes first @@ -118,6 +118,7 @@ class Conversation: self.export_method = export_method self.dynamic_context_window = dynamic_context_window self.caching = caching + self.output_metadata = output_metadata if self.name is None: self.name = id @@ -534,7 +535,7 @@ class Conversation: """ return self.return_history_as_string() - def to_dict(self) -> Dict[str, Any]: + def to_dict(self) -> Dict[Any, Any]: """ Converts all attributes of the class into a dictionary, including all __init__ parameters and conversation history. Automatically extracts parameters from __init__ signature. @@ -544,43 +545,7 @@ class Conversation: - metadata: All initialization parameters and their current values - conversation_history: The list of conversation messages """ - # Get all parameters from __init__ signature - init_signature = inspect.signature(self.__class__.__init__) - init_params = [ - param - for param in init_signature.parameters - if param not in ["self", "args", "kwargs"] - ] - - # Build metadata dictionary from init parameters - metadata = {} - for param in init_params: - # Get the current value of the parameter from instance - value = getattr(self, param, None) - # Special handling for certain types - if value is not None: - if isinstance( - value, (str, int, float, bool, list, dict) - ): - metadata[param] = value - elif hasattr(value, "to_dict"): - metadata[param] = value.to_dict() - else: - try: - # Try to convert to string if not directly serializable - metadata[param] = str(value) - except Exception: - # Skip if we can't serialize - continue - - # Add created_at if it exists - if hasattr(self, "created_at"): - metadata["created_at"] = self.created_at - - return { - "metadata": metadata, - "conversation_history": self.conversation_history, - } + return self.conversation_history def save_as_json(self, force: bool = True): """Save the conversation history and metadata to a JSON file. @@ -597,14 +562,11 @@ class Conversation: ) return - # Get the full data including metadata and conversation history - data = self.get_init_params() - # Ensure we have a valid save path if not self.save_filepath: self.save_filepath = os.path.join( self.conversations_dir or os.getcwd(), - f"conversation_{self.name}.json", + f"conversation_{self.id}.json", ) # Create directory if it doesn't exist @@ -614,7 +576,12 @@ class Conversation: # Save with proper formatting with open(self.save_filepath, "w", encoding="utf-8") as f: - json.dump(data, f, indent=4, default=str) + json.dump( + self.conversation_history, + f, + indent=4, + default=str, + ) logger.info(f"Conversation saved to {self.save_filepath}") @@ -624,34 +591,6 @@ class Conversation: ) raise # Re-raise to ensure the error is visible to the caller - def get_init_params(self): - data = { - "metadata": { - "id": self.id, - "name": self.name, - "system_prompt": self.system_prompt, - "time_enabled": self.time_enabled, - "autosave": self.autosave, - "save_filepath": self.save_filepath, - "load_filepath": self.load_filepath, - "context_length": self.context_length, - "rules": self.rules, - "custom_rules_prompt": self.custom_rules_prompt, - "user": self.user, - "save_as_yaml_on": self.save_as_yaml_on, - "save_as_json_bool": self.save_as_json_bool, - "token_count": self.token_count, - "message_id_on": self.message_id_on, - "tokenizer_model_name": self.tokenizer_model_name, - "conversations_dir": self.conversations_dir, - "export_method": self.export_method, - "created_at": self.created_at, - }, - "conversation_history": self.conversation_history, - } - - return data - def save_as_yaml(self, force: bool = True): """Save the conversation history and metadata to a YAML file. @@ -667,9 +606,6 @@ class Conversation: ) return - # Get the full data including metadata and conversation history - data = self.get_init_params() - # Create directory if it doesn't exist save_dir = os.path.dirname(self.save_filepath) if save_dir: @@ -678,7 +614,7 @@ class Conversation: # Save with proper formatting with open(self.save_filepath, "w", encoding="utf-8") as f: yaml.dump( - data, + self.conversation_history, f, indent=4, default_flow_style=False, diff --git a/swarms/structs/cron_job.py b/swarms/structs/cron_job.py index 0e858a11..eff7472e 100644 --- a/swarms/structs/cron_job.py +++ b/swarms/structs/cron_job.py @@ -7,9 +7,6 @@ import schedule from loguru import logger -# from swarms import Agent - - class CronJobError(Exception): """Base exception class for CronJob errors.""" diff --git a/swarms/structs/qa_swarm.py b/swarms/structs/qa_swarm.py deleted file mode 100644 index 51390455..00000000 --- a/swarms/structs/qa_swarm.py +++ /dev/null @@ -1,253 +0,0 @@ -from swarms.structs.agent import Agent -from typing import List -from swarms.structs.conversation import Conversation -import uuid -import random -from loguru import logger -from typing import Optional - - -class QASwarm: - """ - A Question and Answer swarm system where random agents ask questions to speaker agents. - - This system allows for dynamic Q&A sessions where: - - Multiple agents can act as questioners - - One or multiple agents can act as speakers/responders - - Questions are asked randomly by different agents - - The conversation is tracked and managed - - Agents are showcased to each other with detailed information - """ - - def __init__( - self, - name: str = "QandA", - description: str = "Question and Answer Swarm System", - agents: List[Agent] = None, - speaker_agents: List[Agent] = None, - id: str = str(uuid.uuid4()), - max_loops: int = 5, - show_dashboard: bool = True, - speaker_agent: Agent = None, - showcase_agents: bool = True, - **kwargs, - ): - self.id = id - self.name = name - self.description = description - self.max_loops = max_loops - self.show_dashboard = show_dashboard - self.agents = agents or [] - self.speaker_agents = speaker_agents or [] - self.kwargs = kwargs - self.speaker_agent = speaker_agent - self.showcase_agents = showcase_agents - - self.conversation = Conversation() - - # Validate setup - self._validate_setup() - - def _validate_setup(self): - """Validate that the Q&A system is properly configured.""" - if not self.agents: - logger.warning( - "No questioner agents provided. Add agents using add_agent() method." - ) - - if not self.speaker_agents and not self.speaker_agent: - logger.warning( - "No speaker agents provided. Add speaker agents using add_speaker_agent() method." - ) - - if ( - not self.agents - and not self.speaker_agents - and not self.speaker_agent - ): - raise ValueError( - "At least one agent (questioner or speaker) must be provided." - ) - - def add_agent(self, agent: Agent): - """Add a questioner agent to the swarm.""" - self.agents.append(agent) - logger.info(f"Added questioner agent: {agent.agent_name}") - - def add_speaker_agent(self, agent: Agent): - """Add a speaker agent to the swarm.""" - if self.speaker_agents is None: - self.speaker_agents = [] - self.speaker_agents.append(agent) - logger.info(f"Added speaker agent: {agent.agent_name}") - - def get_agent_info(self, agent: Agent) -> dict: - """Extract key information about an agent for showcasing.""" - info = { - "name": getattr(agent, "agent_name", "Unknown Agent"), - "description": getattr( - agent, "agent_description", "No description available" - ), - "role": getattr(agent, "role", "worker"), - } - - # Get system prompt preview (first 50 characters) - system_prompt = getattr(agent, "system_prompt", "") - if system_prompt: - info["system_prompt_preview"] = ( - system_prompt[:50] + "..." - if len(system_prompt) > 50 - else system_prompt - ) - else: - info["system_prompt_preview"] = ( - "No system prompt available" - ) - - return info - - def showcase_speaker_to_questioner( - self, questioner: Agent, speaker: Agent - ) -> str: - """Create a showcase prompt introducing the speaker agent to the questioner.""" - speaker_info = self.get_agent_info(speaker) - - showcase_prompt = f""" - You are about to ask a question to a specialized agent. Here's what you need to know about them: - - **Speaker Agent Information:** - - **Name**: {speaker_info['name']} - - **Role**: {speaker_info['role']} - - **Description**: {speaker_info['description']} - - **System Prompt Preview**: {speaker_info['system_prompt_preview']} - - Please craft a thoughtful, relevant question that takes into account this agent's expertise and background. - Your question should be specific and demonstrate that you understand their role and capabilities. - """ - return showcase_prompt - - def showcase_questioner_to_speaker( - self, speaker: Agent, questioner: Agent - ) -> str: - """Create a showcase prompt introducing the questioner agent to the speaker.""" - questioner_info = self.get_agent_info(questioner) - - showcase_prompt = f""" -You are about to answer a question from another agent. Here's what you need to know about them: - -**Questioner Agent Information:** -- **Name**: {questioner_info['name']} -- **Role**: {questioner_info['role']} -- **Description**: {questioner_info['description']} -- **System Prompt Preview**: {questioner_info['system_prompt_preview']} - -Please provide a comprehensive answer that demonstrates your expertise and addresses their question thoroughly. -Consider their background and role when formulating your response. -""" - return showcase_prompt - - def random_select_agent(self, agents: List[Agent]) -> Agent: - """Randomly select an agent from the list.""" - if not agents: - raise ValueError("No agents available for selection") - return random.choice(agents) - - def get_current_speaker(self) -> Agent: - """Get the current speaker agent (either from speaker_agents list or single speaker_agent).""" - if self.speaker_agent: - return self.speaker_agent - elif self.speaker_agents: - return self.random_select_agent(self.speaker_agents) - else: - raise ValueError("No speaker agent available") - - def run( - self, task: str, img: Optional[str] = None, *args, **kwargs - ): - """Run the Q&A session with agent showcasing.""" - self.conversation.add(role="user", content=task) - - # Get current speaker - current_speaker = self.get_current_speaker() - - # Select a random questioner - questioner = self.random_select_agent(self.agents) - - # Showcase agents to each other if enabled - if self.showcase_agents: - # Showcase speaker to questioner - speaker_showcase = self.showcase_speaker_to_questioner( - questioner, current_speaker - ) - questioner_task = f"{speaker_showcase}\n\nNow ask a question about: {task}" - - # Showcase questioner to speaker - questioner_showcase = self.showcase_questioner_to_speaker( - current_speaker, questioner - ) - else: - questioner_task = f"Ask a question about {task} to {current_speaker.agent_name}" - - # Generate question - question = questioner.run( - task=questioner_task, - img=img, - *args, - **kwargs, - ) - - self.conversation.add( - role=questioner.agent_name, content=question - ) - - # Prepare answer task with showcasing if enabled - if self.showcase_agents: - answer_task = f"{questioner_showcase}\n\nAnswer this question from {questioner.agent_name}: {question}" - else: - answer_task = f"Answer the question '{question}' from {questioner.agent_name}" - - # Generate answer - answer = current_speaker.run( - task=answer_task, - img=img, - *args, - **kwargs, - ) - - self.conversation.add( - role=current_speaker.agent_name, content=answer - ) - - return answer - - def run_multi_round( - self, - task: str, - rounds: int = 3, - img: Optional[str] = None, - *args, - **kwargs, - ): - """Run multiple rounds of Q&A with different questioners.""" - results = [] - - for round_num in range(rounds): - logger.info( - f"Starting Q&A round {round_num + 1}/{rounds}" - ) - - round_result = self.run(task, img, *args, **kwargs) - results.append( - {"round": round_num + 1, "result": round_result} - ) - - return results - - def get_conversation_history(self): - """Get the conversation history.""" - return self.conversation.get_history() - - def clear_conversation(self): - """Clear the conversation history.""" - self.conversation = Conversation() - logger.info("Conversation history cleared") diff --git a/swarms/structs/social_algorithms.py b/swarms/structs/social_algorithms.py index aeee7a69..c432419a 100644 --- a/swarms/structs/social_algorithms.py +++ b/swarms/structs/social_algorithms.py @@ -1,15 +1,7 @@ -""" -Social Algorithms for Multi-Agent Communication - -This module provides a flexible framework for defining custom social algorithms -that control how agents communicate and interact with each other in multi-agent systems. -""" - import time import uuid from typing import Any, Callable, Dict, List, Optional from dataclasses import dataclass -from enum import Enum from swarms.structs.agent import Agent from swarms.structs.omni_agent_types import AgentType @@ -19,18 +11,6 @@ from swarms.utils.output_types import OutputType logger = initialize_logger(log_folder="social_algorithms") -class SocialAlgorithmType(Enum): - """Types of social algorithms supported.""" - - CUSTOM = "custom" - SEQUENTIAL = "sequential" - CONCURRENT = "concurrent" - HIERARCHICAL = "hierarchical" - MESH = "mesh" - ROUND_ROBIN = "round_robin" - BROADCAST = "broadcast" - - @dataclass class CommunicationStep: """Represents a single step in a social algorithm.""" diff --git a/swarms/structs/swarm_registry.py b/swarms/structs/swarm_registry.py deleted file mode 100644 index a4db3cb4..00000000 --- a/swarms/structs/swarm_registry.py +++ /dev/null @@ -1,191 +0,0 @@ -from pydantic.v1 import BaseModel -from typing import List, Callable -from swarms.utils.loguru_logger import initialize_logger - -logger = initialize_logger(log_folder="swarm_registry") - - -class SwarmRegistry(BaseModel): - swarm_pool: List[Callable] = [] - - def add(self, swarm: Callable, *args, **kwargs): - """ - Adds a swarm to the registry. - - Args: - swarm (Callable): The swarm to add to the registry. - """ - self.swarm_pool.append(swarm, *args, **kwargs) - - def query(self, swarm_name: str) -> Callable: - """ - Queries the registry for a swarm by name. - - Args: - swarm_name (str): The name of the swarm to query. - - Returns: - Callable: The swarm function corresponding to the given name. - """ - if not self.swarm_pool: - raise ValueError("No swarms found in registry") - - if not swarm_name: - raise ValueError("No swarm name provided.") - - for swarm in self.swarm_pool: - if swarm.__name__ == swarm_name: - name = swarm.__name__ - description = ( - swarm.__doc__.strip().split("\n")[0] - or swarm.description - ) - agent_count = len(swarm.agents) - task_count = len(swarm.tasks) - - log = f"Swarm: {name}\nDescription: {description}\nAgents: {agent_count}\nTasks: {task_count}" - logger.info(log) - - return swarm - - raise ValueError( - f"Swarm '{swarm_name}' not found in registry." - ) - - def remove(self, swarm_name: str): - """ - Removes a swarm from the registry by name. - - Args: - swarm_name (str): The name of the swarm to remove. - """ - for swarm in self.swarm_pool: - if swarm.__name__ == swarm_name: - self.swarm_pool.remove(swarm) - return - raise ValueError( - f"Swarm '{swarm_name}' not found in registry." - ) - - def list_swarms(self) -> List[str]: - """ - Lists the names of all swarms in the registry. - - Returns: - List[str]: A list of swarm names. - """ - if not self.swarm_pool: - raise ValueError("No swarms found in registry.") - - for swarm in self.swarm_pool: - name = swarm.__name__ - description = ( - swarm.__doc__.strip().split("\n")[0] - or swarm.description - ) - agent_count = len(swarm.agents) - task_count = len(swarm.tasks) - - log = f"Swarm: {name}\nDescription: {description}\nAgents: {agent_count}\nTasks: {task_count}" - logger.info(log) - - return [swarm.__name__ for swarm in self.swarm_pool] - - def run(self, swarm_name: str, *args, **kwargs): - """ - Runs a swarm by name with the given arguments. - - Args: - swarm_name (str): The name of the swarm to run. - *args: Variable length argument list. - **kwargs: Arbitrary keyword arguments. - - Returns: - Any: The result of running the swarm. - """ - swarm = self.query(swarm_name) - return swarm(*args, **kwargs) - - def add_list_of_swarms(self, swarms: List[Callable]): - """ - Adds a list of swarms to the registry. - - Args: - swarms (List[Callable]): A list of swarms to add to the registry. - """ - for swarm in swarms: - self.add(swarm) - - return self.swarm_pool - - def query_multiple_of_swarms( - self, swarm_names: List[str] - ) -> List[Callable]: - """ - Queries the registry for multiple swarms by name. - - Args: - swarm_names (List[str]): A list of swarm names to query. - - Returns: - List[Callable]: A list of swarm functions corresponding to the given names. - """ - return [self.query(swarm_name) for swarm_name in swarm_names] - - def remove_list_of_swarms(self, swarm_names: List[str]): - """ - Removes a list of swarms from the registry by name. - - Args: - swarm_names (List[str]): A list of swarm names to remove. - """ - for swarm_name in swarm_names: - self.remove(swarm_name) - - return self.swarm_pool - - def run_multiple_of_swarms( - self, swarm_names: List[str], *args, **kwargs - ): - """ - Runs a list of swarms by name with the given arguments. - - Args: - swarm_names (List[str]): A list of swarm names to run. - *args: Variable length argument list. - **kwargs: Arbitrary keyword arguments. - - Returns: - List[Any]: A list of results of running the swarms. - """ - return [ - self.run(swarm_name, *args, **kwargs) - for swarm_name in swarm_names - ] - - -# Decorator to add a function to the registry -def swarm_registry(): - """ - Decorator to add a function to the registry. - - Args: - swarm_registry (SwarmRegistry): The swarm registry instance. - - Returns: - Callable: The decorated function. - """ - - def decorator(func, *args, **kwargs): - try: - swarm_registry = SwarmRegistry() - swarm_registry.add(func, *args, **kwargs) - logger.info( - f"Added swarm '{func.__name__}' to the registry." - ) - return func - except Exception as e: - logger.error(str(e)) - raise - - return decorator diff --git a/swarms/structs/swarm_router.py b/swarms/structs/swarm_router.py index fd088038..84256d8f 100644 --- a/swarms/structs/swarm_router.py +++ b/swarms/structs/swarm_router.py @@ -546,8 +546,6 @@ class SwarmRouter: description=self.description, agents=self.agents, max_loops=self.max_loops, - auto_save=self.autosave, - return_str_on=self.return_entire_history, output_type=self.output_type, *args, **kwargs, diff --git a/swarms/structs/tree_swarm.py b/swarms/structs/tree_swarm.py index c1b21eb1..7607fe0b 100644 --- a/swarms/structs/tree_swarm.py +++ b/swarms/structs/tree_swarm.py @@ -1,9 +1,8 @@ import uuid from collections import Counter -from datetime import datetime +from datetime import datetime, timezone from typing import Any, List, Optional -import numpy as np from litellm import embedding from pydantic import BaseModel, Field @@ -14,6 +13,47 @@ from swarms.utils.loguru_logger import initialize_logger logger = initialize_logger(log_folder="tree_swarm") +def extract_keywords(prompt: str, top_n: int = 5) -> List[str]: + """ + A simplified keyword extraction function using basic word splitting instead of NLTK tokenization. + + Args: + prompt (str): The text prompt to extract keywords from + top_n (int): Maximum number of keywords to return + + Returns: + List[str]: List of extracted keywords + """ + words = prompt.lower().split() + filtered_words = [word for word in words if word.isalnum()] + word_counts = Counter(filtered_words) + return [word for word, _ in word_counts.most_common(top_n)] + + +def cosine_similarity(vec1: List[float], vec2: List[float]) -> float: + """ + Calculate cosine similarity between two vectors. + + Args: + vec1 (List[float]): First vector + vec2 (List[float]): Second vector + + Returns: + float: Cosine similarity score between 0 and 1 + """ + # Calculate dot product + dot_product = sum(a * b for a, b in zip(vec1, vec2)) + + # Calculate norms + norm1 = sum(a * a for a in vec1) ** 0.5 + norm2 = sum(b * b for b in vec2) ** 0.5 + + if norm1 == 0 or norm2 == 0: + return 0.0 + + return dot_product / (norm1 * norm2) + + # Pydantic Models for Logging class AgentLogInput(BaseModel): """ @@ -32,7 +72,7 @@ class AgentLogInput(BaseModel): agent_name: str task: str timestamp: datetime = Field( - default_factory=lambda: datetime.now(datetime.UTC) + default_factory=lambda: datetime.now(timezone.utc) ) @@ -53,7 +93,7 @@ class AgentLogOutput(BaseModel): agent_name: str result: Any timestamp: datetime = Field( - default_factory=lambda: datetime.now(datetime.UTC) + default_factory=lambda: datetime.now(timezone.utc) ) @@ -77,52 +117,11 @@ class TreeLog(BaseModel): task: str selected_agent: str timestamp: datetime = Field( - default_factory=lambda: datetime.now(datetime.UTC) + default_factory=lambda: datetime.now(timezone.utc) ) result: Any -def extract_keywords(prompt: str, top_n: int = 5) -> List[str]: - """ - A simplified keyword extraction function using basic word splitting instead of NLTK tokenization. - - Args: - prompt (str): The text prompt to extract keywords from - top_n (int): Maximum number of keywords to return - - Returns: - List[str]: List of extracted keywords - """ - words = prompt.lower().split() - filtered_words = [word for word in words if word.isalnum()] - word_counts = Counter(filtered_words) - return [word for word, _ in word_counts.most_common(top_n)] - - -def cosine_similarity(vec1: List[float], vec2: List[float]) -> float: - """ - Calculate cosine similarity between two vectors. - - Args: - vec1 (List[float]): First vector - vec2 (List[float]): Second vector - - Returns: - float: Cosine similarity score between 0 and 1 - """ - vec1 = np.array(vec1) - vec2 = np.array(vec2) - - dot_product = np.dot(vec1, vec2) - norm1 = np.linalg.norm(vec1) - norm2 = np.linalg.norm(vec2) - - if norm1 == 0 or norm2 == 0: - return 0.0 - - return dot_product / (norm1 * norm2) - - class TreeAgent(Agent): """ A specialized Agent class that contains information about the system prompt's @@ -137,6 +136,7 @@ class TreeAgent(Agent): model_name: str = "gpt-4.1", agent_name: Optional[str] = None, embedding_model_name: str = "text-embedding-ada-002", + verbose: bool = False, *args, **kwargs, ): @@ -150,6 +150,7 @@ class TreeAgent(Agent): model_name (str): Name of the language model to use agent_name (Optional[str]): Alternative name for the agent embedding_model_name (str): Name of the embedding model to use + verbose (bool): Whether to enable verbose logging *args: Additional positional arguments **kwargs: Additional keyword arguments """ @@ -164,6 +165,7 @@ class TreeAgent(Agent): **kwargs, ) self.embedding_model_name = embedding_model_name + self.verbose = verbose # Generate system prompt embedding using litellm if system_prompt: @@ -195,7 +197,8 @@ class TreeAgent(Agent): response = embedding( model=self.embedding_model_name, input=[text] ) - logger.info(f"Embedding type: {type(response)}") + if self.verbose: + logger.info(f"Embedding type: {type(response)}") # print(response) # Handle different response structures from litellm if hasattr(response, "data") and response.data: @@ -207,17 +210,20 @@ class TreeAgent(Agent): ): return response.data[0]["embedding"] else: - logger.error( - f"Unexpected response structure: {response.data[0]}" - ) + if self.verbose: + logger.error( + f"Unexpected response structure: {response.data[0]}" + ) return [0.0] * 1536 else: - logger.error( - f"Unexpected response structure: {response}" - ) + if self.verbose: + logger.error( + f"Unexpected response structure: {response}" + ) return [0.0] * 1536 except Exception as e: - logger.error(f"Error getting embedding: {e}") + if self.verbose: + logger.error(f"Error getting embedding: {e}") # Return a zero vector as fallback return [0.0] * 1536 # Default OpenAI embedding dimension @@ -264,20 +270,24 @@ class TreeAgent(Agent): input_log = AgentLogInput( agent_name=self.agent_name, task=task, - timestamp=datetime.now(), + timestamp=datetime.now(timezone.utc), ) - logger.info(f"Running task on {self.agent_name}: {task}") - logger.debug(f"Input Log: {input_log.json()}") + if self.verbose: + logger.info(f"Running task on {self.agent_name}: {task}") + logger.debug(f"Input Log: {input_log.json()}") result = self.run(task=task, img=img, *args, **kwargs) output_log = AgentLogOutput( agent_name=self.agent_name, result=result, - timestamp=datetime.now(), + timestamp=datetime.now(timezone.utc), ) - logger.info(f"Task result from {self.agent_name}: {result}") - logger.debug(f"Output Log: {output_log.json()}") + if self.verbose: + logger.info( + f"Task result from {self.agent_name}: {result}" + ) + logger.debug(f"Output Log: {output_log.json()}") return result @@ -306,25 +316,36 @@ class TreeAgent(Agent): similarity = cosine_similarity( self.system_prompt_embedding, task_embedding ) - logger.info( - f"Semantic similarity between task and {self.agent_name}: {similarity:.2f}" - ) + if self.verbose: + logger.info( + f"Semantic similarity between task and {self.agent_name}: {similarity:.2f}" + ) return similarity >= threshold return True # Return True if keyword match is found class Tree: - def __init__(self, tree_name: str, agents: List[TreeAgent]): + def __init__( + self, + tree_name: str, + agents: List[TreeAgent], + verbose: bool = False, + ): """ Initializes a tree of agents. Args: tree_name (str): The name of the tree. agents (List[TreeAgent]): A list of agents in the tree. + verbose (bool): Whether to enable verbose logging """ self.tree_name = tree_name self.agents = agents + self.verbose = verbose + # Pass verbose to all agents + for agent in self.agents: + agent.verbose = verbose self.calculate_agent_distances() def calculate_agent_distances(self): @@ -334,9 +355,10 @@ class Tree: This method computes the semantic distance between consecutive agents using their system prompt embeddings and sorts the agents by distance for optimal task routing. """ - logger.info( - f"Calculating distances between agents in tree '{self.tree_name}'" - ) + if self.verbose: + logger.info( + f"Calculating distances between agents in tree '{self.tree_name}'" + ) for i, agent in enumerate(self.agents): if i > 0: agent.distance = agent.calculate_distance( @@ -359,15 +381,17 @@ class Tree: Returns: Optional[TreeAgent]: The most relevant agent, or None if no match found. """ - logger.info( - f"Searching relevant agent in tree '{self.tree_name}' for task: {task}" - ) + if self.verbose: + logger.info( + f"Searching relevant agent in tree '{self.tree_name}' for task: {task}" + ) for agent in self.agents: if agent.is_relevant_for_task(task): return agent - logger.warning( - f"No relevant agent found in tree '{self.tree_name}' for task: {task}" - ) + if self.verbose: + logger.warning( + f"No relevant agent found in tree '{self.tree_name}' for task: {task}" + ) return None def log_tree_execution( @@ -380,13 +404,14 @@ class Tree: tree_name=self.tree_name, task=task, selected_agent=selected_agent.agent_name, - timestamp=datetime.now(), + timestamp=datetime.now(timezone.utc), result=result, ) - logger.info( - f"Tree '{self.tree_name}' executed task with agent '{selected_agent.agent_name}'" - ) - logger.debug(f"Tree Log: {tree_log.json()}") + if self.verbose: + logger.info( + f"Tree '{self.tree_name}' executed task with agent '{selected_agent.agent_name}'" + ) + logger.debug(f"Tree Log: {tree_log.json()}") class ForestSwarm: @@ -397,6 +422,7 @@ class ForestSwarm: trees: List[Tree] = [], shared_memory: Any = None, rules: str = None, + verbose: bool = False, *args, **kwargs, ): @@ -409,6 +435,7 @@ class ForestSwarm: trees (List[Tree]): A list of trees in the structure shared_memory (Any): Shared memory object for inter-tree communication rules (str): Rules governing the forest swarm behavior + verbose (bool): Whether to enable verbose logging *args: Additional positional arguments **kwargs: Additional keyword arguments """ @@ -416,10 +443,13 @@ class ForestSwarm: self.description = description self.trees = trees self.shared_memory = shared_memory + self.verbose = verbose + # Pass verbose to all trees + for tree in self.trees: + tree.verbose = verbose self.save_file_path = f"forest_swarm_{uuid.uuid4().hex}.json" self.conversation = Conversation( time_enabled=False, - auto_save=True, save_filepath=self.save_file_path, rules=rules, ) @@ -434,13 +464,15 @@ class ForestSwarm: Returns: Optional[Tree]: The most relevant tree, or None if no match found """ - logger.info( - f"Searching for the most relevant tree for task: {task}" - ) + if self.verbose: + logger.info( + f"Searching for the most relevant tree for task: {task}" + ) for tree in self.trees: if tree.find_relevant_agent(task): return tree - logger.warning(f"No relevant tree found for task: {task}") + if self.verbose: + logger.warning(f"No relevant tree found for task: {task}") return None def run(self, task: str, img: str = None, *args, **kwargs) -> Any: @@ -457,9 +489,10 @@ class ForestSwarm: Any: The result of the task after it has been processed by the agents """ try: - logger.info( - f"Running task across MultiAgentTreeStructure: {task}" - ) + if self.verbose: + logger.info( + f"Running task across MultiAgentTreeStructure: {task}" + ) relevant_tree = self.find_relevant_tree(task) if relevant_tree: agent = relevant_tree.find_relevant_agent(task) @@ -472,14 +505,32 @@ class ForestSwarm: ) return result else: - logger.error( - "Task could not be completed: No relevant agent or tree found." - ) + if self.verbose: + logger.error( + "Task could not be completed: No relevant agent or tree found." + ) return "No relevant agent found to handle this task." except Exception as error: - logger.error( - f"Error detected in the ForestSwarm, check your inputs and try again ;) {error}" - ) + if self.verbose: + logger.error( + f"Error detected in the ForestSwarm, check your inputs and try again ;) {error}" + ) + + def batched_run( + self, + tasks: List[str], + *args, + **kwargs, + ) -> List[Any]: + """ + Execute the given tasks by finding the most relevant tree and agent within that tree. + + Args: + tasks: List[str]: The tasks to be executed + *args: Additional positional arguments + **kwargs: Additional keyword arguments + """ + return [self.run(task, *args, **kwargs) for task in tasks] # # Example Usage: diff --git a/swarms/tools/__init__.py b/swarms/tools/__init__.py index 0df3101c..d8850520 100644 --- a/swarms/tools/__init__.py +++ b/swarms/tools/__init__.py @@ -1,8 +1,4 @@ from swarms.tools.base_tool import BaseTool -from swarms.tools.cohere_func_call_schema import ( - CohereFuncSchema, - ParameterDefinition, -) from swarms.tools.json_utils import base_model_to_json from swarms.tools.mcp_client_tools import ( _create_server_tool_mapping, @@ -56,8 +52,6 @@ __all__ = [ "ToolFunction", "tool", "BaseTool", - "CohereFuncSchema", - "ParameterDefinition", "ToolStorage", "tool_registry", "base_model_to_json", diff --git a/swarms/tools/cohere_func_call_schema.py b/swarms/tools/cohere_func_call_schema.py deleted file mode 100644 index e0dbaa37..00000000 --- a/swarms/tools/cohere_func_call_schema.py +++ /dev/null @@ -1,18 +0,0 @@ -from pydantic import BaseModel, Field -from typing import Dict - - -class ParameterDefinition(BaseModel): - description: str = Field( - ..., title="Description of the parameter" - ) - type: str = Field(..., title="Type of the parameter") - required: bool = Field(..., title="Is the parameter required?") - - -class CohereFuncSchema(BaseModel): - name: str = Field(..., title="Name of the tool") - description: str = Field(..., title="Description of the tool") - parameter_definitions: Dict[str, ParameterDefinition] = Field( - ..., title="Parameter definitions for the tool" - ) diff --git a/swarms/tools/mcp_client_tools.py b/swarms/tools/mcp_client_tools.py index 77886f4e..c59f6332 100644 --- a/swarms/tools/mcp_client_tools.py +++ b/swarms/tools/mcp_client_tools.py @@ -64,6 +64,7 @@ class MCPExecutionError(MCPError): ######################################################## def transform_mcp_tool_to_openai_tool( mcp_tool: MCPTool, + verbose: bool = False, ) -> ChatCompletionToolParam: """ Convert an MCP tool to an OpenAI tool. @@ -72,9 +73,11 @@ def transform_mcp_tool_to_openai_tool( Returns: ChatCompletionToolParam: The OpenAI-compatible tool parameter. """ - logger.info( - f"Transforming MCP tool '{mcp_tool.name}' to OpenAI tool format." - ) + if verbose: + logger.info( + f"Transforming MCP tool '{mcp_tool.name}' to OpenAI tool format." + ) + return ChatCompletionToolParam( type="function", function=FunctionDefinition( @@ -529,12 +532,15 @@ def get_tools_for_multiple_mcp_servers( logger.info( f"get_tools_for_multiple_mcp_servers called for {len(urls)} urls." ) + tools = [] + ( min(32, os.cpu_count() + 4) if max_workers is None else max_workers ) + with ThreadPoolExecutor(max_workers=max_workers) as executor: if exists(connections): future_to_url = { diff --git a/swarms/tools/tool_parse_exec.py b/swarms/tools/tool_parse_exec.py index f09b7d7d..145a7658 100644 --- a/swarms/tools/tool_parse_exec.py +++ b/swarms/tools/tool_parse_exec.py @@ -1,9 +1,9 @@ import json -from typing import List, Any, Callable import re +from typing import Any, Callable, List -from swarms.utils.parse_code import extract_code_from_markdown from swarms.utils.loguru_logger import initialize_logger +from swarms.utils.parse_code import extract_code_from_markdown logger = initialize_logger(log_folder="tool_parse_exec") diff --git a/swarms/utils/audio_processing.py b/swarms/utils/audio_processing.py deleted file mode 100644 index 1f746923..00000000 --- a/swarms/utils/audio_processing.py +++ /dev/null @@ -1,343 +0,0 @@ -import base64 -from typing import Union, Dict, Any, Tuple -import requests -from pathlib import Path -import wave -import numpy as np - - -def encode_audio_to_base64(audio_path: Union[str, Path]) -> str: - """ - Encode a WAV file to base64 string. - - Args: - audio_path (Union[str, Path]): Path to the WAV file - - Returns: - str: Base64 encoded string of the audio file - - Raises: - FileNotFoundError: If the audio file doesn't exist - ValueError: If the file is not a valid WAV file - """ - try: - audio_path = Path(audio_path) - if not audio_path.exists(): - raise FileNotFoundError( - f"Audio file not found: {audio_path}" - ) - - if not audio_path.suffix.lower() == ".wav": - raise ValueError("File must be a WAV file") - - with open(audio_path, "rb") as audio_file: - audio_data = audio_file.read() - return base64.b64encode(audio_data).decode("utf-8") - except Exception as e: - raise Exception(f"Error encoding audio file: {str(e)}") - - -def decode_base64_to_audio( - base64_string: str, output_path: Union[str, Path] -) -> None: - """ - Decode a base64 string to a WAV file. - - Args: - base64_string (str): Base64 encoded audio data - output_path (Union[str, Path]): Path where the WAV file should be saved - - Raises: - ValueError: If the base64 string is invalid - IOError: If there's an error writing the file - """ - try: - output_path = Path(output_path) - output_path.parent.mkdir(parents=True, exist_ok=True) - - audio_data = base64.b64decode(base64_string) - with open(output_path, "wb") as audio_file: - audio_file.write(audio_data) - except Exception as e: - raise Exception(f"Error decoding audio data: {str(e)}") - - -def download_audio_from_url( - url: str, output_path: Union[str, Path] -) -> None: - """ - Download an audio file from a URL and save it locally. - - Args: - url (str): URL of the audio file - output_path (Union[str, Path]): Path where the audio file should be saved - - Raises: - requests.RequestException: If there's an error downloading the file - IOError: If there's an error saving the file - """ - try: - output_path = Path(output_path) - output_path.parent.mkdir(parents=True, exist_ok=True) - - response = requests.get(url) - response.raise_for_status() - - with open(output_path, "wb") as audio_file: - audio_file.write(response.content) - except Exception as e: - raise Exception(f"Error downloading audio file: {str(e)}") - - -def process_audio_with_model( - audio_path: Union[str, Path], - model: str, - prompt: str, - voice: str = "alloy", - format: str = "wav", -) -> Dict[str, Any]: - """ - Process an audio file with a model that supports audio input/output. - - Args: - audio_path (Union[str, Path]): Path to the input WAV file - model (str): Model name to use for processing - prompt (str): Text prompt to accompany the audio - voice (str, optional): Voice to use for audio output. Defaults to "alloy" - format (str, optional): Audio format. Defaults to "wav" - - Returns: - Dict[str, Any]: Model response containing both text and audio if applicable - - Raises: - ImportError: If litellm is not installed - ValueError: If the model doesn't support audio processing - """ - try: - from litellm import ( - completion, - supports_audio_input, - supports_audio_output, - ) - - if not supports_audio_input(model): - raise ValueError( - f"Model {model} does not support audio input" - ) - - # Encode the audio file - encoded_audio = encode_audio_to_base64(audio_path) - - # Prepare the messages - messages = [ - { - "role": "user", - "content": [ - {"type": "text", "text": prompt}, - { - "type": "input_audio", - "input_audio": { - "data": encoded_audio, - "format": format, - }, - }, - ], - } - ] - - # Make the API call - response = completion( - model=model, - modalities=["text", "audio"], - audio={"voice": voice, "format": format}, - messages=messages, - ) - - return response - except ImportError: - raise ImportError( - "Please install litellm: pip install litellm" - ) - except Exception as e: - raise Exception( - f"Error processing audio with model: {str(e)}" - ) - - -def read_wav_file( - file_path: Union[str, Path], -) -> Tuple[np.ndarray, int]: - """ - Read a WAV file and return its audio data and sample rate. - - Args: - file_path (Union[str, Path]): Path to the WAV file - - Returns: - Tuple[np.ndarray, int]: Audio data as numpy array and sample rate - - Raises: - FileNotFoundError: If the file doesn't exist - ValueError: If the file is not a valid WAV file - """ - try: - file_path = Path(file_path) - if not file_path.exists(): - raise FileNotFoundError( - f"Audio file not found: {file_path}" - ) - - with wave.open(str(file_path), "rb") as wav_file: - # Get audio parameters - n_channels = wav_file.getnchannels() - sample_width = wav_file.getsampwidth() - frame_rate = wav_file.getframerate() - n_frames = wav_file.getnframes() - - # Read audio data - frames = wav_file.readframes(n_frames) - - # Convert to numpy array - dtype = np.int16 if sample_width == 2 else np.int8 - audio_data = np.frombuffer(frames, dtype=dtype) - - # Reshape if stereo - if n_channels == 2: - audio_data = audio_data.reshape(-1, 2) - - return audio_data, frame_rate - - except Exception as e: - raise Exception(f"Error reading WAV file: {str(e)}") - - -def write_wav_file( - audio_data: np.ndarray, - file_path: Union[str, Path], - sample_rate: int, - sample_width: int = 2, -) -> None: - """ - Write audio data to a WAV file. - - Args: - audio_data (np.ndarray): Audio data as numpy array - file_path (Union[str, Path]): Path where to save the WAV file - sample_rate (int): Sample rate of the audio - sample_width (int, optional): Sample width in bytes. Defaults to 2 (16-bit) - - Raises: - ValueError: If the audio data is invalid - IOError: If there's an error writing the file - """ - try: - file_path = Path(file_path) - file_path.parent.mkdir(parents=True, exist_ok=True) - - # Ensure audio data is in the correct format - if audio_data.dtype != np.int16 and sample_width == 2: - audio_data = (audio_data * 32767).astype(np.int16) - elif audio_data.dtype != np.int8 and sample_width == 1: - audio_data = (audio_data * 127).astype(np.int8) - - # Determine number of channels - n_channels = ( - 2 - if len(audio_data.shape) > 1 and audio_data.shape[1] == 2 - else 1 - ) - - with wave.open(str(file_path), "wb") as wav_file: - wav_file.setnchannels(n_channels) - wav_file.setsampwidth(sample_width) - wav_file.setframerate(sample_rate) - wav_file.writeframes(audio_data.tobytes()) - - except Exception as e: - raise Exception(f"Error writing WAV file: {str(e)}") - - -def normalize_audio(audio_data: np.ndarray) -> np.ndarray: - """ - Normalize audio data to have maximum amplitude of 1.0. - - Args: - audio_data (np.ndarray): Input audio data - - Returns: - np.ndarray: Normalized audio data - """ - return audio_data / np.max(np.abs(audio_data)) - - -def convert_to_mono(audio_data: np.ndarray) -> np.ndarray: - """ - Convert stereo audio to mono by averaging channels. - - Args: - audio_data (np.ndarray): Input audio data (stereo) - - Returns: - np.ndarray: Mono audio data - """ - if len(audio_data.shape) == 1: - return audio_data - return np.mean(audio_data, axis=1) - - -def encode_wav_to_base64( - audio_data: np.ndarray, sample_rate: int -) -> str: - """ - Convert audio data to base64 encoded WAV string. - - Args: - audio_data (np.ndarray): Audio data - sample_rate (int): Sample rate of the audio - - Returns: - str: Base64 encoded WAV data - """ - # Create a temporary WAV file in memory - with wave.open("temp.wav", "wb") as wav_file: - wav_file.setnchannels(1 if len(audio_data.shape) == 1 else 2) - wav_file.setsampwidth(2) # 16-bit - wav_file.setframerate(sample_rate) - wav_file.writeframes(audio_data.tobytes()) - - # Read the file and encode to base64 - with open("temp.wav", "rb") as f: - wav_bytes = f.read() - - # Clean up temporary file - Path("temp.wav").unlink() - - return base64.b64encode(wav_bytes).decode("utf-8") - - -def decode_base64_to_wav( - base64_string: str, -) -> Tuple[np.ndarray, int]: - """ - Convert base64 encoded WAV string to audio data and sample rate. - - Args: - base64_string (str): Base64 encoded WAV data - - Returns: - Tuple[np.ndarray, int]: Audio data and sample rate - """ - # Decode base64 string - wav_bytes = base64.b64decode(base64_string) - - # Write to temporary file - with open("temp.wav", "wb") as f: - f.write(wav_bytes) - - # Read the WAV file - audio_data, sample_rate = read_wav_file("temp.wav") - - # Clean up temporary file - Path("temp.wav").unlink() - - return audio_data, sample_rate diff --git a/swarms/utils/auto_download_check_packages.py b/swarms/utils/auto_download_check_packages.py deleted file mode 100644 index 187e2b11..00000000 --- a/swarms/utils/auto_download_check_packages.py +++ /dev/null @@ -1,151 +0,0 @@ -""" -Package installation utility that checks for package existence and installs if needed. -Supports both pip and conda package managers. -""" - -import importlib.util -import subprocess -import sys -from typing import Literal, Optional, Union -from swarms.utils.loguru_logger import initialize_logger - - -from importlib.metadata import distribution, PackageNotFoundError - -logger = initialize_logger("autocheckpackages") - - -def check_and_install_package( - package_name: str, - package_manager: Literal["pip", "conda"] = "pip", - version: Optional[str] = None, - upgrade: bool = False, -) -> bool: - """ - Check if a package is installed and install it if not found. - - Args: - package_name: Name of the package to check/install - package_manager: Package manager to use ('pip' or 'conda') - version: Specific version to install (optional) - upgrade: Whether to upgrade the package if it exists - - Returns: - bool: True if package is available after check/install, False if installation failed - - Raises: - ValueError: If invalid package manager is specified - """ - try: - # Check if package exists - if package_manager == "pip": - try: - distribution(package_name) - if not upgrade: - logger.info( - f"Package {package_name} is already installed" - ) - return True - except PackageNotFoundError: - pass - - # Construct installation command - cmd = [sys.executable, "-m", "pip", "install"] - if upgrade: - cmd.append("--upgrade") - - if version: - cmd.append(f"{package_name}=={version}") - else: - cmd.append(package_name) - - elif package_manager == "conda": - # Check if conda is available - try: - subprocess.run( - ["conda", "--version"], - check=True, - capture_output=True, - ) - except (subprocess.CalledProcessError, FileNotFoundError): - logger.error( - "Conda is not available. Please install conda first." - ) - return False - - # Construct conda command - cmd = ["conda", "install", "-y"] - if version: - cmd.append(f"{package_name}={version}") - else: - cmd.append(package_name) - else: - raise ValueError( - f"Invalid package manager: {package_manager}" - ) - - # Run installation - logger.info(f"Installing {package_name}...") - subprocess.run( - cmd, check=True, capture_output=True, text=True - ) - - # Verify installation - try: - importlib.import_module(package_name) - logger.info(f"Successfully installed {package_name}") - return True - except ImportError: - logger.error( - f"Package {package_name} was installed but cannot be imported" - ) - return False - - except subprocess.CalledProcessError as e: - logger.error(f"Failed to install {package_name}: {e.stderr}") - return False - except Exception as e: - logger.error( - f"Unexpected error while installing {package_name}: {str(e)}" - ) - return False - - -def auto_check_and_download_package( - packages: Union[str, list[str]], - package_manager: Literal["pip", "conda"] = "pip", - upgrade: bool = False, -) -> bool: - """ - Ensure multiple packages are installed. - - Args: - packages: Single package name or list of package names - package_manager: Package manager to use ('pip' or 'conda') - upgrade: Whether to upgrade existing packages - - Returns: - bool: True if all packages are available, False if any installation failed - """ - if isinstance(packages, str): - packages = [packages] - - success = True - for package in packages: - if ":" in package: - name, version = package.split(":") - if not check_and_install_package( - name, package_manager, version, upgrade - ): - success = False - else: - if not check_and_install_package( - package, package_manager, upgrade=upgrade - ): - success = False - - return success - - -# if __name__ == "__main__": -# print(auto_check_and_download_package("torch")) diff --git a/swarms/utils/best_models.py b/swarms/utils/best_models.py deleted file mode 100644 index 49a77930..00000000 --- a/swarms/utils/best_models.py +++ /dev/null @@ -1,88 +0,0 @@ -# Best LLM Models by Task Type -# Simplified dictionary structure with model names and categories - -best_models = { - "Vision": [ - {"model": "gemini/gemini-2.5-pro", "category": "Vision"}, - ], - "text-generation": [ - { - "model": "claude-sonnet-4-20250514", - "category": "text-generation", - }, - {"model": "gpt-5-chat", "category": "text-generation"}, - ], -} - - -# Function to get all models for a task type -def get_models_by_task(task_type: str) -> list: - """ - Get all models for a specific task type. - - Args: - task_type (str): The task category (e.g., 'WebDev', 'Vision', 'text-generation') - - Returns: - list: List of all models for the task type - """ - if task_type not in best_models: - raise ValueError( - f"Task type '{task_type}' not found. Available types: {list(best_models.keys())}" - ) - - return best_models[task_type] - - -# Function to get the first model for a task type (simplified from get_top_model) -def get_first_model(task_type: str) -> dict: - """ - Get the first model for a specific task type. - - Args: - task_type (str): The task category (e.g., 'WebDev', 'Vision', 'text-generation') - - Returns: - dict: First model information with model name and category - """ - if task_type not in best_models: - raise ValueError( - f"Task type '{task_type}' not found. Available types: {list(best_models.keys())}" - ) - - models = best_models[task_type] - if not models: - raise ValueError( - f"No models found for task type '{task_type}'" - ) - - return models[0] - - -# Function to search for a specific model across all categories -def find_model_by_name(model_name: str) -> dict: - """ - Find a model by name across all task categories. - - Args: - model_name (str): The model name to search for - - Returns: - dict: Model information if found, None otherwise - """ - for task_type, models in best_models.items(): - for model in models: - if model["model"].lower() == model_name.lower(): - return model - return None - - -# Function to get all available task types -def get_available_task_types() -> list: - """ - Get all available task types/categories. - - Returns: - list: List of all task type names - """ - return list(best_models.keys()) diff --git a/swarms/utils/image_generator.py b/swarms/utils/image_generator.py deleted file mode 100644 index 1fffdb30..00000000 --- a/swarms/utils/image_generator.py +++ /dev/null @@ -1,54 +0,0 @@ -from typing import Any -from litellm import image_generation - - -class ImageGenerator: - def __init__( - self, - model: str | None = None, - n: int | None = 2, - quality: Any = None, - response_format: str | None = None, - size: str | None = 10, - style: str | None = None, - user: str | None = None, - input_fidelity: str | None = None, - timeout: int = 600, - output_path_folder: str | None = "images", - api_key: str | None = None, - api_base: str | None = None, - ): - self.model = model - self.n = n - self.quality = quality - self.response_format = response_format - self.size = size - self.style = style - self.user = user - self.input_fidelity = input_fidelity - self.timeout = timeout - self.output_path_folder = output_path_folder - self.api_key = api_key - self.api_base = api_base - - def run(self, task: str = None): - - return image_generation( - prompt=task, - model=self.model, - n=self.n, - quality=self.quality, - response_format=self.response_format, - size=self.size, - style=self.style, - user=self.user, - input_fidelity=self.input_fidelity, - timeout=self.timeout, - ) - - -# if __name__ == "__main__": -# image_generator = ImageGenerator() -# print(image_generator.run(task="A beautiful sunset over a calm ocean")) - -# print(model_list) diff --git a/swarms/utils/lite_utils.py b/swarms/utils/lite_utils.py deleted file mode 100644 index 1f8b0cf0..00000000 --- a/swarms/utils/lite_utils.py +++ /dev/null @@ -1,5 +0,0 @@ -def litellm_check_for_tools(model_name: str): - """Check if the model supports tools.""" - from litellm.utils import supports_function_calling - - return supports_function_calling(model_name) diff --git a/tests/agent/agents/test_agent_logging.py b/tests/agent/agents/test_agent_logging.py deleted file mode 100644 index 1439935e..00000000 --- a/tests/agent/agents/test_agent_logging.py +++ /dev/null @@ -1,114 +0,0 @@ -from unittest.mock import MagicMock -import unittest -from swarms.structs.agent import Agent -from swarms.tools.tool_parse_exec import parse_and_execute_json - -# Mock parse_and_execute_json for testing -parse_and_execute_json = MagicMock() -parse_and_execute_json.return_value = { - "tool_name": "calculator", - "args": {"numbers": [2, 2]}, - "output": "4", -} - - -class TestAgentLogging(unittest.TestCase): - def setUp(self): - self.mock_tokenizer = MagicMock() - self.mock_tokenizer.count_tokens.return_value = 100 - - self.mock_short_memory = MagicMock() - self.mock_short_memory.get_memory_stats.return_value = { - "message_count": 2 - } - - self.mock_long_memory = MagicMock() - self.mock_long_memory.get_memory_stats.return_value = { - "item_count": 5 - } - - self.agent = Agent( - tokenizer=self.mock_tokenizer, - short_memory=self.mock_short_memory, - long_term_memory=self.mock_long_memory, - ) - - def test_log_step_metadata_basic(self): - log_result = self.agent.log_step_metadata( - 1, "Test prompt", "Test response" - ) - - self.assertIn("step_id", log_result) - self.assertIn("timestamp", log_result) - self.assertIn("tokens", log_result) - self.assertIn("memory_usage", log_result) - - self.assertEqual(log_result["tokens"]["total"], 200) - - def test_log_step_metadata_no_long_term_memory(self): - self.agent.long_term_memory = None - log_result = self.agent.log_step_metadata( - 1, "prompt", "response" - ) - self.assertEqual(log_result["memory_usage"]["long_term"], {}) - - def test_log_step_metadata_timestamp(self): - log_result = self.agent.log_step_metadata( - 1, "prompt", "response" - ) - self.assertIn("timestamp", log_result) - - def test_token_counting_integration(self): - self.mock_tokenizer.count_tokens.side_effect = [150, 250] - log_result = self.agent.log_step_metadata( - 1, "prompt", "response" - ) - - self.assertEqual(log_result["tokens"]["total"], 400) - - def test_agent_output_updating(self): - initial_total_tokens = sum( - step["tokens"]["total"] - for step in self.agent.agent_output.steps - ) - self.agent.log_step_metadata(1, "prompt", "response") - - final_total_tokens = sum( - step["tokens"]["total"] - for step in self.agent.agent_output.steps - ) - self.assertEqual( - final_total_tokens - initial_total_tokens, 200 - ) - self.assertEqual(len(self.agent.agent_output.steps), 1) - - -class TestAgentLoggingIntegration(unittest.TestCase): - def setUp(self): - self.agent = Agent(agent_name="test-agent") - - def test_full_logging_cycle(self): - task = "Test task" - max_loops = 1 - - result = self.agent._run(task, max_loops=max_loops) - - self.assertIsInstance(result, dict) - self.assertIn("steps", result) - self.assertIsInstance(result["steps"], list) - self.assertEqual(len(result["steps"]), max_loops) - - if result["steps"]: - step = result["steps"][0] - self.assertIn("step_id", step) - self.assertIn("timestamp", step) - self.assertIn("task", step) - self.assertIn("response", step) - self.assertEqual(step["task"], task) - self.assertEqual(step["response"], "Response for loop 1") - - self.assertTrue(len(self.agent.agent_output.steps) > 0) - - -if __name__ == "__main__": - unittest.main() diff --git a/tests/agent/agents/test_create_agents_from_yaml.py b/tests/agent/agents/test_create_agents_from_yaml.py deleted file mode 100644 index 4e7e61df..00000000 --- a/tests/agent/agents/test_create_agents_from_yaml.py +++ /dev/null @@ -1,267 +0,0 @@ -import unittest -from unittest.mock import patch -from swarms import create_agents_from_yaml -import os - - -class TestCreateAgentsFromYaml(unittest.TestCase): - - def setUp(self): - # Mock the environment variable for API key - os.environ["OPENAI_API_KEY"] = "fake-api-key" - - # Mock agent configuration YAML content - self.valid_yaml_content = """ - agents: - - agent_name: "Financial-Analysis-Agent" - model: - openai_api_key: "fake-api-key" - model_name: "gpt-4o-mini" - temperature: 0.1 - max_tokens: 2000 - system_prompt: "financial_agent_sys_prompt" - max_loops: 1 - autosave: true - dashboard: false - verbose: true - dynamic_temperature_enabled: true - saved_state_path: "finance_agent.json" - user_name: "swarms_corp" - retry_attempts: 1 - context_length: 200000 - return_step_meta: false - output_type: "str" - task: "How can I establish a ROTH IRA to buy stocks and get a tax break?" - - - agent_name: "Stock-Analysis-Agent" - model: - openai_api_key: "fake-api-key" - model_name: "gpt-4o-mini" - temperature: 0.2 - max_tokens: 1500 - system_prompt: "stock_agent_sys_prompt" - max_loops: 2 - autosave: true - dashboard: false - verbose: true - dynamic_temperature_enabled: false - saved_state_path: "stock_agent.json" - user_name: "stock_user" - retry_attempts: 3 - context_length: 150000 - return_step_meta: true - output_type: "json" - task: "What is the best strategy for long-term stock investment?" - """ - - @patch( - "builtins.open", - new_callable=unittest.mock.mock_open, - read_data="", - ) - @patch("yaml.safe_load") - def test_create_agents_return_agents( - self, mock_safe_load, mock_open - ): - # Mock YAML content parsing - mock_safe_load.return_value = { - "agents": [ - { - "agent_name": "Financial-Analysis-Agent", - "model": { - "openai_api_key": "fake-api-key", - "model_name": "gpt-4o-mini", - "temperature": 0.1, - "max_tokens": 2000, - }, - "system_prompt": "financial_agent_sys_prompt", - "max_loops": 1, - "autosave": True, - "dashboard": False, - "verbose": True, - "dynamic_temperature_enabled": True, - "saved_state_path": "finance_agent.json", - "user_name": "swarms_corp", - "retry_attempts": 1, - "context_length": 200000, - "return_step_meta": False, - "output_type": "str", - "task": "How can I establish a ROTH IRA to buy stocks and get a tax break?", - } - ] - } - - # Test if agents are returned correctly - agents = create_agents_from_yaml( - "fake_yaml_path.yaml", return_type="agents" - ) - self.assertEqual(len(agents), 1) - self.assertEqual( - agents[0].agent_name, "Financial-Analysis-Agent" - ) - - @patch( - "builtins.open", - new_callable=unittest.mock.mock_open, - read_data="", - ) - @patch("yaml.safe_load") - @patch( - "swarms.Agent.run", return_value="Task completed successfully" - ) - def test_create_agents_return_tasks( - self, mock_agent_run, mock_safe_load, mock_open - ): - # Mock YAML content parsing - mock_safe_load.return_value = { - "agents": [ - { - "agent_name": "Financial-Analysis-Agent", - "model": { - "openai_api_key": "fake-api-key", - "model_name": "gpt-4o-mini", - "temperature": 0.1, - "max_tokens": 2000, - }, - "system_prompt": "financial_agent_sys_prompt", - "max_loops": 1, - "autosave": True, - "dashboard": False, - "verbose": True, - "dynamic_temperature_enabled": True, - "saved_state_path": "finance_agent.json", - "user_name": "swarms_corp", - "retry_attempts": 1, - "context_length": 200000, - "return_step_meta": False, - "output_type": "str", - "task": "How can I establish a ROTH IRA to buy stocks and get a tax break?", - } - ] - } - - # Test if tasks are executed and results are returned - task_results = create_agents_from_yaml( - "fake_yaml_path.yaml", return_type="tasks" - ) - self.assertEqual(len(task_results), 1) - self.assertEqual( - task_results[0]["agent_name"], "Financial-Analysis-Agent" - ) - self.assertIsNotNone(task_results[0]["output"]) - - @patch( - "builtins.open", - new_callable=unittest.mock.mock_open, - read_data="", - ) - @patch("yaml.safe_load") - def test_create_agents_return_both( - self, mock_safe_load, mock_open - ): - # Mock YAML content parsing - mock_safe_load.return_value = { - "agents": [ - { - "agent_name": "Financial-Analysis-Agent", - "model": { - "openai_api_key": "fake-api-key", - "model_name": "gpt-4o-mini", - "temperature": 0.1, - "max_tokens": 2000, - }, - "system_prompt": "financial_agent_sys_prompt", - "max_loops": 1, - "autosave": True, - "dashboard": False, - "verbose": True, - "dynamic_temperature_enabled": True, - "saved_state_path": "finance_agent.json", - "user_name": "swarms_corp", - "retry_attempts": 1, - "context_length": 200000, - "return_step_meta": False, - "output_type": "str", - "task": "How can I establish a ROTH IRA to buy stocks and get a tax break?", - } - ] - } - - # Test if both agents and tasks are returned - agents, task_results = create_agents_from_yaml( - "fake_yaml_path.yaml", return_type="both" - ) - self.assertEqual(len(agents), 1) - self.assertEqual(len(task_results), 1) - self.assertEqual( - agents[0].agent_name, "Financial-Analysis-Agent" - ) - self.assertIsNotNone(task_results[0]["output"]) - - @patch( - "builtins.open", - new_callable=unittest.mock.mock_open, - read_data="", - ) - @patch("yaml.safe_load") - def test_missing_agents_in_yaml(self, mock_safe_load, mock_open): - # Mock YAML content with missing "agents" key - mock_safe_load.return_value = {} - - # Test if the function raises an error for missing "agents" key - with self.assertRaises(ValueError) as context: - create_agents_from_yaml( - "fake_yaml_path.yaml", return_type="agents" - ) - self.assertTrue( - "The YAML configuration does not contain 'agents'." - in str(context.exception) - ) - - @patch( - "builtins.open", - new_callable=unittest.mock.mock_open, - read_data="", - ) - @patch("yaml.safe_load") - def test_invalid_return_type(self, mock_safe_load, mock_open): - # Mock YAML content parsing - mock_safe_load.return_value = { - "agents": [ - { - "agent_name": "Financial-Analysis-Agent", - "model": { - "openai_api_key": "fake-api-key", - "model_name": "gpt-4o-mini", - "temperature": 0.1, - "max_tokens": 2000, - }, - "system_prompt": "financial_agent_sys_prompt", - "max_loops": 1, - "autosave": True, - "dashboard": False, - "verbose": True, - "dynamic_temperature_enabled": True, - "saved_state_path": "finance_agent.json", - "user_name": "swarms_corp", - "retry_attempts": 1, - "context_length": 200000, - "return_step_meta": False, - "output_type": "str", - "task": "How can I establish a ROTH IRA to buy stocks and get a tax break?", - } - ] - } - - # Test if an error is raised for invalid return_type - with self.assertRaises(ValueError) as context: - create_agents_from_yaml( - "fake_yaml_path.yaml", return_type="invalid_type" - ) - self.assertTrue( - "Invalid return_type" in str(context.exception) - ) - - -if __name__ == "__main__": - unittest.main() diff --git a/tests/agent/agents/test_llm_args.py b/tests/agent/agents/test_llm_args.py deleted file mode 100644 index 7745ed5c..00000000 --- a/tests/agent/agents/test_llm_args.py +++ /dev/null @@ -1,190 +0,0 @@ -#!/usr/bin/env python3 -""" -Test script to verify the LiteLLM initialization fix for combined parameters. -This test ensures that llm_args, tools_list_dictionary, and MCP tools can be used together. -""" - -import sys - - -from swarms import Agent - - -def test_combined_llm_args(): - """Test that llm_args, tools_list_dictionary, and MCP tools can be combined.""" - - # Mock tools list dictionary - tools_list = [ - { - "type": "function", - "function": { - "name": "test_function", - "description": "A test function", - "parameters": { - "type": "object", - "properties": { - "test_param": { - "type": "string", - "description": "A test parameter", - } - }, - }, - }, - } - ] - - # Mock llm_args with Azure OpenAI specific parameters - llm_args = { - "api_version": "2024-02-15-preview", - "base_url": "https://your-resource.openai.azure.com/", - "api_key": "your-api-key", - } - - try: - # Test 1: Only llm_args - print("Testing Agent with only llm_args...") - Agent( - agent_name="test-agent-1", - model_name="gpt-4o-mini", - llm_args=llm_args, - ) - print("āœ“ Agent with only llm_args created successfully") - - # Test 2: Only tools_list_dictionary - print("Testing Agent with only tools_list_dictionary...") - Agent( - agent_name="test-agent-2", - model_name="gpt-4o-mini", - tools_list_dictionary=tools_list, - ) - print( - "āœ“ Agent with only tools_list_dictionary created successfully" - ) - - # Test 3: Combined llm_args and tools_list_dictionary - print( - "Testing Agent with combined llm_args and tools_list_dictionary..." - ) - agent3 = Agent( - agent_name="test-agent-3", - model_name="gpt-4o-mini", - llm_args=llm_args, - tools_list_dictionary=tools_list, - ) - print( - "āœ“ Agent with combined llm_args and tools_list_dictionary created successfully" - ) - - # Test 4: Verify that the LLM instance has the correct configuration - print("Verifying LLM configuration...") - - # Check that agent3 has both llm_args and tools configured - assert agent3.llm_args == llm_args, "llm_args not preserved" - assert ( - agent3.tools_list_dictionary == tools_list - ), "tools_list_dictionary not preserved" - - # Check that the LLM instance was created - assert agent3.llm is not None, "LLM instance not created" - - print("āœ“ LLM configuration verified successfully") - - # Test 5: Test that the LLM can be called (without actually making API calls) - print("Testing LLM call preparation...") - try: - # This should not fail due to configuration issues - # We're not actually calling the API, just testing the setup - print("āœ“ LLM call preparation successful") - except Exception as e: - print(f"āœ— LLM call preparation failed: {e}") - return False - - print( - "\nšŸŽ‰ All tests passed! The LiteLLM initialization fix is working correctly." - ) - return True - - except Exception as e: - print(f"āœ— Test failed: {e}") - import traceback - - traceback.print_exc() - return False - - -def test_azure_openai_example(): - """Test the Azure OpenAI example with api_version parameter.""" - - print("\nTesting Azure OpenAI example with api_version...") - - try: - # Create an agent with Azure OpenAI configuration - agent = Agent( - agent_name="azure-test-agent", - model_name="azure/gpt-4o", - llm_args={ - "api_version": "2024-02-15-preview", - "base_url": "https://your-resource.openai.azure.com/", - "api_key": "your-api-key", - }, - tools_list_dictionary=[ - { - "type": "function", - "function": { - "name": "get_weather", - "description": "Get weather information", - "parameters": { - "type": "object", - "properties": { - "location": { - "type": "string", - "description": "The city and state", - } - }, - }, - }, - } - ], - ) - - print( - "āœ“ Azure OpenAI agent with combined parameters created successfully" - ) - - # Verify configuration - assert agent.llm_args is not None, "llm_args not set" - assert ( - "api_version" in agent.llm_args - ), "api_version not in llm_args" - assert ( - agent.tools_list_dictionary is not None - ), "tools_list_dictionary not set" - assert ( - len(agent.tools_list_dictionary) > 0 - ), "tools_list_dictionary is empty" - - print("āœ“ Azure OpenAI configuration verified") - return True - - except Exception as e: - print(f"āœ— Azure OpenAI test failed: {e}") - import traceback - - traceback.print_exc() - return False - - -if __name__ == "__main__": - print("šŸ§Ŗ Testing LiteLLM initialization fix...") - - success1 = test_combined_llm_args() - success2 = test_azure_openai_example() - - if success1 and success2: - print("\nāœ… All tests passed! The fix is working correctly.") - sys.exit(0) - else: - print( - "\nāŒ Some tests failed. Please check the implementation." - ) - sys.exit(1) diff --git a/tests/agent/agents/test_llm_handling_args.py b/tests/agent/agents/test_llm_handling_args.py deleted file mode 100644 index 04122bb5..00000000 --- a/tests/agent/agents/test_llm_handling_args.py +++ /dev/null @@ -1,73 +0,0 @@ -#!/usr/bin/env python3 -""" -Test script to verify that the llm_handling method properly handles args and kwargs. -""" - -import sys -import os - -# Add the swarms directory to the path -sys.path.insert(0, os.path.join(os.path.dirname(__file__), "swarms")) - -from swarms.structs.agent import Agent - - -def test_llm_handling_args_kwargs(): - """Test that llm_handling properly handles both args and kwargs.""" - - # Create an agent instance - agent = Agent( - agent_name="test-agent", - model_name="gpt-4o-mini", - temperature=0.7, - max_tokens=1000, - ) - - # Test 1: Call llm_handling with kwargs - print("Test 1: Testing kwargs handling...") - try: - # This should work and add the kwargs to additional_args - agent.llm_handling(top_p=0.9, frequency_penalty=0.1) - print("āœ“ kwargs handling works") - except Exception as e: - print(f"āœ— kwargs handling failed: {e}") - - # Test 2: Call llm_handling with args (dictionary) - print("\nTest 2: Testing args handling with dictionary...") - try: - # This should merge the dictionary into additional_args - additional_config = { - "presence_penalty": 0.2, - "logit_bias": {"123": 1}, - } - agent.llm_handling(additional_config) - print("āœ“ args handling with dictionary works") - except Exception as e: - print(f"āœ— args handling with dictionary failed: {e}") - - # Test 3: Call llm_handling with both args and kwargs - print("\nTest 3: Testing both args and kwargs...") - try: - # This should handle both - additional_config = {"presence_penalty": 0.3} - agent.llm_handling( - additional_config, top_p=0.8, frequency_penalty=0.2 - ) - print("āœ“ combined args and kwargs handling works") - except Exception as e: - print(f"āœ— combined args and kwargs handling failed: {e}") - - # Test 4: Call llm_handling with non-dictionary args - print("\nTest 4: Testing non-dictionary args...") - try: - # This should store args under 'additional_args' key - agent.llm_handling( - "some_string", 123, ["list", "of", "items"] - ) - print("āœ“ non-dictionary args handling works") - except Exception as e: - print(f"āœ— non-dictionary args handling failed: {e}") - - -if __name__ == "__main__": - test_llm_handling_args_kwargs() diff --git a/tests/agent/agents/test_tool_agent.py b/tests/agent/agents/test_tool_agent.py deleted file mode 100644 index 11aca6bf..00000000 --- a/tests/agent/agents/test_tool_agent.py +++ /dev/null @@ -1,230 +0,0 @@ -from unittest.mock import Mock, patch -import pytest - -from transformers import AutoModelForCausalLM, AutoTokenizer - -from swarms import ToolAgent -from swarms.agents.exceptions import ( - ToolExecutionError, - ToolNotFoundError, - ToolParameterError, -) - - -def test_tool_agent_init(): - model = Mock(spec=AutoModelForCausalLM) - tokenizer = Mock(spec=AutoTokenizer) - json_schema = { - "type": "object", - "properties": { - "name": {"type": "string"}, - "age": {"type": "number"}, - "is_student": {"type": "boolean"}, - "courses": {"type": "array", "items": {"type": "string"}}, - }, - } - name = "Test Agent" - description = "This is a test agent" - - agent = ToolAgent( - name, description, model, tokenizer, json_schema - ) - - assert agent.name == name - assert agent.description == description - assert agent.model == model - assert agent.tokenizer == tokenizer - assert agent.json_schema == json_schema - - -@patch.object(ToolAgent, "run") -def test_tool_agent_run(mock_run): - model = Mock(spec=AutoModelForCausalLM) - tokenizer = Mock(spec=AutoTokenizer) - json_schema = { - "type": "object", - "properties": { - "name": {"type": "string"}, - "age": {"type": "number"}, - "is_student": {"type": "boolean"}, - "courses": {"type": "array", "items": {"type": "string"}}, - }, - } - name = "Test Agent" - description = "This is a test agent" - task = ( - "Generate a person's information based on the following" - " schema:" - ) - - agent = ToolAgent( - name, description, model, tokenizer, json_schema - ) - agent.run(task) - - mock_run.assert_called_once_with(task) - - -def test_tool_agent_init_with_kwargs(): - model = Mock(spec=AutoModelForCausalLM) - tokenizer = Mock(spec=AutoTokenizer) - json_schema = { - "type": "object", - "properties": { - "name": {"type": "string"}, - "age": {"type": "number"}, - "is_student": {"type": "boolean"}, - "courses": {"type": "array", "items": {"type": "string"}}, - }, - } - name = "Test Agent" - description = "This is a test agent" - - kwargs = { - "debug": True, - "max_array_length": 20, - "max_number_tokens": 12, - "temperature": 0.5, - "max_string_token_length": 20, - } - - agent = ToolAgent( - name, description, model, tokenizer, json_schema, **kwargs - ) - - assert agent.name == name - assert agent.description == description - assert agent.model == model - assert agent.tokenizer == tokenizer - assert agent.json_schema == json_schema - assert agent.debug == kwargs["debug"] - assert agent.max_array_length == kwargs["max_array_length"] - assert agent.max_number_tokens == kwargs["max_number_tokens"] - assert agent.temperature == kwargs["temperature"] - assert ( - agent.max_string_token_length - == kwargs["max_string_token_length"] - ) - - -def test_tool_agent_initialization(): - """Test tool agent initialization with valid parameters.""" - agent = ToolAgent( - model_name="test-model", temperature=0.7, max_tokens=1000 - ) - assert agent.model_name == "test-model" - assert agent.temperature == 0.7 - assert agent.max_tokens == 1000 - assert agent.retry_attempts == 3 - assert agent.retry_interval == 1.0 - - -def test_tool_agent_initialization_error(): - """Test tool agent initialization with invalid model.""" - with pytest.raises(ToolExecutionError) as exc_info: - ToolAgent(model_name="invalid-model") - assert "model_initialization" in str(exc_info.value) - - -def test_tool_validation(): - """Test tool parameter validation.""" - tools_list = [ - { - "name": "test_tool", - "parameters": [ - {"name": "required_param", "required": True}, - {"name": "optional_param", "required": False}, - ], - } - ] - - agent = ToolAgent(tools_list_dictionary=tools_list) - - # Test missing required parameter - with pytest.raises(ToolParameterError) as exc_info: - agent._validate_tool("test_tool", {}) - assert "Missing required parameters" in str(exc_info.value) - - # Test valid parameters - agent._validate_tool("test_tool", {"required_param": "value"}) - - # Test non-existent tool - with pytest.raises(ToolNotFoundError) as exc_info: - agent._validate_tool("non_existent_tool", {}) - assert "Tool 'non_existent_tool' not found" in str(exc_info.value) - - -def test_retry_mechanism(): - """Test retry mechanism for failed operations.""" - mock_llm = Mock() - mock_llm.generate.side_effect = [ - Exception("First attempt failed"), - Exception("Second attempt failed"), - Mock(outputs=[Mock(text="Success")]), - ] - - agent = ToolAgent(model_name="test-model") - agent.llm = mock_llm - - # Test successful retry - result = agent.run("test task") - assert result == "Success" - assert mock_llm.generate.call_count == 3 - - # Test all retries failing - mock_llm.generate.side_effect = Exception("All attempts failed") - with pytest.raises(ToolExecutionError) as exc_info: - agent.run("test task") - assert "All attempts failed" in str(exc_info.value) - - -def test_batched_execution(): - """Test batched execution with error handling.""" - mock_llm = Mock() - mock_llm.generate.side_effect = [ - Mock(outputs=[Mock(text="Success 1")]), - Exception("Task 2 failed"), - Mock(outputs=[Mock(text="Success 3")]), - ] - - agent = ToolAgent(model_name="test-model") - agent.llm = mock_llm - - tasks = ["Task 1", "Task 2", "Task 3"] - results = agent.batched_run(tasks) - - assert len(results) == 3 - assert results[0] == "Success 1" - assert "Error" in results[1] - assert results[2] == "Success 3" - - -def test_prompt_preparation(): - """Test prompt preparation with and without system prompt.""" - # Test without system prompt - agent = ToolAgent() - prompt = agent._prepare_prompt("test task") - assert prompt == "User: test task\nAssistant:" - - # Test with system prompt - agent = ToolAgent(system_prompt="You are a helpful assistant") - prompt = agent._prepare_prompt("test task") - assert ( - prompt - == "You are a helpful assistant\n\nUser: test task\nAssistant:" - ) - - -def test_tool_execution_error_handling(): - """Test error handling during tool execution.""" - agent = ToolAgent(model_name="test-model") - agent.llm = None # Simulate uninitialized LLM - - with pytest.raises(ToolExecutionError) as exc_info: - agent.run("test task") - assert "LLM not initialized" in str(exc_info.value) - - # Test with invalid parameters - with pytest.raises(ToolExecutionError) as exc_info: - agent.run("test task", invalid_param="value") - assert "Error running task" in str(exc_info.value) diff --git a/tests/agent/benchmark_agent/test_agent_benchmark_init.py b/tests/agent/benchmark_agent/test_agent_benchmark_init.py deleted file mode 100644 index 5f852576..00000000 --- a/tests/agent/benchmark_agent/test_agent_benchmark_init.py +++ /dev/null @@ -1,171 +0,0 @@ -from time import perf_counter_ns -import psutil -import os -from rich.panel import Panel -from rich.console import Console -from rich.table import Table -from statistics import mean, median, stdev, variance -from swarms.structs.agent import Agent -from swarms.prompts.finance_agent_sys_prompt import ( - FINANCIAL_AGENT_SYS_PROMPT, -) - - -def get_memory_stats(memory_readings): - """Calculate memory statistics""" - return { - "peak": max(memory_readings), - "min": min(memory_readings), - "mean": mean(memory_readings), - "median": median(memory_readings), - "stdev": ( - stdev(memory_readings) if len(memory_readings) > 1 else 0 - ), - "variance": ( - variance(memory_readings) - if len(memory_readings) > 1 - else 0 - ), - } - - -def get_time_stats(times): - """Calculate time statistics""" - return { - "total": sum(times), - "mean": mean(times), - "median": median(times), - "min": min(times), - "max": max(times), - "stdev": stdev(times) if len(times) > 1 else 0, - "variance": variance(times) if len(times) > 1 else 0, - } - - -def benchmark_multiple_agents(num_agents=100): - console = Console() - init_times = [] - memory_readings = [] - process = psutil.Process(os.getpid()) - - # Create benchmark tables - time_table = Table(title="Time Statistics") - time_table.add_column("Metric", style="cyan") - time_table.add_column("Value", style="green") - - memory_table = Table(title="Memory Statistics") - memory_table.add_column("Metric", style="cyan") - memory_table.add_column("Value", style="green") - - initial_memory = process.memory_info().rss / 1024 - start_total_time = perf_counter_ns() - - # Initialize agents and measure performance - for i in range(num_agents): - start_time = perf_counter_ns() - - Agent( - agent_name=f"Financial-Analysis-Agent-{i}", - agent_description="Personal finance advisor agent", - system_prompt=FINANCIAL_AGENT_SYS_PROMPT, - max_loops=2, - model_name="gpt-4o-mini", - dynamic_temperature_enabled=True, - interactive=False, - ) - - init_time = (perf_counter_ns() - start_time) / 1_000_000 - init_times.append(init_time) - - current_memory = process.memory_info().rss / 1024 - memory_readings.append(current_memory - initial_memory) - - if (i + 1) % 10 == 0: - console.print( - f"Created {i + 1} agents...", style="bold blue" - ) - - total_elapsed_time = ( - perf_counter_ns() - start_total_time - ) / 1_000_000 - - # Calculate statistics - time_stats = get_time_stats(init_times) - memory_stats = get_memory_stats(memory_readings) - - # Add time measurements - time_table.add_row( - "Total Wall Time", f"{total_elapsed_time:.2f} ms" - ) - time_table.add_row( - "Total Init Time", f"{time_stats['total']:.2f} ms" - ) - time_table.add_row( - "Average Init Time", f"{time_stats['mean']:.2f} ms" - ) - time_table.add_row( - "Median Init Time", f"{time_stats['median']:.2f} ms" - ) - time_table.add_row("Fastest Init", f"{time_stats['min']:.2f} ms") - time_table.add_row("Slowest Init", f"{time_stats['max']:.2f} ms") - time_table.add_row( - "Std Deviation", f"{time_stats['stdev']:.2f} ms" - ) - time_table.add_row( - "Variance", f"{time_stats['variance']:.4f} msĀ²" - ) - time_table.add_row( - "Throughput", - f"{(num_agents/total_elapsed_time) * 1000:.2f} agents/second", - ) - time_table.add_row( - "Agents per Minute", - f"{(num_agents/total_elapsed_time) * 60000:.0f} agents/minute", - ) - - # Add memory measurements - memory_table.add_row( - "Peak Memory Usage", f"{memory_stats['peak']:.2f} KB" - ) - memory_table.add_row( - "Minimum Memory Usage", f"{memory_stats['min']:.2f} KB" - ) - memory_table.add_row( - "Average Memory Usage", f"{memory_stats['mean']:.2f} KB" - ) - memory_table.add_row( - "Median Memory Usage", f"{memory_stats['median']:.2f} KB" - ) - memory_table.add_row( - "Memory Std Deviation", f"{memory_stats['stdev']:.2f} KB" - ) - memory_table.add_row( - "Memory Variance", f"{memory_stats['variance']:.2f} KBĀ²" - ) - memory_table.add_row( - "Avg Memory Per Agent", - f"{memory_stats['mean']/num_agents:.2f} KB", - ) - - # Create and display panels - time_panel = Panel( - time_table, - title="Time Benchmark Results", - border_style="blue", - padding=(1, 2), - ) - - memory_panel = Panel( - memory_table, - title="Memory Benchmark Results", - border_style="green", - padding=(1, 2), - ) - - console.print(time_panel) - console.print("\n") - console.print(memory_panel) - - -if __name__ == "__main__": - benchmark_multiple_agents(1000) diff --git a/tests/agent/benchmark_agent/test_agent_exec_benchmark.py b/tests/agent/benchmark_agent/test_agent_exec_benchmark.py deleted file mode 100644 index 11872304..00000000 --- a/tests/agent/benchmark_agent/test_agent_exec_benchmark.py +++ /dev/null @@ -1,284 +0,0 @@ -import asyncio -import concurrent.futures -import json -import os -import psutil -import datetime -from pathlib import Path -from typing import List, Dict, Any, Optional -from swarms.structs.agent import Agent -from loguru import logger - - -class AgentBenchmark: - def __init__( - self, - num_iterations: int = 5, - output_dir: str = "benchmark_results", - ): - self.num_iterations = num_iterations - self.output_dir = Path(output_dir) - self.output_dir.mkdir(exist_ok=True) - - # Use process pool for CPU-bound tasks - self.process_pool = concurrent.futures.ProcessPoolExecutor( - max_workers=min(os.cpu_count(), 4) - ) - - # Use thread pool for I/O-bound tasks - self.thread_pool = concurrent.futures.ThreadPoolExecutor( - max_workers=min(os.cpu_count() * 2, 8) - ) - - self.default_queries = [ - "Conduct an analysis of the best real undervalued ETFs", - "What are the top performing tech stocks this quarter?", - "Analyze current market trends in renewable energy sector", - "Compare Bitcoin and Ethereum investment potential", - "Evaluate the risk factors in emerging markets", - ] - - self.agent = self._initialize_agent() - self.process = psutil.Process() - - # Cache for storing repeated query results - self._query_cache = {} - - def _initialize_agent(self) -> Agent: - return Agent( - agent_name="Financial-Analysis-Agent", - agent_description="Personal finance advisor agent", - # system_prompt=FINANCIAL_AGENT_SYS_PROMPT, - max_loops=1, - model_name="gpt-4o-mini", - dynamic_temperature_enabled=True, - interactive=False, - ) - - def _get_system_metrics(self) -> Dict[str, float]: - # Optimized system metrics collection - return { - "cpu_percent": self.process.cpu_percent(), - "memory_mb": self.process.memory_info().rss / 1024 / 1024, - } - - def _calculate_statistics( - self, values: List[float] - ) -> Dict[str, float]: - if not values: - return {} - - sorted_values = sorted(values) - n = len(sorted_values) - mean_val = sum(values) / n - - stats = { - "mean": mean_val, - "median": sorted_values[n // 2], - "min": sorted_values[0], - "max": sorted_values[-1], - } - - # Only calculate stdev if we have enough values - if n > 1: - stats["std_dev"] = ( - sum((x - mean_val) ** 2 for x in values) / n - ) ** 0.5 - - return {k: round(v, 3) for k, v in stats.items()} - - async def process_iteration( - self, query: str, iteration: int - ) -> Dict[str, Any]: - """Process a single iteration of a query""" - try: - # Check cache for repeated queries - cache_key = f"{query}_{iteration}" - if cache_key in self._query_cache: - return self._query_cache[cache_key] - - iteration_start = datetime.datetime.now() - pre_metrics = self._get_system_metrics() - - # Run the agent - try: - self.agent.run(query) - success = True - except Exception as e: - str(e) - success = False - - execution_time = ( - datetime.datetime.now() - iteration_start - ).total_seconds() - post_metrics = self._get_system_metrics() - - result = { - "execution_time": execution_time, - "success": success, - "pre_metrics": pre_metrics, - "post_metrics": post_metrics, - "iteration_data": { - "iteration": iteration + 1, - "execution_time": round(execution_time, 3), - "success": success, - "system_metrics": { - "pre": pre_metrics, - "post": post_metrics, - }, - }, - } - - # Cache the result - self._query_cache[cache_key] = result - return result - - except Exception as e: - logger.error(f"Error in iteration {iteration}: {e}") - raise - - async def run_benchmark( - self, queries: Optional[List[str]] = None - ) -> Dict[str, Any]: - """Run the benchmark asynchronously""" - queries = queries or self.default_queries - benchmark_data = { - "metadata": { - "timestamp": datetime.datetime.now().isoformat(), - "num_iterations": self.num_iterations, - "agent_config": { - "model_name": self.agent.model_name, - "max_loops": self.agent.max_loops, - }, - }, - "results": {}, - } - - async def process_query(query: str): - query_results = { - "execution_times": [], - "system_metrics": [], - "iterations": [], - } - - # Process iterations concurrently - tasks = [ - self.process_iteration(query, i) - for i in range(self.num_iterations) - ] - iteration_results = await asyncio.gather(*tasks) - - for result in iteration_results: - query_results["execution_times"].append( - result["execution_time"] - ) - query_results["system_metrics"].append( - result["post_metrics"] - ) - query_results["iterations"].append( - result["iteration_data"] - ) - - # Calculate statistics - query_results["statistics"] = { - "execution_time": self._calculate_statistics( - query_results["execution_times"] - ), - "memory_usage": self._calculate_statistics( - [ - m["memory_mb"] - for m in query_results["system_metrics"] - ] - ), - "cpu_usage": self._calculate_statistics( - [ - m["cpu_percent"] - for m in query_results["system_metrics"] - ] - ), - } - - return query, query_results - - # Execute all queries concurrently - query_tasks = [process_query(query) for query in queries] - query_results = await asyncio.gather(*query_tasks) - - for query, results in query_results: - benchmark_data["results"][query] = results - - return benchmark_data - - def save_results(self, benchmark_data: Dict[str, Any]) -> str: - """Save benchmark results efficiently""" - timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S") - filename = ( - self.output_dir / f"benchmark_results_{timestamp}.json" - ) - - # Write results in a single operation - with open(filename, "w") as f: - json.dump(benchmark_data, f, indent=2) - - logger.info(f"Benchmark results saved to: {filename}") - return str(filename) - - def print_summary(self, results: Dict[str, Any]): - """Print a summary of the benchmark results""" - print("\n=== Benchmark Summary ===") - for query, data in results["results"].items(): - print(f"\nQuery: {query[:50]}...") - stats = data["statistics"]["execution_time"] - print(f"Average time: {stats['mean']:.2f}s") - print( - f"Memory usage (avg): {data['statistics']['memory_usage']['mean']:.1f}MB" - ) - print( - f"CPU usage (avg): {data['statistics']['cpu_usage']['mean']:.1f}%" - ) - - async def run_with_timeout( - self, timeout: int = 300 - ) -> Dict[str, Any]: - """Run benchmark with timeout""" - try: - return await asyncio.wait_for( - self.run_benchmark(), timeout - ) - except asyncio.TimeoutError: - logger.error( - f"Benchmark timed out after {timeout} seconds" - ) - raise - - def cleanup(self): - """Cleanup resources""" - self.process_pool.shutdown() - self.thread_pool.shutdown() - self._query_cache.clear() - - -async def main(): - try: - # Create and run benchmark - benchmark = AgentBenchmark(num_iterations=1) - - # Run benchmark with timeout - results = await benchmark.run_with_timeout(timeout=300) - - # Save results - benchmark.save_results(results) - - # Print summary - benchmark.print_summary(results) - - except Exception as e: - logger.error(f"Benchmark failed: {e}") - finally: - # Cleanup resources - benchmark.cleanup() - - -if __name__ == "__main__": - # Run the async main function - asyncio.run(main()) diff --git a/tests/agent/benchmark_agent/test_auto_test_eval.py b/tests/agent/benchmark_agent/test_auto_test_eval.py deleted file mode 100644 index 0b4d799a..00000000 --- a/tests/agent/benchmark_agent/test_auto_test_eval.py +++ /dev/null @@ -1,318 +0,0 @@ -import json -import os -import platform -import sys -import traceback -from dataclasses import dataclass -from datetime import datetime -from typing import Any, Dict, List, Optional - -import psutil -import requests -from loguru import logger -from swarm_models import OpenAIChat - -from swarms.structs.agent import Agent - - -@dataclass -class SwarmSystemInfo: - """System information for Swarms issue reports.""" - - os_name: str - os_version: str - python_version: str - cpu_usage: float - memory_usage: float - disk_usage: float - swarms_version: str # Added Swarms version tracking - cuda_available: bool # Added CUDA availability check - gpu_info: Optional[str] # Added GPU information - - -class SwarmsIssueReporter: - """ - Production-grade GitHub issue reporter specifically designed for the Swarms library. - Automatically creates detailed issues for the https://github.com/kyegomez/swarms repository. - - Features: - - Swarms-specific error categorization - - Automatic version and dependency tracking - - CUDA and GPU information collection - - Integration with Swarms logging system - - Detailed environment information - """ - - REPO_OWNER = "kyegomez" - REPO_NAME = "swarms" - ISSUE_CATEGORIES = { - "agent": ["agent", "automation"], - "memory": ["memory", "storage"], - "tool": ["tools", "integration"], - "llm": ["llm", "model"], - "performance": ["performance", "optimization"], - "compatibility": ["compatibility", "environment"], - } - - def __init__( - self, - github_token: str, - rate_limit: int = 10, - rate_period: int = 3600, - log_file: str = "swarms_issues.log", - enable_duplicate_check: bool = True, - ): - """ - Initialize the Swarms Issue Reporter. - - Args: - github_token (str): GitHub personal access token - rate_limit (int): Maximum number of issues to create per rate_period - rate_period (int): Time period for rate limiting in seconds - log_file (str): Path to log file - enable_duplicate_check (bool): Whether to check for duplicate issues - """ - self.github_token = github_token - self.rate_limit = rate_limit - self.rate_period = rate_period - self.enable_duplicate_check = enable_duplicate_check - self.github_token = os.getenv("GITHUB_API_KEY") - - # Initialize logging - log_path = os.path.join(os.getcwd(), "logs", log_file) - os.makedirs(os.path.dirname(log_path), exist_ok=True) - - # Issue tracking - self.issues_created = [] - self.last_issue_time = datetime.now() - - def _get_swarms_version(self) -> str: - """Get the installed version of Swarms.""" - try: - import swarms - - return swarms.__version__ - except: - return "Unknown" - - def _get_system_info(self) -> SwarmSystemInfo: - """Collect system and Swarms-specific information.""" - - return SwarmSystemInfo( - os_name=platform.system(), - os_version=platform.version(), - python_version=sys.version, - cpu_usage=psutil.cpu_percent(), - memory_usage=psutil.virtual_memory().percent, - disk_usage=psutil.disk_usage("/").percent, - swarms_version=self._get_swarms_version(), - ) - - def _categorize_error( - self, error: Exception, context: Dict - ) -> List[str]: - """Categorize the error and return appropriate labels.""" - error_str = str(error).lower() - type(error).__name__ - - labels = ["bug", "automated"] - - # Check error message and context for category keywords - for ( - category, - category_labels, - ) in self.ISSUE_CATEGORIES.items(): - if any( - keyword in error_str for keyword in category_labels - ): - labels.extend(category_labels) - break - - # Add severity label based on error type - if issubclass(type(error), (SystemError, MemoryError)): - labels.append("severity:critical") - elif issubclass(type(error), (ValueError, TypeError)): - labels.append("severity:medium") - else: - labels.append("severity:low") - - return list(set(labels)) # Remove duplicates - - def _format_swarms_issue_body( - self, - error: Exception, - system_info: SwarmSystemInfo, - context: Dict, - ) -> str: - """Format the issue body with Swarms-specific information.""" - return f""" - ## Swarms Error Report - - **Error Type**: {type(error).__name__} - - **Error Message**: {str(error)} - - **Swarms Version**: {system_info.swarms_version} - - ## Environment Information - - **OS**: {system_info.os_name} {system_info.os_version} - - **Python Version**: {system_info.python_version} - - **CUDA Available**: {system_info.cuda_available} - - **GPU**: {system_info.gpu_info or "N/A"} - - **CPU Usage**: {system_info.cpu_usage}% - - **Memory Usage**: {system_info.memory_usage}% - - **Disk Usage**: {system_info.disk_usage}% - - ## Stack Trace - {traceback.format_exc()} - - ## Context - {json.dumps(context, indent=2)} - - ## Dependencies - {self._get_dependencies_info()} - - ## Time of Occurrence - {datetime.now().isoformat()} - - --- - *This issue was automatically generated by SwarmsIssueReporter* - """ - - def _get_dependencies_info(self) -> str: - """Get information about installed dependencies.""" - try: - import pkg_resources - - deps = [] - for dist in pkg_resources.working_set: - deps.append(f"- {dist.key} {dist.version}") - return "\n".join(deps) - except: - return "Unable to fetch dependency information" - - # First, add this method to your SwarmsIssueReporter class - def _check_rate_limit(self) -> bool: - """Check if we're within rate limits.""" - now = datetime.now() - time_diff = (now - self.last_issue_time).total_seconds() - - if ( - len(self.issues_created) >= self.rate_limit - and time_diff < self.rate_period - ): - logger.warning("Rate limit exceeded for issue creation") - return False - - # Clean up old issues from tracking - self.issues_created = [ - time - for time in self.issues_created - if (now - time).total_seconds() < self.rate_period - ] - - return True - - def report_swarms_issue( - self, - error: Exception, - agent: Optional[Agent] = None, - context: Dict[str, Any] = None, - priority: str = "normal", - ) -> Optional[int]: - """ - Report a Swarms-specific issue to GitHub. - - Args: - error (Exception): The exception to report - agent (Optional[Agent]): The Swarms agent instance that encountered the error - context (Dict[str, Any]): Additional context about the error - priority (str): Issue priority ("low", "normal", "high", "critical") - - Returns: - Optional[int]: Issue number if created successfully - """ - try: - if not self._check_rate_limit(): - logger.warning( - "Skipping issue creation due to rate limit" - ) - return None - - # Collect system information - system_info = self._get_system_info() - - # Prepare context with agent information if available - full_context = context or {} - if agent: - full_context.update( - { - "agent_name": agent.agent_name, - "agent_description": agent.agent_description, - "max_loops": agent.max_loops, - "context_length": agent.context_length, - } - ) - - # Create issue title - title = f"[{type(error).__name__}] {str(error)[:100]}" - if agent: - title = f"[Agent: {agent.agent_name}] {title}" - - # Get appropriate labels - labels = self._categorize_error(error, full_context) - labels.append(f"priority:{priority}") - - # Create the issue - url = f"https://api.github.com/repos/{self.REPO_OWNER}/{self.REPO_NAME}/issues" - data = { - "title": title, - "body": self._format_swarms_issue_body( - error, system_info, full_context - ), - "labels": labels, - } - - response = requests.post( - url, - headers={ - "Authorization": f"token {self.github_token}" - }, - json=data, - ) - response.raise_for_status() - - issue_number = response.json()["number"] - logger.info( - f"Successfully created Swarms issue #{issue_number}" - ) - - return issue_number - - except Exception as e: - logger.error(f"Error creating Swarms issue: {str(e)}") - return None - - -# Setup the reporter with your GitHub token -reporter = SwarmsIssueReporter( - github_token=os.getenv("GITHUB_API_KEY") -) - - -# Force an error to test the reporter -try: - # This will raise an error since the input isn't valid - # Create an agent that might have issues - model = OpenAIChat(model_name="gpt-4.1") - agent = Agent(agent_name="Test-Agent", max_loops=1) - - result = agent.run(None) - - raise ValueError("test") -except Exception as e: - # Report the issue - issue_number = reporter.report_swarms_issue( - error=e, - agent=agent, - context={"task": "test_run"}, - priority="high", - ) - print(f"Created issue number: {issue_number}") diff --git a/tests/agent/benchmark_agent/test_github_summarizer_agent.py b/tests/agent/benchmark_agent/test_github_summarizer_agent.py deleted file mode 100644 index 6c852b28..00000000 --- a/tests/agent/benchmark_agent/test_github_summarizer_agent.py +++ /dev/null @@ -1,180 +0,0 @@ -import requests -import datetime -from typing import List, Dict, Tuple -from loguru import logger -from swarms import Agent -from swarm_models import OpenAIChat - -# GitHub API Configurations -GITHUB_REPO = "kyegomez/swarms" # Swarms GitHub repository -GITHUB_API_URL = f"https://api.github.com/repos/{GITHUB_REPO}/commits" - - -# Step 1: Fetch the latest commits from GitHub -def fetch_latest_commits( - repo_url: str, limit: int = 5 -) -> List[Dict[str, str]]: - """ - Fetch the latest commits from a public GitHub repository. - """ - logger.info( - f"Fetching the latest {limit} commits from {repo_url}" - ) - try: - params = {"per_page": limit} - response = requests.get(repo_url, params=params) - response.raise_for_status() - - commits = response.json() - commit_data = [] - - for commit in commits: - commit_data.append( - { - "sha": commit["sha"][:7], # Short commit hash - "author": commit["commit"]["author"]["name"], - "message": commit["commit"]["message"], - "date": commit["commit"]["author"]["date"], - } - ) - - logger.success("Successfully fetched commit data") - return commit_data - - except Exception as e: - logger.error(f"Error fetching commits: {e}") - raise - - -# Step 2: Format commits and fetch current time -def format_commits_with_time( - commits: List[Dict[str, str]], -) -> Tuple[str, str]: - """ - Format commit data into a readable string and return current time. - """ - current_time = datetime.datetime.now().strftime( - "%Y-%m-%d %H:%M:%S" - ) - logger.info(f"Formatting commits at {current_time}") - - commit_summary = "\n".join( - [ - f"- `{commit['sha']}` by {commit['author']} on {commit['date']}: {commit['message']}" - for commit in commits - ] - ) - - logger.success("Commits formatted successfully") - return current_time, commit_summary - - -# Step 3: Build a dynamic system prompt -def build_custom_system_prompt( - current_time: str, commit_summary: str -) -> str: - """ - Build a dynamic system prompt with the current time and commit summary. - """ - logger.info("Building the custom system prompt for the agent") - prompt = f""" -You are a software analyst tasked with summarizing the latest commits from the Swarms GitHub repository. - -The current time is **{current_time}**. - -Here are the latest commits: -{commit_summary} - -**Your task**: -1. Summarize the changes into a clear and concise table in **markdown format**. -2. Highlight the key improvements and fixes. -3. End your output with the token ``. - -Make sure the table includes the following columns: Commit SHA, Author, Date, and Commit Message. -""" - logger.success("System prompt created successfully") - return prompt - - -# Step 4: Initialize the Agent -def initialize_agent() -> Agent: - """ - Initialize the Swarms agent with OpenAI model. - """ - logger.info("Initializing the agent with GPT-4o") - model = OpenAIChat(model_name="gpt-4.1") - - agent = Agent( - agent_name="Commit-Summarization-Agent", - agent_description="Fetch and summarize GitHub commits for Swarms repository.", - system_prompt="", # Will set dynamically - max_loops=1, - llm=model, - dynamic_temperature_enabled=True, - user_name="Kye", - retry_attempts=3, - context_length=8192, - return_step_meta=False, - output_type="str", - auto_generate_prompt=False, - max_tokens=4000, - stopping_token="", - interactive=False, - ) - logger.success("Agent initialized successfully") - return agent - - -# Step 5: Run the Agent with Data -def summarize_commits_with_agent(agent: Agent, prompt: str) -> str: - """ - Pass the system prompt to the agent and fetch the result. - """ - logger.info("Sending data to the agent for summarization") - try: - result = agent.run( - f"{prompt}", - all_cores=True, - ) - logger.success("Agent completed the summarization task") - return result - except Exception as e: - logger.error(f"Agent encountered an error: {e}") - raise - - -# Main Execution -if __name__ == "__main__": - try: - logger.info("Starting commit summarization process") - - # Fetch latest commits - latest_commits = fetch_latest_commits(GITHUB_API_URL, limit=5) - - # Format commits and get current time - current_time, commit_summary = format_commits_with_time( - latest_commits - ) - - # Build the custom system prompt - custom_system_prompt = build_custom_system_prompt( - current_time, commit_summary - ) - - # Initialize agent - agent = initialize_agent() - - # Set the dynamic system prompt - agent.system_prompt = custom_system_prompt - - # Run the agent and summarize commits - result = summarize_commits_with_agent( - agent, custom_system_prompt - ) - - # Print the result - print("### Commit Summary in Markdown:") - print(result) - - except Exception as e: - logger.critical(f"Process failed: {e}") diff --git a/tests/agent/benchmark_agent/test_profiling_agent.py b/tests/agent/benchmark_agent/test_profiling_agent.py deleted file mode 100644 index 4b7dbd70..00000000 --- a/tests/agent/benchmark_agent/test_profiling_agent.py +++ /dev/null @@ -1,46 +0,0 @@ -import os -import uuid -from swarms import Agent -from swarm_models import OpenAIChat -from swarms.prompts.finance_agent_sys_prompt import ( - FINANCIAL_AGENT_SYS_PROMPT, -) -import time - -start_time = time.time() - - -# Get the OpenAI API key from the environment variable -api_key = os.getenv("OPENAI_API_KEY") - -# Create an instance of the OpenAIChat class -model = OpenAIChat( - api_key=api_key, model_name="gpt-4o-mini", temperature=0.1 -) - - -agent = Agent( - agent_name=f"{uuid.uuid4().hex}", - system_prompt=FINANCIAL_AGENT_SYS_PROMPT, - llm=model, - max_loops=1, - autosave=True, - dashboard=False, - verbose=True, - dynamic_temperature_enabled=True, - saved_state_path=f"{uuid.uuid4().hex}", - user_name="swarms_corp", - retry_attempts=1, - context_length=3000, - return_step_meta=False, -) - -out = agent.run( - "How can I establish a ROTH IRA to buy stocks and get a tax break? What are the criteria" -) -print(out) - -end_time = time.time() - -print(f"Execution time: {end_time - start_time} seconds") -# Execution time: 9.922541856765747 seconds for the whole script diff --git a/tests/aop/test_data/aop_benchmark_data/Detailed_Bench.xlsx b/tests/aop/test_data/aop_benchmark_data/Detailed_Bench.xlsx deleted file mode 100644 index 1d4c1635..00000000 Binary files a/tests/aop/test_data/aop_benchmark_data/Detailed_Bench.xlsx and /dev/null differ diff --git a/tests/aop/test_data/aop_benchmark_data/bench1.png b/tests/aop/test_data/aop_benchmark_data/bench1.png deleted file mode 100644 index d9be8b42..00000000 Binary files a/tests/aop/test_data/aop_benchmark_data/bench1.png and /dev/null differ diff --git a/tests/aop/test_data/aop_benchmark_data/bench2.png b/tests/aop/test_data/aop_benchmark_data/bench2.png deleted file mode 100644 index dbf8d772..00000000 Binary files a/tests/aop/test_data/aop_benchmark_data/bench2.png and /dev/null differ diff --git a/tests/aop/test_data/aop_benchmark_data/bench3.png b/tests/aop/test_data/aop_benchmark_data/bench3.png deleted file mode 100644 index 0e6badd3..00000000 Binary files a/tests/aop/test_data/aop_benchmark_data/bench3.png and /dev/null differ diff --git a/tests/aop/test_data/aop_benchmark_data/bench4.png b/tests/aop/test_data/aop_benchmark_data/bench4.png deleted file mode 100644 index 0f832ad8..00000000 Binary files a/tests/aop/test_data/aop_benchmark_data/bench4.png and /dev/null differ diff --git a/tests/aop/test_data/aop_benchmark_data/bench5.png b/tests/aop/test_data/aop_benchmark_data/bench5.png deleted file mode 100644 index c712b24d..00000000 Binary files a/tests/aop/test_data/aop_benchmark_data/bench5.png and /dev/null differ diff --git a/tests/aop/test_data/aop_benchmark_data/benchmark_results.csv b/tests/aop/test_data/aop_benchmark_data/benchmark_results.csv deleted file mode 100644 index 495d77a3..00000000 --- a/tests/aop/test_data/aop_benchmark_data/benchmark_results.csv +++ /dev/null @@ -1,91 +0,0 @@ -agent_count,test_name,model_name,latency_ms,throughput_rps,memory_usage_mb,cpu_usage_percent,success_rate,error_count,total_requests,concurrent_requests,timestamp,cost_usd,tokens_used,response_quality_score,additional_metrics,agent_creation_time,tool_registration_time,execution_time,total_latency,chaining_steps,chaining_success,error_scenarios_tested,recovery_rate,resource_cycles,avg_memory_delta,memory_leak_detected -1,scaling_test,gpt-4o-mini,1131.7063331604004,4.131429224630576,1.25,0.0,1.0,0,20,5,1759345643.9453266,0.0015359999999999996,10240,0.8548663728748707,"{'min_latency_ms': 562.7951622009277, 'max_latency_ms': 1780.4391384124756, 'p95_latency_ms': np.float64(1744.0685987472534), 'p99_latency_ms': np.float64(1773.1650304794312), 'total_time_s': 4.84093976020813, 'initial_memory_mb': 291.5546875, 'final_memory_mb': 292.8046875, 'avg_tokens_per_request': 512.0, 'cost_per_request': 7.679999999999998e-05, 'quality_std': 0.0675424923987846, 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'total_time_s': 13.113297462463379, 'initial_memory_mb': 295.0546875, 'final_memory_mb': 295.0546875, 'avg_tokens_per_request': 512.0, 'cost_per_request': 0.000384, 'quality_std': 0.06082135675952047, 'data_size_processed': 1000, 'model_provider': 'gemini', 'iteration': 0}",0.0,0.0,0.0,0.0,0,False,0,0.0,0,0.0,False -5,memory_test,gemini-1.5-flash,1180.0980806350708,4.016049090832003,0.0,0.0,1.0,0,50,5,1759346484.5327744,0.019200000000000002,25600,0.8718428063415768,"{'min_latency_ms': 109.58051681518555, 'max_latency_ms': 1993.358850479126, 'p95_latency_ms': np.float64(1872.3165988922117), 'p99_latency_ms': np.float64(1992.416422367096), 'total_time_s': 12.450047016143799, 'initial_memory_mb': 295.0546875, 'final_memory_mb': 295.0546875, 'avg_tokens_per_request': 512.0, 'cost_per_request': 0.000384, 'quality_std': 0.0613916834940056, 'data_size_processed': 1000, 'model_provider': 'gemini', 'iteration': 1}",0.0,0.0,0.0,0.0,0,False,0,0.0,0,0.0,False 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deleted file mode 100644 index 1da58229..00000000 Binary files a/tests/aop/test_data/image1.jpg and /dev/null differ diff --git a/tests/aop/test_data/image2.png b/tests/aop/test_data/image2.png deleted file mode 100644 index 613d1bd5..00000000 Binary files a/tests/aop/test_data/image2.png and /dev/null differ diff --git a/tests/aop/aop_benchmark.py b/tests/benchmarks/aop_benchmark.py similarity index 100% rename from tests/aop/aop_benchmark.py rename to tests/benchmarks/aop_benchmark.py diff --git a/tests/requirements.txt b/tests/requirements.txt index 16e1cafe..f097d0fb 100644 --- a/tests/requirements.txt +++ b/tests/requirements.txt @@ -1,4 +1,15 @@ swarms pytest matplotlib -loguru \ No newline at end of file +loguru +psutil +pyyaml +python-dotenv +rich +pydantic +numpy +pandas +openpyxl +seaborn +requests +swarms-memory \ No newline at end of file diff --git a/tests/structs/multiagentrouter_models_test.py b/tests/structs/multiagentrouter_models_test.py deleted file mode 100644 index 7ec28bb0..00000000 --- a/tests/structs/multiagentrouter_models_test.py +++ /dev/null @@ -1,64 +0,0 @@ -from swarms.structs.agent import Agent -from swarms.structs.multi_agent_router import MultiAgentRouter - -# Example usage: -agents = [ - Agent( - agent_name="ResearchAgent", - agent_description="Specializes in researching topics and providing detailed, factual information", - system_prompt="You are a research specialist. Provide detailed, well-researched information about any topic, citing sources when possible.", - max_loops=1, - ), - Agent( - agent_name="CodeExpertAgent", - agent_description="Expert in writing, reviewing, and explaining code across multiple programming languages", - system_prompt="You are a coding expert. Write, review, and explain code with a focus on best practices and clean code principles.", - max_loops=1, - ), - Agent( - agent_name="WritingAgent", - agent_description="Skilled in creative and technical writing, content creation, and editing", - system_prompt="You are a writing specialist. Create, edit, and improve written content while maintaining appropriate tone and style.", - max_loops=1, - ), -] - -models_to_test = [ - "gpt-4.1", - "gpt-4o", - "gpt-5-mini", - "o4-mini", - "o3", - "claude-opus-4-20250514", - "claude-sonnet-4-20250514", - "claude-3-7-sonnet-20250219", - "gemini/gemini-2.5-flash", - "gemini/gemini-2.5-pro", -] - -task = "Use all the agents available to you to remake the Fibonacci function in Python, providing both an explanation and code." - -model_logs = [] - -for model_name in models_to_test: - print(f"\n--- Testing model: {model_name} ---") - router_execute = MultiAgentRouter( - agents=agents, - temperature=0.5, - model=model_name, - ) - try: - result = router_execute.run(task) - print(f"Run completed successfully for {model_name}") - model_logs.append( - {"model": model_name, "status": "āœ… Success"} - ) - except Exception as e: - print(f"An error occurred for {model_name}") - model_logs.append( - {"model": model_name, "status": f"āŒ Error: {e}"} - ) - -print("\n===== Model Run Summary =====") -for log in model_logs: - print(f"{log['model']}: {log['status']}") diff --git a/tests/structs/test_agent.py b/tests/structs/test_agent.py index 1c4c7971..417c77c7 100644 --- a/tests/structs/test_agent.py +++ b/tests/structs/test_agent.py @@ -1,1334 +1,2479 @@ +import asyncio import json import os -from unittest import mock +import tempfile +import time +import unittest +from statistics import mean, median, stdev, variance from unittest.mock import MagicMock, patch +import psutil import pytest +import yaml from dotenv import load_dotenv +from rich.console import Console +from rich.table import Table -from swarm_models import OpenAIChat -from swarms.structs.agent import Agent, stop_when_repeats -from swarms.utils.loguru_logger import logger +from swarms import ( + Agent, + create_agents_from_yaml, +) +# Load environment variables load_dotenv() +# Global test configuration openai_api_key = os.getenv("OPENAI_API_KEY") -# Mocks and Fixtures -@pytest.fixture -def mocked_llm(): - return OpenAIChat( - openai_api_key=openai_api_key, - ) +# ============================================================================ +# FIXTURES AND UTILITIES +# ============================================================================ @pytest.fixture def basic_flow(mocked_llm): - return Agent(llm=mocked_llm, max_loops=5) + """Basic agent flow for testing""" + return Agent(llm=mocked_llm, max_loops=1) @pytest.fixture def flow_with_condition(mocked_llm): + """Agent flow with stopping condition""" + from swarms.structs.agent import stop_when_repeats + return Agent( llm=mocked_llm, - max_loops=5, + max_loops=1, stopping_condition=stop_when_repeats, ) -# Basic Tests -def test_stop_when_repeats(): - assert stop_when_repeats("Please Stop now") - assert not stop_when_repeats("Continue the process") - - -def test_flow_initialization(basic_flow): - assert basic_flow.max_loops == 5 - assert basic_flow.stopping_condition is None - assert basic_flow.loop_interval == 1 - assert basic_flow.retry_attempts == 3 - assert basic_flow.retry_interval == 1 - assert basic_flow.feedback == [] - assert basic_flow.memory == [] - assert basic_flow.task is None - assert basic_flow.stopping_token == "" - assert not basic_flow.interactive - - -def test_provide_feedback(basic_flow): - feedback = "Test feedback" - basic_flow.provide_feedback(feedback) - assert feedback in basic_flow.feedback - - -@patch("time.sleep", return_value=None) # to speed up tests -def test_run_without_stopping_condition(mocked_sleep, basic_flow): - response = basic_flow.run("Test task") - assert ( - response == "Test task" - ) # since our mocked llm doesn't modify the response - - -@patch("time.sleep", return_value=None) # to speed up tests -def test_run_with_stopping_condition( - mocked_sleep, flow_with_condition -): - response = flow_with_condition.run("Stop") - assert response == "Stop" - - -@patch("time.sleep", return_value=None) # to speed up tests -def test_run_with_exception(mocked_sleep, basic_flow): - basic_flow.llm.side_effect = Exception("Test Exception") - with pytest.raises(Exception, match="Test Exception"): - basic_flow.run("Test task") - - -def test_bulk_run(basic_flow): - inputs = [{"task": "Test1"}, {"task": "Test2"}] - responses = basic_flow.bulk_run(inputs) - assert responses == ["Test1", "Test2"] - - -# Tests involving file IO -def test_save_and_load(basic_flow, tmp_path): - file_path = tmp_path / "memory.json" - basic_flow.memory.append(["Test1", "Test2"]) - basic_flow.save(file_path) - - new_flow = Agent(llm=mocked_llm, max_loops=5) - new_flow.load(file_path) - assert new_flow.memory == [["Test1", "Test2"]] - - -# Environment variable mock test -def test_env_variable_handling(monkeypatch): - monkeypatch.setenv("API_KEY", "test_key") - assert os.getenv("API_KEY") == "test_key" - - -# TODO: Add more tests, especially edge cases and exception cases. Implement parametrized tests for varied inputs. - - -# Test initializing the agent with different stopping conditions -def test_flow_with_custom_stopping_condition(mocked_llm): - def stopping_condition(x): - return "terminate" in x.lower() - - agent = Agent( - llm=mocked_llm, - max_loops=5, - stopping_condition=stopping_condition, - ) - assert agent.stopping_condition("Please terminate now") - assert not agent.stopping_condition("Continue the process") - - -# Test calling the agent directly -def test_flow_call(basic_flow): - response = basic_flow("Test call") - assert response == "Test call" - - -# Test formatting the prompt -def test_format_prompt(basic_flow): - formatted_prompt = basic_flow.format_prompt( - "Hello {name}", name="John" - ) - assert formatted_prompt == "Hello John" - - -# Test with max loops -@patch("time.sleep", return_value=None) -def test_max_loops(mocked_sleep, basic_flow): - basic_flow.max_loops = 3 - response = basic_flow.run("Looping") - assert response == "Looping" - - -# Test stopping token -@patch("time.sleep", return_value=None) -def test_stopping_token(mocked_sleep, basic_flow): - basic_flow.stopping_token = "Terminate" - response = basic_flow.run("Loop until Terminate") - assert response == "Loop until Terminate" - - -# Test interactive mode -def test_interactive(basic_flow): - basic_flow.interactive = True - assert basic_flow.interactive - - -# Test bulk run with varied inputs -def test_bulk_run_varied_inputs(basic_flow): - inputs = [ - {"task": "Test1"}, - {"task": "Test2"}, - {"task": "Stop now"}, - ] - responses = basic_flow.bulk_run(inputs) - assert responses == ["Test1", "Test2", "Stop now"] - - -# Test loading non-existent file -def test_load_non_existent_file(basic_flow, tmp_path): - file_path = tmp_path / "non_existent.json" - with pytest.raises(FileNotFoundError): - basic_flow.load(file_path) - - -# Test saving with different memory data -def test_save_different_memory(basic_flow, tmp_path): - file_path = tmp_path / "memory.json" - basic_flow.memory.append(["Task1", "Task2", "Task3"]) - basic_flow.save(file_path) - with open(file_path) as f: - data = json.load(f) - assert data == [["Task1", "Task2", "Task3"]] - - -# Test the stopping condition check -def test_check_stopping_condition(flow_with_condition): - assert flow_with_condition._check_stopping_condition( - "Stop this process" - ) - assert not flow_with_condition._check_stopping_condition( - "Continue the task" - ) - - -# Test without providing max loops (default value should be 5) -def test_default_max_loops(mocked_llm): - agent = Agent(llm=mocked_llm) - assert agent.max_loops == 5 - - -# Test creating agent from llm and template -def test_from_llm_and_template(mocked_llm): - agent = Agent.from_llm_and_template(mocked_llm, "Test template") - assert isinstance(agent, Agent) - +@pytest.fixture +def mock_agents(): + """Mock agents for testing""" -# Mocking the OpenAIChat for testing -@patch("swarms.models.OpenAIChat", autospec=True) -def test_mocked_openai_chat(MockedOpenAIChat): - llm = MockedOpenAIChat(openai_api_key=openai_api_key) - llm.return_value = MagicMock() - agent = Agent(llm=llm, max_loops=5) - agent.run("Mocked run") - assert MockedOpenAIChat.called + class MockAgent: + def __init__(self, name): + self.name = name + self.agent_name = name + def run(self, task, img=None, *args, **kwargs): + return f"{self.name} processed {task}" -# Test retry attempts -@patch("time.sleep", return_value=None) -def test_retry_attempts(mocked_sleep, basic_flow): - basic_flow.retry_attempts = 2 - basic_flow.llm.side_effect = [ - Exception("Test Exception"), - "Valid response", + return [ + MockAgent(name="Agent1"), + MockAgent(name="Agent2"), + MockAgent(name="Agent3"), ] - response = basic_flow.run("Test retry") - assert response == "Valid response" - - -# Test different loop intervals -@patch("time.sleep", return_value=None) -def test_different_loop_intervals(mocked_sleep, basic_flow): - basic_flow.loop_interval = 2 - response = basic_flow.run("Test loop interval") - assert response == "Test loop interval" - - -# Test different retry intervals -@patch("time.sleep", return_value=None) -def test_different_retry_intervals(mocked_sleep, basic_flow): - basic_flow.retry_interval = 2 - response = basic_flow.run("Test retry interval") - assert response == "Test retry interval" - - -# Test invoking the agent with additional kwargs -@patch("time.sleep", return_value=None) -def test_flow_call_with_kwargs(mocked_sleep, basic_flow): - response = basic_flow( - "Test call", param1="value1", param2="value2" - ) - assert response == "Test call" - - -# Test initializing the agent with all parameters -def test_flow_initialization_all_params(mocked_llm): - agent = Agent( - llm=mocked_llm, - max_loops=10, - stopping_condition=stop_when_repeats, - loop_interval=2, - retry_attempts=4, - retry_interval=2, - interactive=True, - param1="value1", - param2="value2", - ) - assert agent.max_loops == 10 - assert agent.loop_interval == 2 - assert agent.retry_attempts == 4 - assert agent.retry_interval == 2 - assert agent.interactive - - -# Test the stopping token is in the response -@patch("time.sleep", return_value=None) -def test_stopping_token_in_response(mocked_sleep, basic_flow): - response = basic_flow.run("Test stopping token") - assert basic_flow.stopping_token in response @pytest.fixture -def flow_instance(): - # Create an instance of the Agent class with required parameters for testing - # You may need to adjust this based on your actual class initialization - llm = OpenAIChat( - openai_api_key=openai_api_key, - ) - agent = Agent( - llm=llm, - max_loops=5, - interactive=False, - dashboard=False, - dynamic_temperature=False, - ) - return agent - - -def test_flow_run(flow_instance): - # Test the basic run method of the Agent class - response = flow_instance.run("Test task") - assert isinstance(response, str) - assert len(response) > 0 - - -def test_flow_interactive(flow_instance): - # Test the interactive mode of the Agent class - flow_instance.interactive = True - response = flow_instance.run("Test task") - assert isinstance(response, str) - assert len(response) > 0 - - -def test_flow_dashboard_mode(flow_instance): - # Test the dashboard mode of the Agent class - flow_instance.dashboard = True - response = flow_instance.run("Test task") - assert isinstance(response, str) - assert len(response) > 0 - - -def test_flow_autosave(flow_instance): - # Test the autosave functionality of the Agent class - flow_instance.autosave = True - response = flow_instance.run("Test task") - assert isinstance(response, str) - assert len(response) > 0 - # Ensure that the state is saved (you may need to implement this logic) - assert flow_instance.saved_state_path is not None - - -def test_flow_response_filtering(flow_instance): - # Test the response filtering functionality - flow_instance.add_response_filter("filter_this") - response = flow_instance.filtered_run( - "This message should filter_this" - ) - assert "filter_this" not in response - - -def test_flow_undo_last(flow_instance): - # Test the undo functionality - response1 = flow_instance.run("Task 1") - flow_instance.run("Task 2") - previous_state, message = flow_instance.undo_last() - assert response1 == previous_state - assert "Restored to" in message - - -def test_flow_dynamic_temperature(flow_instance): - # Test dynamic temperature adjustment - flow_instance.dynamic_temperature = True - response = flow_instance.run("Test task") - assert isinstance(response, str) - assert len(response) > 0 - - -def test_flow_streamed_generation(flow_instance): - # Test streamed generation - response = flow_instance.streamed_generation("Generating...") - assert isinstance(response, str) - assert len(response) > 0 - - -def test_flow_step(flow_instance): - # Test the step method - response = flow_instance.step("Test step") - assert isinstance(response, str) - assert len(response) > 0 - - -def test_flow_graceful_shutdown(flow_instance): - # Test graceful shutdown - result = flow_instance.graceful_shutdown() - assert result is not None - - -# Add more test cases as needed to cover various aspects of your Agent class - - -def test_flow_max_loops(flow_instance): - # Test setting and getting the maximum number of loops - flow_instance.set_max_loops(10) - assert flow_instance.get_max_loops() == 10 - - -def test_flow_autosave_path(flow_instance): - # Test setting and getting the autosave path - flow_instance.set_autosave_path("text.txt") - assert flow_instance.get_autosave_path() == "txt.txt" - - -def test_flow_response_length(flow_instance): - # Test checking the length of the response - response = flow_instance.run( - "Generate a 10,000 word long blog on mental clarity and the" - " benefits of meditation." +def test_agent(): + """Create a real agent for testing""" + with patch("swarms.structs.agent.LiteLLM") as mock_llm: + mock_llm.return_value.run.return_value = "Test response" + return Agent( + agent_name="test_agent", + agent_description="A test agent", + system_prompt="You are a test agent", + model_name="gpt-4o-mini", + max_loops=1, + verbose=False, + print_on=False, + ) + + +# ============================================================================ +# BASIC AGENT TESTS +# ============================================================================ + + +class TestBasicAgent: + """Test basic agent functionality""" + + def test_stop_when_repeats(self): + """Test stopping condition function""" + from swarms.structs.agent import stop_when_repeats + + assert stop_when_repeats("Please Stop now") + assert not stop_when_repeats("Continue the process") + + def test_flow_initialization(self, basic_flow): + """Test agent initialization""" + assert basic_flow.max_loops == 5 + assert basic_flow.stopping_condition is None + assert basic_flow.loop_interval == 1 + assert basic_flow.retry_attempts == 3 + assert basic_flow.retry_interval == 1 + assert basic_flow.feedback == [] + assert basic_flow.memory == [] + assert basic_flow.task is None + assert basic_flow.stopping_token == "" + assert not basic_flow.interactive + + def test_provide_feedback(self, basic_flow): + """Test feedback functionality""" + feedback = "Test feedback" + basic_flow.provide_feedback(feedback) + assert feedback in basic_flow.feedback + + @patch("time.sleep", return_value=None) + def test_run_without_stopping_condition( + self, mocked_sleep, basic_flow + ): + """Test running without stopping condition""" + response = basic_flow.run("Test task") + assert response is not None + + @patch("time.sleep", return_value=None) + def test_run_with_stopping_condition( + self, mocked_sleep, flow_with_condition + ): + """Test running with stopping condition""" + response = flow_with_condition.run("Stop") + assert response is not None + + def test_bulk_run(self, basic_flow): + """Test bulk run functionality""" + inputs = [{"task": "Test1"}, {"task": "Test2"}] + responses = basic_flow.bulk_run(inputs) + assert responses is not None + + def test_save_and_load(self, basic_flow, tmp_path): + """Test save and load functionality""" + file_path = tmp_path / "memory.json" + basic_flow.memory.append(["Test1", "Test2"]) + basic_flow.save(file_path) + + new_flow = Agent(llm=basic_flow.llm, max_loops=5) + new_flow.load(file_path) + assert new_flow.memory == [["Test1", "Test2"]] + + def test_flow_call(self, basic_flow): + """Test calling agent directly""" + response = basic_flow("Test call") + assert response == "Test call" + + def test_format_prompt(self, basic_flow): + """Test prompt formatting""" + formatted_prompt = basic_flow.format_prompt( + "Hello {name}", name="John" + ) + assert formatted_prompt == "Hello John" + + +# ============================================================================ +# AGENT FEATURES TESTS +# ============================================================================ + + +class TestAgentFeatures: + """Test advanced agent features""" + + def test_basic_agent_functionality(self): + """Test basic agent initialization and task execution""" + print("\nTesting basic agent functionality...") + + agent = Agent( + agent_name="Test-Agent", model_name="gpt-4.1", max_loops=1 + ) + + response = agent.run("What is 2+2?") + assert ( + response is not None + ), "Agent response should not be None" + + # Test agent properties + assert ( + agent.agent_name == "Test-Agent" + ), "Agent name not set correctly" + assert agent.max_loops == 1, "Max loops not set correctly" + assert agent.llm is not None, "LLM not initialized" + + print("āœ“ Basic agent functionality test passed") + + def test_memory_management(self): + """Test agent memory management functionality""" + print("\nTesting memory management...") + + agent = Agent( + agent_name="Memory-Test-Agent", + max_loops=1, + model_name="gpt-4.1", + context_length=8192, + ) + + # Test adding to memory + agent.add_memory("Test memory entry") + assert ( + "Test memory entry" + in agent.short_memory.return_history_as_string() + ) + + # Test memory query + agent.memory_query("Test query") + + # Test token counting + tokens = agent.check_available_tokens() + assert isinstance( + tokens, int + ), "Token count should be an integer" + + print("āœ“ Memory management test passed") + + def test_agent_output_formats(self): + """Test all available output formats""" + print("\nTesting all output formats...") + + test_task = "Say hello!" + + output_types = { + "str": str, + "string": str, + "list": str, # JSON string containing list + "json": str, # JSON string + "dict": dict, + "yaml": str, + } + + for output_type, expected_type in output_types.items(): + agent = Agent( + agent_name=f"{output_type.capitalize()}-Output-Agent", + model_name="gpt-4.1", + max_loops=1, + output_type=output_type, + ) + + response = agent.run(test_task) + assert ( + response is not None + ), f"{output_type} output should not be None" + + if output_type == "yaml": + # Verify YAML can be parsed + try: + yaml.safe_load(response) + print(f"āœ“ {output_type} output valid") + except yaml.YAMLError: + assert ( + False + ), f"Invalid YAML output for {output_type}" + elif output_type in ["json", "list"]: + # Verify JSON can be parsed + try: + json.loads(response) + print(f"āœ“ {output_type} output valid") + except json.JSONDecodeError: + assert ( + False + ), f"Invalid JSON output for {output_type}" + + print("āœ“ Output formats test passed") + + def test_agent_state_management(self): + """Test comprehensive state management functionality""" + print("\nTesting state management...") + + # Create temporary directory for test files + with tempfile.TemporaryDirectory() as temp_dir: + state_path = os.path.join(temp_dir, "agent_state.json") + + # Create agent with initial state + agent1 = Agent( + agent_name="State-Test-Agent", + model_name="gpt-4.1", + max_loops=1, + saved_state_path=state_path, + ) + + # Add some data to the agent + agent1.run("Remember this: Test message 1") + agent1.add_memory("Test message 2") + + # Save state + agent1.save() + assert os.path.exists( + state_path + ), "State file not created" + + # Create new agent and load state + agent2 = Agent( + agent_name="State-Test-Agent", + model_name="gpt-4.1", + max_loops=1, + ) + agent2.load(state_path) + + # Verify state loaded correctly + history2 = agent2.short_memory.return_history_as_string() + assert ( + "Test message 1" in history2 + ), "State not loaded correctly" + assert ( + "Test message 2" in history2 + ), "Memory not loaded correctly" + + # Test autosave functionality + agent3 = Agent( + agent_name="Autosave-Test-Agent", + model_name="gpt-4.1", + max_loops=1, + saved_state_path=os.path.join( + temp_dir, "autosave_state.json" + ), + autosave=True, + ) + + agent3.run("Test autosave") + time.sleep(2) # Wait for autosave + assert os.path.exists( + os.path.join(temp_dir, "autosave_state.json") + ), "Autosave file not created" + + print("āœ“ State management test passed") + + def test_agent_tools_and_execution(self): + """Test agent tool handling and execution""" + print("\nTesting tools and execution...") + + def sample_tool(x: int, y: int) -> int: + """Sample tool that adds two numbers""" + return x + y + + agent = Agent( + agent_name="Tools-Test-Agent", + model_name="gpt-4.1", + max_loops=1, + tools=[sample_tool], + ) + + # Test adding tools + agent.add_tool(lambda x: x * 2) + assert len(agent.tools) == 2, "Tool not added correctly" + + # Test removing tools + agent.remove_tool(sample_tool) + assert len(agent.tools) == 1, "Tool not removed correctly" + + # Test tool execution + response = agent.run("Calculate 2 + 2 using the sample tool") + assert response is not None, "Tool execution failed" + + print("āœ“ Tools and execution test passed") + + def test_agent_concurrent_execution(self): + """Test agent concurrent execution capabilities""" + print("\nTesting concurrent execution...") + + agent = Agent( + agent_name="Concurrent-Test-Agent", + model_name="gpt-4.1", + max_loops=1, + ) + + # Test bulk run + tasks = [ + {"task": "Count to 3"}, + {"task": "Say hello"}, + {"task": "Tell a short joke"}, + ] + + responses = agent.bulk_run(tasks) + assert len(responses) == len(tasks), "Not all tasks completed" + assert all( + response is not None for response in responses + ), "Some tasks failed" + + # Test concurrent tasks + concurrent_responses = agent.run_concurrent_tasks( + ["Task 1", "Task 2", "Task 3"] + ) + assert ( + len(concurrent_responses) == 3 + ), "Not all concurrent tasks completed" + + print("āœ“ Concurrent execution test passed") + + def test_agent_error_handling(self): + """Test agent error handling and recovery""" + print("\nTesting error handling...") + + agent = Agent( + agent_name="Error-Test-Agent", + model_name="gpt-4.1", + max_loops=1, + retry_attempts=3, + retry_interval=1, + ) + + # Test invalid tool execution + try: + agent.parse_and_execute_tools("invalid_json") + print("āœ“ Invalid tool execution handled") + except Exception: + assert True, "Expected error caught" + + # Test recovery after error + response = agent.run("Continue after error") + assert ( + response is not None + ), "Agent failed to recover after error" + + print("āœ“ Error handling test passed") + + def test_agent_configuration(self): + """Test agent configuration and parameters""" + print("\nTesting agent configuration...") + + agent = Agent( + agent_name="Config-Test-Agent", + model_name="gpt-4.1", + max_loops=1, + temperature=0.7, + max_tokens=4000, + context_length=8192, + ) + + # Test configuration methods + agent.update_system_prompt("New system prompt") + agent.update_max_loops(2) + agent.update_loop_interval(2) + + # Verify updates + assert agent.max_loops == 2, "Max loops not updated" + assert agent.loop_interval == 2, "Loop interval not updated" + + # Test configuration export + config_dict = agent.to_dict() + assert isinstance( + config_dict, dict + ), "Configuration export failed" + + # Test YAML export + yaml_config = agent.to_yaml() + assert isinstance(yaml_config, str), "YAML export failed" + + print("āœ“ Configuration test passed") + + def test_agent_with_stopping_condition(self): + """Test agent with custom stopping condition""" + print("\nTesting agent with stopping condition...") + + def custom_stopping_condition(response: str) -> bool: + return "STOP" in response.upper() + + agent = Agent( + agent_name="Stopping-Condition-Agent", + model_name="gpt-4.1", + max_loops=1, + stopping_condition=custom_stopping_condition, + ) + + response = agent.run("Count up until you see the word STOP") + assert response is not None, "Stopping condition test failed" + print("āœ“ Stopping condition test passed") + + def test_agent_with_retry_mechanism(self): + """Test agent retry mechanism""" + print("\nTesting agent retry mechanism...") + + agent = Agent( + agent_name="Retry-Test-Agent", + model_name="gpt-4.1", + max_loops=1, + retry_attempts=3, + retry_interval=1, + ) + + response = agent.run("Tell me a joke.") + assert response is not None, "Retry mechanism test failed" + print("āœ“ Retry mechanism test passed") + + def test_bulk_and_filtered_operations(self): + """Test bulk operations and response filtering""" + print("\nTesting bulk and filtered operations...") + + agent = Agent( + agent_name="Bulk-Filter-Test-Agent", + model_name="gpt-4.1", + max_loops=1, + ) + + # Test bulk run + bulk_tasks = [ + {"task": "What is 2+2?"}, + {"task": "Name a color"}, + {"task": "Count to 3"}, + ] + bulk_responses = agent.bulk_run(bulk_tasks) + assert len(bulk_responses) == len( + bulk_tasks + ), "Bulk run should return same number of responses as tasks" + + # Test response filtering + agent.add_response_filter("color") + filtered_response = agent.filtered_run( + "What is your favorite color?" + ) + assert ( + "[FILTERED]" in filtered_response + ), "Response filter not applied" + + print("āœ“ Bulk and filtered operations test passed") + + async def test_async_operations(self): + """Test asynchronous operations""" + print("\nTesting async operations...") + + agent = Agent( + agent_name="Async-Test-Agent", + model_name="gpt-4.1", + max_loops=1, + ) + + # Test single async run + response = await agent.arun("What is 1+1?") + assert response is not None, "Async run failed" + + # Test concurrent async runs + tasks = ["Task 1", "Task 2", "Task 3"] + responses = await asyncio.gather( + *[agent.arun(task) for task in tasks] + ) + assert len(responses) == len( + tasks + ), "Not all async tasks completed" + + print("āœ“ Async operations test passed") + + def test_memory_and_state_persistence(self): + """Test memory management and state persistence""" + print("\nTesting memory and state persistence...") + + with tempfile.TemporaryDirectory() as temp_dir: + state_path = os.path.join(temp_dir, "test_state.json") + + # Create agent with memory configuration + agent1 = Agent( + agent_name="Memory-State-Test-Agent", + model_name="gpt-4.1", + max_loops=1, + saved_state_path=state_path, + context_length=8192, + autosave=True, + ) + + # Test memory operations + agent1.add_memory("Important fact: The sky is blue") + agent1.memory_query("What color is the sky?") + + # Save state + agent1.save() + + # Create new agent and load state + agent2 = Agent( + agent_name="Memory-State-Test-Agent", + model_name="gpt-4.1", + max_loops=1, + ) + agent2.load(state_path) + + # Verify memory persistence + memory_content = ( + agent2.short_memory.return_history_as_string() + ) + assert ( + "sky is blue" in memory_content + ), "Memory not properly persisted" + + print("āœ“ Memory and state persistence test passed") + + def test_sentiment_and_evaluation(self): + """Test sentiment analysis and response evaluation""" + print("\nTesting sentiment analysis and evaluation...") + + def mock_sentiment_analyzer(text): + """Mock sentiment analyzer that returns a score between 0 and 1""" + return 0.7 if "positive" in text.lower() else 0.3 + + def mock_evaluator(response): + """Mock evaluator that checks response quality""" + return "GOOD" if len(response) > 10 else "BAD" + + agent = Agent( + agent_name="Sentiment-Eval-Test-Agent", + model_name="gpt-4.1", + max_loops=1, + sentiment_analyzer=mock_sentiment_analyzer, + sentiment_threshold=0.5, + evaluator=mock_evaluator, + ) + + # Test sentiment analysis + agent.run("Generate a positive message") + + # Test evaluation + agent.run("Generate a detailed response") + + print("āœ“ Sentiment and evaluation test passed") + + def test_tool_management(self): + """Test tool management functionality""" + print("\nTesting tool management...") + + def tool1(x: int) -> int: + """Sample tool 1""" + return x * 2 + + def tool2(x: int) -> int: + """Sample tool 2""" + return x + 2 + + agent = Agent( + agent_name="Tool-Test-Agent", + model_name="gpt-4.1", + max_loops=1, + tools=[tool1], + ) + + # Test adding tools + agent.add_tool(tool2) + assert len(agent.tools) == 2, "Tool not added correctly" + + # Test removing tools + agent.remove_tool(tool1) + assert len(agent.tools) == 1, "Tool not removed correctly" + + # Test adding multiple tools + agent.add_tools([tool1, tool2]) + assert ( + len(agent.tools) == 3 + ), "Multiple tools not added correctly" + + print("āœ“ Tool management test passed") + + def test_system_prompt_and_configuration(self): + """Test system prompt and configuration updates""" + print("\nTesting system prompt and configuration...") + + agent = Agent( + agent_name="Config-Test-Agent", + model_name="gpt-4.1", + max_loops=1, + ) + + # Test updating system prompt + new_prompt = "You are a helpful assistant." + agent.update_system_prompt(new_prompt) + assert ( + agent.system_prompt == new_prompt + ), "System prompt not updated" + + # Test configuration updates + agent.update_max_loops(5) + assert agent.max_loops == 5, "Max loops not updated" + + agent.update_loop_interval(2) + assert agent.loop_interval == 2, "Loop interval not updated" + + # Test configuration export + config_dict = agent.to_dict() + assert isinstance( + config_dict, dict + ), "Configuration export failed" + + print("āœ“ System prompt and configuration test passed") + + def test_agent_with_dynamic_temperature(self): + """Test agent with dynamic temperature""" + print("\nTesting agent with dynamic temperature...") + + agent = Agent( + agent_name="Dynamic-Temp-Agent", + model_name="gpt-4.1", + max_loops=2, + dynamic_temperature_enabled=True, + ) + + response = agent.run("Generate a creative story.") + assert response is not None, "Dynamic temperature test failed" + print("āœ“ Dynamic temperature test passed") + + +# ============================================================================ +# AGENT LOGGING TESTS +# ============================================================================ + + +class TestAgentLogging: + """Test agent logging functionality""" + + def setUp(self): + """Set up test fixtures""" + self.mock_tokenizer = MagicMock() + self.mock_tokenizer.count_tokens.return_value = 100 + + self.mock_short_memory = MagicMock() + self.mock_short_memory.get_memory_stats.return_value = { + "message_count": 2 + } + + self.mock_long_memory = MagicMock() + self.mock_long_memory.get_memory_stats.return_value = { + "item_count": 5 + } + + self.agent = Agent( + tokenizer=self.mock_tokenizer, + short_memory=self.mock_short_memory, + long_term_memory=self.mock_long_memory, + ) + + def test_log_step_metadata_basic(self): + """Test basic step metadata logging""" + log_result = self.agent.log_step_metadata( + 1, "Test prompt", "Test response" + ) + + assert "step_id" in log_result + assert "timestamp" in log_result + assert "tokens" in log_result + assert "memory_usage" in log_result + + assert log_result["tokens"]["total"] == 200 + + def test_log_step_metadata_no_long_term_memory(self): + """Test step metadata logging without long term memory""" + self.agent.long_term_memory = None + log_result = self.agent.log_step_metadata( + 1, "prompt", "response" + ) + assert log_result["memory_usage"]["long_term"] == {} + + def test_log_step_metadata_timestamp(self): + """Test step metadata logging timestamp""" + log_result = self.agent.log_step_metadata( + 1, "prompt", "response" + ) + assert "timestamp" in log_result + + def test_token_counting_integration(self): + """Test token counting integration""" + self.mock_tokenizer.count_tokens.side_effect = [150, 250] + log_result = self.agent.log_step_metadata( + 1, "prompt", "response" + ) + + assert log_result["tokens"]["total"] == 400 + + def test_agent_output_updating(self): + """Test agent output updating""" + initial_total_tokens = sum( + step["tokens"]["total"] + for step in self.agent.agent_output.steps + ) + self.agent.log_step_metadata(1, "prompt", "response") + + final_total_tokens = sum( + step["tokens"]["total"] + for step in self.agent.agent_output.steps + ) + assert final_total_tokens - initial_total_tokens == 200 + assert len(self.agent.agent_output.steps) == 1 + + def test_full_logging_cycle(self): + """Test full logging cycle""" + agent = Agent(agent_name="test-agent") + task = "Test task" + max_loops = 1 + + result = agent._run(task, max_loops=max_loops) + + assert isinstance(result, dict) + assert "steps" in result + assert isinstance(result["steps"], list) + assert len(result["steps"]) == max_loops + + if result["steps"]: + step = result["steps"][0] + assert "step_id" in step + assert "timestamp" in step + assert "task" in step + assert "response" in step + assert step["task"] == task + assert step["response"] == "Response for loop 1" + + assert len(self.agent.agent_output.steps) > 0 + + +# ============================================================================ +# YAML AGENT CREATION TESTS +# ============================================================================ + + +class TestCreateAgentsFromYaml: + """Test YAML agent creation functionality""" + + def setUp(self): + """Set up test fixtures""" + # Mock the environment variable for API key + os.environ["OPENAI_API_KEY"] = "fake-api-key" + + # Mock agent configuration YAML content + self.valid_yaml_content = """ + agents: + - agent_name: "Financial-Analysis-Agent" + model: + openai_api_key: "fake-api-key" + model_name: "gpt-4o-mini" + temperature: 0.1 + max_tokens: 2000 + system_prompt: "financial_agent_sys_prompt" + max_loops: 1 + autosave: true + dashboard: false + verbose: true + dynamic_temperature_enabled: true + saved_state_path: "finance_agent.json" + user_name: "swarms_corp" + retry_attempts: 1 + context_length: 200000 + return_step_meta: false + output_type: "str" + task: "How can I establish a ROTH IRA to buy stocks and get a tax break?" + """ + + @patch( + "builtins.open", + new_callable=unittest.mock.mock_open, + read_data="", ) - assert ( - len(response) > flow_instance.get_response_length_threshold() + @patch("yaml.safe_load") + def test_create_agents_return_agents( + self, mock_safe_load, mock_open + ): + """Test creating agents from YAML and returning agents""" + # Mock YAML content parsing + mock_safe_load.return_value = { + "agents": [ + { + "agent_name": "Financial-Analysis-Agent", + "model": { + "openai_api_key": "fake-api-key", + "model_name": "gpt-4o-mini", + "temperature": 0.1, + "max_tokens": 2000, + }, + "system_prompt": "financial_agent_sys_prompt", + "max_loops": 1, + "autosave": True, + "dashboard": False, + "verbose": True, + "dynamic_temperature_enabled": True, + "saved_state_path": "finance_agent.json", + "user_name": "swarms_corp", + "retry_attempts": 1, + "context_length": 200000, + "return_step_meta": False, + "output_type": "str", + "task": "How can I establish a ROTH IRA to buy stocks and get a tax break?", + } + ] + } + + # Test if agents are returned correctly + agents = create_agents_from_yaml( + "fake_yaml_path.yaml", return_type="agents" + ) + assert len(agents) == 1 + assert agents[0].agent_name == "Financial-Analysis-Agent" + + @patch( + "builtins.open", + new_callable=unittest.mock.mock_open, + read_data="", ) - - -def test_flow_set_response_length_threshold(flow_instance): - # Test setting and getting the response length threshold - flow_instance.set_response_length_threshold(100) - assert flow_instance.get_response_length_threshold() == 100 - - -def test_flow_add_custom_filter(flow_instance): - # Test adding a custom response filter - flow_instance.add_response_filter("custom_filter") - assert "custom_filter" in flow_instance.get_response_filters() - - -def test_flow_remove_custom_filter(flow_instance): - # Test removing a custom response filter - flow_instance.add_response_filter("custom_filter") - flow_instance.remove_response_filter("custom_filter") - assert "custom_filter" not in flow_instance.get_response_filters() - - -def test_flow_dynamic_pacing(flow_instance): - # Test dynamic pacing - flow_instance.enable_dynamic_pacing() - assert flow_instance.is_dynamic_pacing_enabled() is True - - -def test_flow_disable_dynamic_pacing(flow_instance): - # Test disabling dynamic pacing - flow_instance.disable_dynamic_pacing() - assert flow_instance.is_dynamic_pacing_enabled() is False - - -def test_flow_change_prompt(flow_instance): - # Test changing the current prompt - flow_instance.change_prompt("New prompt") - assert flow_instance.get_current_prompt() == "New prompt" - - -def test_flow_add_instruction(flow_instance): - # Test adding an instruction to the conversation - flow_instance.add_instruction("Follow these steps:") - assert "Follow these steps:" in flow_instance.get_instructions() - - -def test_flow_clear_instructions(flow_instance): - # Test clearing all instructions from the conversation - flow_instance.add_instruction("Follow these steps:") - flow_instance.clear_instructions() - assert len(flow_instance.get_instructions()) == 0 - - -def test_flow_add_user_message(flow_instance): - # Test adding a user message to the conversation - flow_instance.add_user_message("User message") - assert "User message" in flow_instance.get_user_messages() - - -def test_flow_clear_user_messages(flow_instance): - # Test clearing all user messages from the conversation - flow_instance.add_user_message("User message") - flow_instance.clear_user_messages() - assert len(flow_instance.get_user_messages()) == 0 - - -def test_flow_get_response_history(flow_instance): - # Test getting the response history - flow_instance.run("Message 1") - flow_instance.run("Message 2") - history = flow_instance.get_response_history() - assert len(history) == 2 - assert "Message 1" in history[0] - assert "Message 2" in history[1] - - -def test_flow_clear_response_history(flow_instance): - # Test clearing the response history - flow_instance.run("Message 1") - flow_instance.run("Message 2") - flow_instance.clear_response_history() - assert len(flow_instance.get_response_history()) == 0 - - -def test_flow_get_conversation_log(flow_instance): - # Test getting the entire conversation log - flow_instance.run("Message 1") - flow_instance.run("Message 2") - conversation_log = flow_instance.get_conversation_log() - assert ( - len(conversation_log) == 4 - ) # Including system and user messages - - -def test_flow_clear_conversation_log(flow_instance): - # Test clearing the entire conversation log - flow_instance.run("Message 1") - flow_instance.run("Message 2") - flow_instance.clear_conversation_log() - assert len(flow_instance.get_conversation_log()) == 0 - - -def test_flow_get_state(flow_instance): - # Test getting the current state of the Agent instance - state = flow_instance.get_state() - assert isinstance(state, dict) - assert "current_prompt" in state - assert "instructions" in state - assert "user_messages" in state - assert "response_history" in state - assert "conversation_log" in state - assert "dynamic_pacing_enabled" in state - assert "response_length_threshold" in state - assert "response_filters" in state - assert "max_loops" in state - assert "autosave_path" in state - - -def test_flow_load_state(flow_instance): - # Test loading the state into the Agent instance - state = { - "current_prompt": "Loaded prompt", - "instructions": ["Step 1", "Step 2"], - "user_messages": ["User message 1", "User message 2"], - "response_history": ["Response 1", "Response 2"], - "conversation_log": [ - "System message 1", - "User message 1", - "System message 2", - "User message 2", - ], - "dynamic_pacing_enabled": True, - "response_length_threshold": 50, - "response_filters": ["filter1", "filter2"], - "max_loops": 10, - "autosave_path": "/path/to/load", - } - flow_instance.load(state) - assert flow_instance.get_current_prompt() == "Loaded prompt" - assert "Step 1" in flow_instance.get_instructions() - assert "User message 1" in flow_instance.get_user_messages() - assert "Response 1" in flow_instance.get_response_history() - assert "System message 1" in flow_instance.get_conversation_log() - assert flow_instance.is_dynamic_pacing_enabled() is True - assert flow_instance.get_response_length_threshold() == 50 - assert "filter1" in flow_instance.get_response_filters() - assert flow_instance.get_max_loops() == 10 - assert flow_instance.get_autosave_path() == "/path/to/load" - - -def test_flow_save_state(flow_instance): - # Test saving the state of the Agent instance - flow_instance.change_prompt("New prompt") - flow_instance.add_instruction("Step 1") - flow_instance.add_user_message("User message") - flow_instance.run("Response") - state = flow_instance.save_state() - assert "current_prompt" in state - assert "instructions" in state - assert "user_messages" in state - assert "response_history" in state - assert "conversation_log" in state - assert "dynamic_pacing_enabled" in state - assert "response_length_threshold" in state - assert "response_filters" in state - assert "max_loops" in state - assert "autosave_path" in state - - -def test_flow_rollback(flow_instance): - # Test rolling back to a previous state - state1 = flow_instance.get_state() - flow_instance.change_prompt("New prompt") - flow_instance.get_state() - flow_instance.rollback_to_state(state1) - assert ( - flow_instance.get_current_prompt() == state1["current_prompt"] - ) - assert flow_instance.get_instructions() == state1["instructions"] - assert ( - flow_instance.get_user_messages() == state1["user_messages"] - ) - assert ( - flow_instance.get_response_history() - == state1["response_history"] + @patch("yaml.safe_load") + @patch( + "swarms.Agent.run", return_value="Task completed successfully" ) - assert ( - flow_instance.get_conversation_log() - == state1["conversation_log"] + def test_create_agents_return_tasks( + self, mock_agent_run, mock_safe_load, mock_open + ): + """Test creating agents from YAML and returning task results""" + # Mock YAML content parsing + mock_safe_load.return_value = { + "agents": [ + { + "agent_name": "Financial-Analysis-Agent", + "model": { + "openai_api_key": "fake-api-key", + "model_name": "gpt-4o-mini", + "temperature": 0.1, + "max_tokens": 2000, + }, + "system_prompt": "financial_agent_sys_prompt", + "max_loops": 1, + "autosave": True, + "dashboard": False, + "verbose": True, + "dynamic_temperature_enabled": True, + "saved_state_path": "finance_agent.json", + "user_name": "swarms_corp", + "retry_attempts": 1, + "context_length": 200000, + "return_step_meta": False, + "output_type": "str", + "task": "How can I establish a ROTH IRA to buy stocks and get a tax break?", + } + ] + } + + # Test if tasks are executed and results are returned + task_results = create_agents_from_yaml( + "fake_yaml_path.yaml", return_type="tasks" + ) + assert len(task_results) == 1 + assert ( + task_results[0]["agent_name"] + == "Financial-Analysis-Agent" + ) + assert task_results[0]["output"] is not None + + @patch( + "builtins.open", + new_callable=unittest.mock.mock_open, + read_data="", ) - assert ( - flow_instance.is_dynamic_pacing_enabled() - == state1["dynamic_pacing_enabled"] + @patch("yaml.safe_load") + def test_create_agents_return_both( + self, mock_safe_load, mock_open + ): + """Test creating agents from YAML and returning both agents and tasks""" + # Mock YAML content parsing + mock_safe_load.return_value = { + "agents": [ + { + "agent_name": "Financial-Analysis-Agent", + "model": { + "openai_api_key": "fake-api-key", + "model_name": "gpt-4o-mini", + "temperature": 0.1, + "max_tokens": 2000, + }, + "system_prompt": "financial_agent_sys_prompt", + "max_loops": 1, + "autosave": True, + "dashboard": False, + "verbose": True, + "dynamic_temperature_enabled": True, + "saved_state_path": "finance_agent.json", + "user_name": "swarms_corp", + "retry_attempts": 1, + "context_length": 200000, + "return_step_meta": False, + "output_type": "str", + "task": "How can I establish a ROTH IRA to buy stocks and get a tax break?", + } + ] + } + + # Test if both agents and tasks are returned + agents, task_results = create_agents_from_yaml( + "fake_yaml_path.yaml", return_type="both" + ) + assert len(agents) == 1 + assert len(task_results) == 1 + assert agents[0].agent_name == "Financial-Analysis-Agent" + assert task_results[0]["output"] is not None + + @patch( + "builtins.open", + new_callable=unittest.mock.mock_open, + read_data="", ) - assert ( - flow_instance.get_response_length_threshold() - == state1["response_length_threshold"] + @patch("yaml.safe_load") + def test_missing_agents_in_yaml(self, mock_safe_load, mock_open): + """Test handling missing agents in YAML""" + # Mock YAML content with missing "agents" key + mock_safe_load.return_value = {} + + # Test if the function raises an error for missing "agents" key + with pytest.raises(ValueError) as context: + create_agents_from_yaml( + "fake_yaml_path.yaml", return_type="agents" + ) + assert ( + "The YAML configuration does not contain 'agents'." + in str(context.exception) + ) + + @patch( + "builtins.open", + new_callable=unittest.mock.mock_open, + read_data="", ) - assert ( - flow_instance.get_response_filters() - == state1["response_filters"] - ) - assert flow_instance.get_max_loops() == state1["max_loops"] - assert ( - flow_instance.get_autosave_path() == state1["autosave_path"] - ) - assert flow_instance.get_state() == state1 - - -def test_flow_contextual_intent(flow_instance): - # Test contextual intent handling - flow_instance.add_context("location", "New York") - flow_instance.add_context("time", "tomorrow") - response = flow_instance.run( - "What's the weather like in {location} at {time}?" - ) - assert "New York" in response - assert "tomorrow" in response - - -def test_flow_contextual_intent_override(flow_instance): - # Test contextual intent override - flow_instance.add_context("location", "New York") - response1 = flow_instance.run( - "What's the weather like in {location}?" - ) - flow_instance.add_context("location", "Los Angeles") - response2 = flow_instance.run( - "What's the weather like in {location}?" - ) - assert "New York" in response1 - assert "Los Angeles" in response2 - - -def test_flow_contextual_intent_reset(flow_instance): - # Test resetting contextual intent - flow_instance.add_context("location", "New York") - response1 = flow_instance.run( - "What's the weather like in {location}?" - ) - flow_instance.reset_context() - response2 = flow_instance.run( - "What's the weather like in {location}?" - ) - assert "New York" in response1 - assert "New York" in response2 - - -# Add more test cases as needed to cover various aspects of your Agent class -def test_flow_interruptible(flow_instance): - # Test interruptible mode - flow_instance.interruptible = True - response = flow_instance.run("Interrupt me!") - assert "Interrupted" in response - assert flow_instance.is_interrupted() is True - - -def test_flow_non_interruptible(flow_instance): - # Test non-interruptible mode - flow_instance.interruptible = False - response = flow_instance.run("Do not interrupt me!") - assert "Do not interrupt me!" in response - assert flow_instance.is_interrupted() is False - - -def test_flow_timeout(flow_instance): - # Test conversation timeout - flow_instance.timeout = 60 # Set a timeout of 60 seconds - response = flow_instance.run( - "This should take some time to respond." - ) - assert "Timed out" in response - assert flow_instance.is_timed_out() is True - - -def test_flow_no_timeout(flow_instance): - # Test no conversation timeout - flow_instance.timeout = None - response = flow_instance.run("This should not time out.") - assert "This should not time out." in response - assert flow_instance.is_timed_out() is False - - -def test_flow_custom_delimiter(flow_instance): - # Test setting and getting a custom message delimiter - flow_instance.set_message_delimiter("|||") - assert flow_instance.get_message_delimiter() == "|||" - - -def test_flow_message_history(flow_instance): - # Test getting the message history - flow_instance.run("Message 1") - flow_instance.run("Message 2") - history = flow_instance.get_message_history() - assert len(history) == 2 - assert "Message 1" in history[0] - assert "Message 2" in history[1] - - -def test_flow_clear_message_history(flow_instance): - # Test clearing the message history - flow_instance.run("Message 1") - flow_instance.run("Message 2") - flow_instance.clear_message_history() - assert len(flow_instance.get_message_history()) == 0 - - -def test_flow_save_and_load_conversation(flow_instance): - # Test saving and loading the conversation - flow_instance.run("Message 1") - flow_instance.run("Message 2") - saved_conversation = flow_instance.save_conversation() - flow_instance.clear_conversation() - flow_instance.load_conversation(saved_conversation) - assert len(flow_instance.get_message_history()) == 2 - - -def test_flow_inject_custom_system_message(flow_instance): - # Test injecting a custom system message into the conversation - flow_instance.inject_custom_system_message( - "Custom system message" - ) - assert ( - "Custom system message" in flow_instance.get_message_history() - ) - - -def test_flow_inject_custom_user_message(flow_instance): - # Test injecting a custom user message into the conversation - flow_instance.inject_custom_user_message("Custom user message") - assert ( - "Custom user message" in flow_instance.get_message_history() - ) - - -def test_flow_inject_custom_response(flow_instance): - # Test injecting a custom response into the conversation - flow_instance.inject_custom_response("Custom response") - assert "Custom response" in flow_instance.get_message_history() - - -def test_flow_clear_injected_messages(flow_instance): - # Test clearing injected messages from the conversation - flow_instance.inject_custom_system_message( - "Custom system message" - ) - flow_instance.inject_custom_user_message("Custom user message") - flow_instance.inject_custom_response("Custom response") - flow_instance.clear_injected_messages() - assert ( - "Custom system message" - not in flow_instance.get_message_history() - ) - assert ( - "Custom user message" - not in flow_instance.get_message_history() - ) - assert ( - "Custom response" not in flow_instance.get_message_history() - ) - - -def test_flow_disable_message_history(flow_instance): - # Test disabling message history recording - flow_instance.disable_message_history() - response = flow_instance.run( - "This message should not be recorded in history." - ) - assert ( - "This message should not be recorded in history." in response - ) - assert ( - len(flow_instance.get_message_history()) == 0 - ) # History is empty - - -def test_flow_enable_message_history(flow_instance): - # Test enabling message history recording - flow_instance.enable_message_history() - response = flow_instance.run( - "This message should be recorded in history." - ) - assert "This message should be recorded in history." in response - assert len(flow_instance.get_message_history()) == 1 - - -def test_flow_custom_logger(flow_instance): - # Test setting and using a custom logger - custom_logger = logger # Replace with your custom logger class - flow_instance.set_logger(custom_logger) - response = flow_instance.run("Custom logger test") - assert ( - "Logged using custom logger" in response - ) # Verify logging message - - -def test_flow_batch_processing(flow_instance): - # Test batch processing of messages - messages = ["Message 1", "Message 2", "Message 3"] - responses = flow_instance.process_batch(messages) - assert isinstance(responses, list) - assert len(responses) == len(messages) - for response in responses: - assert isinstance(response, str) - - -def test_flow_custom_metrics(flow_instance): - # Test tracking custom metrics - flow_instance.track_custom_metric("custom_metric_1", 42) - flow_instance.track_custom_metric("custom_metric_2", 3.14) - metrics = flow_instance.get_custom_metrics() - assert "custom_metric_1" in metrics - assert "custom_metric_2" in metrics - assert metrics["custom_metric_1"] == 42 - assert metrics["custom_metric_2"] == 3.14 - - -def test_flow_reset_metrics(flow_instance): - # Test resetting custom metrics - flow_instance.track_custom_metric("custom_metric_1", 42) - flow_instance.track_custom_metric("custom_metric_2", 3.14) - flow_instance.reset_custom_metrics() - metrics = flow_instance.get_custom_metrics() - assert len(metrics) == 0 - - -def test_flow_retrieve_context(flow_instance): - # Test retrieving context - flow_instance.add_context("location", "New York") - context = flow_instance.get_context("location") - assert context == "New York" - - -def test_flow_update_context(flow_instance): - # Test updating context - flow_instance.add_context("location", "New York") - flow_instance.update_context("location", "Los Angeles") - context = flow_instance.get_context("location") - assert context == "Los Angeles" - - -def test_flow_remove_context(flow_instance): - # Test removing context - flow_instance.add_context("location", "New York") - flow_instance.remove_context("location") - context = flow_instance.get_context("location") - assert context is None - - -def test_flow_clear_context(flow_instance): - # Test clearing all context - flow_instance.add_context("location", "New York") - flow_instance.add_context("time", "tomorrow") - flow_instance.clear_context() - context_location = flow_instance.get_context("location") - context_time = flow_instance.get_context("time") - assert context_location is None - assert context_time is None - - -def test_flow_input_validation(flow_instance): - # Test input validation for invalid agent configurations - with pytest.raises(ValueError): - Agent(config=None) # Invalid config, should raise ValueError - - with pytest.raises(ValueError): - flow_instance.set_message_delimiter( - "" - ) # Empty delimiter, should raise ValueError - - with pytest.raises(ValueError): - flow_instance.set_message_delimiter( - None - ) # None delimiter, should raise ValueError - - with pytest.raises(ValueError): - flow_instance.set_message_delimiter( - 123 - ) # Invalid delimiter type, should raise ValueError - - with pytest.raises(ValueError): - flow_instance.set_logger( - "invalid_logger" - ) # Invalid logger type, should raise ValueError - - with pytest.raises(ValueError): - flow_instance.add_context( - None, "value" - ) # None key, should raise ValueError - - with pytest.raises(ValueError): - flow_instance.add_context( - "key", None - ) # None value, should raise ValueError - - with pytest.raises(ValueError): - flow_instance.update_context( - None, "value" - ) # None key, should raise ValueError - - with pytest.raises(ValueError): - flow_instance.update_context( - "key", None - ) # None value, should raise ValueError - - -def test_flow_conversation_reset(flow_instance): - # Test conversation reset - flow_instance.run("Message 1") - flow_instance.run("Message 2") - flow_instance.reset_conversation() - assert len(flow_instance.get_message_history()) == 0 - - -def test_flow_conversation_persistence(flow_instance): - # Test conversation persistence across instances - flow_instance.run("Message 1") - flow_instance.run("Message 2") - conversation = flow_instance.get_conversation() - - new_flow_instance = Agent() - new_flow_instance.load_conversation(conversation) - assert len(new_flow_instance.get_message_history()) == 2 - assert "Message 1" in new_flow_instance.get_message_history()[0] - assert "Message 2" in new_flow_instance.get_message_history()[1] - - -def test_flow_custom_event_listener(flow_instance): - # Test custom event listener - class CustomEventListener: - def on_message_received(self, message): + @patch("yaml.safe_load") + def test_invalid_return_type(self, mock_safe_load, mock_open): + """Test handling invalid return type""" + # Mock YAML content parsing + mock_safe_load.return_value = { + "agents": [ + { + "agent_name": "Financial-Analysis-Agent", + "model": { + "openai_api_key": "fake-api-key", + "model_name": "gpt-4o-mini", + "temperature": 0.1, + "max_tokens": 2000, + }, + "system_prompt": "financial_agent_sys_prompt", + "max_loops": 1, + "autosave": True, + "dashboard": False, + "verbose": True, + "dynamic_temperature_enabled": True, + "saved_state_path": "finance_agent.json", + "user_name": "swarms_corp", + "retry_attempts": 1, + "context_length": 200000, + "return_step_meta": False, + "output_type": "str", + "task": "How can I establish a ROTH IRA to buy stocks and get a tax break?", + } + ] + } + + # Test if an error is raised for invalid return_type + with pytest.raises(ValueError) as context: + create_agents_from_yaml( + "fake_yaml_path.yaml", return_type="invalid_type" + ) + assert "Invalid return_type" in str(context.exception) + + +# ============================================================================ +# BENCHMARK TESTS +# ============================================================================ + + +class TestAgentBenchmark: + """Test agent benchmarking functionality""" + + def test_benchmark_multiple_agents(self): + """Test benchmarking multiple agents""" + console = Console() + init_times = [] + memory_readings = [] + process = psutil.Process(os.getpid()) + + # Create benchmark tables + time_table = Table(title="Time Statistics") + time_table.add_column("Metric", style="cyan") + time_table.add_column("Value", style="green") + + memory_table = Table(title="Memory Statistics") + memory_table.add_column("Metric", style="cyan") + memory_table.add_column("Value", style="green") + + initial_memory = process.memory_info().rss / 1024 + start_total_time = time.perf_counter() + + # Initialize agents and measure performance + num_agents = 10 # Reduced for testing + for i in range(num_agents): + start_time = time.perf_counter() + + Agent( + agent_name=f"Financial-Analysis-Agent-{i}", + agent_description="Personal finance advisor agent", + max_loops=2, + model_name="gpt-4o-mini", + dynamic_temperature_enabled=True, + interactive=False, + ) + + init_time = (time.perf_counter() - start_time) * 1000 + init_times.append(init_time) + + current_memory = process.memory_info().rss / 1024 + memory_readings.append(current_memory - initial_memory) + + if (i + 1) % 5 == 0: + console.print( + f"Created {i + 1} agents...", style="bold blue" + ) + + (time.perf_counter() - start_total_time) * 1000 + + # Calculate statistics + time_stats = self._get_time_stats(init_times) + memory_stats = self._get_memory_stats(memory_readings) + + # Verify basic statistics + assert len(init_times) == num_agents + assert len(memory_readings) == num_agents + assert time_stats["mean"] > 0 + assert memory_stats["mean"] >= 0 + + print("āœ“ Benchmark test passed") + + def _get_memory_stats(self, memory_readings): + """Calculate memory statistics""" + return { + "peak": max(memory_readings) if memory_readings else 0, + "min": min(memory_readings) if memory_readings else 0, + "mean": mean(memory_readings) if memory_readings else 0, + "median": ( + median(memory_readings) if memory_readings else 0 + ), + "stdev": ( + stdev(memory_readings) + if len(memory_readings) > 1 + else 0 + ), + "variance": ( + variance(memory_readings) + if len(memory_readings) > 1 + else 0 + ), + } + + def _get_time_stats(self, times): + """Calculate time statistics""" + return { + "total": sum(times), + "mean": mean(times) if times else 0, + "median": median(times) if times else 0, + "min": min(times) if times else 0, + "max": max(times) if times else 0, + "stdev": stdev(times) if len(times) > 1 else 0, + "variance": variance(times) if len(times) > 1 else 0, + } + + +# ============================================================================ +# TOOL USAGE TESTS +# ============================================================================ + + +class TestAgentToolUsage: + """Test comprehensive tool usage functionality for agents""" + + def test_normal_callable_tools(self): + """Test normal callable tools (functions, lambdas, methods)""" + print("\nTesting normal callable tools...") + + def math_tool(x: int, y: int) -> int: + """Add two numbers together""" + return x + y + + def string_tool(text: str) -> str: + """Convert text to uppercase""" + return text.upper() + + def list_tool(items: list) -> int: + """Count items in a list""" + return len(items) + + # Test with individual function tools + agent = Agent( + agent_name="Callable-Tools-Test-Agent", + model_name="gpt-4o-mini", + max_loops=1, + tools=[math_tool, string_tool, list_tool], + ) + + # Test tool addition + assert len(agent.tools) == 3, "Tools not added correctly" + + # Test tool execution + response = agent.run("Use the math tool to add 5 and 3") + assert response is not None, "Tool execution failed" + + # Test lambda tools + def lambda_tool(x): + return x * 2 + + agent.add_tool(lambda_tool) + assert ( + len(agent.tools) == 4 + ), "Lambda tool not added correctly" + + # Test method tools + class MathOperations: + def multiply(self, x: int, y: int) -> int: + """Multiply two numbers""" + return x * y + + math_ops = MathOperations() + agent.add_tool(math_ops.multiply) + assert ( + len(agent.tools) == 5 + ), "Method tool not added correctly" + + print("āœ“ Normal callable tools test passed") + + def test_tool_management_operations(self): + """Test tool management operations (add, remove, list)""" + print("\nTesting tool management operations...") + + def tool1(x: int) -> int: + """Tool 1""" + return x + 1 + + def tool2(x: int) -> int: + """Tool 2""" + return x * 2 + + def tool3(x: int) -> int: + """Tool 3""" + return x - 1 + + agent = Agent( + agent_name="Tool-Management-Test-Agent", + model_name="gpt-4o-mini", + max_loops=1, + tools=[tool1, tool2], + ) + + # Test initial tools + assert ( + len(agent.tools) == 2 + ), "Initial tools not set correctly" + + # Test adding single tool + agent.add_tool(tool3) + assert len(agent.tools) == 3, "Single tool addition failed" + + # Test adding multiple tools + def tool4(x: int) -> int: + return x**2 + + def tool5(x: int) -> int: + return x // 2 + + agent.add_tools([tool4, tool5]) + assert len(agent.tools) == 5, "Multiple tools addition failed" + + # Test removing single tool + agent.remove_tool(tool1) + assert len(agent.tools) == 4, "Single tool removal failed" + + # Test removing multiple tools + agent.remove_tools([tool2, tool3]) + assert len(agent.tools) == 2, "Multiple tools removal failed" + + print("āœ“ Tool management operations test passed") + + def test_mcp_single_url_tools(self): + """Test MCP single URL tools""" + print("\nTesting MCP single URL tools...") + + # Mock MCP URL for testing + mock_mcp_url = "http://localhost:8000/mcp" + + with patch( + "swarms.structs.agent.get_mcp_tools_sync" + ) as mock_get_tools: + # Mock MCP tools response + mock_tools = [ + { + "type": "function", + "function": { + "name": "mcp_calculator", + "description": "Perform calculations", + "parameters": { + "type": "object", + "properties": { + "expression": { + "type": "string", + "description": "Math expression", + } + }, + "required": ["expression"], + }, + }, + }, + { + "type": "function", + "function": { + "name": "mcp_weather", + "description": "Get weather information", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "City name", + } + }, + "required": ["location"], + }, + }, + }, + ] + mock_get_tools.return_value = mock_tools + + agent = Agent( + agent_name="MCP-Single-URL-Test-Agent", + model_name="gpt-4o-mini", + max_loops=1, + mcp_url=mock_mcp_url, + verbose=True, + ) + + # Test MCP tools integration + tools = agent.add_mcp_tools_to_memory() + assert len(tools) == 2, "MCP tools not loaded correctly" + assert ( + mock_get_tools.called + ), "MCP tools function not called" + + # Verify tool structure + assert "mcp_calculator" in str( + tools + ), "Calculator tool not found" + assert "mcp_weather" in str( + tools + ), "Weather tool not found" + + print("āœ“ MCP single URL tools test passed") + + def test_mcp_multiple_urls_tools(self): + """Test MCP multiple URLs tools""" + print("\nTesting MCP multiple URLs tools...") + + # Mock multiple MCP URLs for testing + mock_mcp_urls = [ + "http://localhost:8000/mcp1", + "http://localhost:8000/mcp2", + "http://localhost:8000/mcp3", + ] + + with patch( + "swarms.structs.agent.get_tools_for_multiple_mcp_servers" + ) as mock_get_tools: + # Mock MCP tools response from multiple servers + mock_tools = [ + { + "type": "function", + "function": { + "name": "server1_tool", + "description": "Tool from server 1", + "parameters": { + "type": "object", + "properties": { + "input": {"type": "string"} + }, + }, + }, + }, + { + "type": "function", + "function": { + "name": "server2_tool", + "description": "Tool from server 2", + "parameters": { + "type": "object", + "properties": { + "data": {"type": "string"} + }, + }, + }, + }, + { + "type": "function", + "function": { + "name": "server3_tool", + "description": "Tool from server 3", + "parameters": { + "type": "object", + "properties": { + "query": {"type": "string"} + }, + }, + }, + }, + ] + mock_get_tools.return_value = mock_tools + + agent = Agent( + agent_name="MCP-Multiple-URLs-Test-Agent", + model_name="gpt-4o-mini", + max_loops=1, + mcp_urls=mock_mcp_urls, + verbose=True, + ) + + # Test MCP tools integration from multiple servers + tools = agent.add_mcp_tools_to_memory() + assert ( + len(tools) == 3 + ), "MCP tools from multiple servers not loaded correctly" + assert ( + mock_get_tools.called + ), "MCP multiple tools function not called" + + # Verify tools from different servers + tools_str = str(tools) + assert ( + "server1_tool" in tools_str + ), "Server 1 tool not found" + assert ( + "server2_tool" in tools_str + ), "Server 2 tool not found" + assert ( + "server3_tool" in tools_str + ), "Server 3 tool not found" + + print("āœ“ MCP multiple URLs tools test passed") + + def test_base_tool_class_tools(self): + """Test BaseTool class tools""" + print("\nTesting BaseTool class tools...") + + from swarms.tools.base_tool import BaseTool + + def sample_function(x: int, y: int) -> int: + """Sample function for testing""" + return x + y + + # Create BaseTool instance + base_tool = BaseTool( + verbose=True, + tools=[sample_function], + tool_system_prompt="You are a helpful tool assistant", + ) + + # Test tool schema generation + schema = base_tool.func_to_dict(sample_function) + assert isinstance( + schema, dict + ), "Tool schema not generated correctly" + assert "name" in schema, "Tool name not in schema" + assert ( + "description" in schema + ), "Tool description not in schema" + assert "parameters" in schema, "Tool parameters not in schema" + + # Test tool execution + test_input = {"x": 5, "y": 3} + result = base_tool.execute_tool(test_input) + assert result is not None, "Tool execution failed" + + print("āœ“ BaseTool class tools test passed") + + def test_tool_execution_and_error_handling(self): + """Test tool execution and error handling""" + print("\nTesting tool execution and error handling...") + + def valid_tool(x: int) -> int: + """Valid tool that works correctly""" + return x * 2 + + def error_tool(x: int) -> int: + """Tool that raises an error""" + raise ValueError("Test error") + + def type_error_tool(x: str) -> str: + """Tool with type error""" + return x.upper() + + agent = Agent( + agent_name="Tool-Execution-Test-Agent", + model_name="gpt-4o-mini", + max_loops=1, + tools=[valid_tool, error_tool, type_error_tool], + ) + + # Test valid tool execution + response = agent.run("Use the valid tool with input 5") + assert response is not None, "Valid tool execution failed" + + # Test error handling + try: + agent.run("Use the error tool") + # Should handle error gracefully + except Exception: + # Expected to handle errors gracefully pass - def on_response_generated(self, response): + print("āœ“ Tool execution and error handling test passed") + + def test_tool_schema_generation(self): + """Test tool schema generation and validation""" + print("\nTesting tool schema generation...") + + def complex_tool( + name: str, + age: int, + email: str = None, + is_active: bool = True, + ) -> dict: + """Complex tool with various parameter types""" + return { + "name": name, + "age": age, + "email": email, + "is_active": is_active, + } + + agent = Agent( + agent_name="Tool-Schema-Test-Agent", + model_name="gpt-4o-mini", + max_loops=1, + tools=[complex_tool], + ) + + # Test that tools are properly registered + assert len(agent.tools) == 1, "Tool not registered correctly" + + # Test tool execution with complex parameters + response = agent.run( + "Use the complex tool with name 'John', age 30, email 'john@example.com'" + ) + assert response is not None, "Complex tool execution failed" + + print("āœ“ Tool schema generation test passed") + + def test_aop_tools(self): + """Test AOP (Agent Operations) tools""" + print("\nTesting AOP tools...") + + from swarms.structs.aop import AOP + + # Create test agents + agent1 = Agent( + agent_name="AOP-Agent-1", + model_name="gpt-4o-mini", + max_loops=1, + ) + + agent2 = Agent( + agent_name="AOP-Agent-2", + model_name="gpt-4o-mini", + max_loops=1, + ) + + # Create AOP instance + aop = AOP( + server_name="test-aop-server", + verbose=True, + ) + + # Test adding agents as tools + tool_names = aop.add_agents_batch( + agents=[agent1, agent2], + tool_names=["math_agent", "text_agent"], + tool_descriptions=[ + "Performs mathematical operations", + "Handles text processing", + ], + ) + + assert ( + len(tool_names) == 2 + ), "AOP agents not added as tools correctly" + assert ( + "math_agent" in tool_names + ), "Math agent tool not created" + assert ( + "text_agent" in tool_names + ), "Text agent tool not created" + + # Test tool discovery + tools = aop.get_available_tools() + assert len(tools) >= 2, "AOP tools not discovered correctly" + + print("āœ“ AOP tools test passed") + + def test_tool_choice_and_execution_modes(self): + """Test different tool choice and execution modes""" + print("\nTesting tool choice and execution modes...") + + def tool_a(x: int) -> int: + """Tool A""" + return x + 1 + + def tool_b(x: int) -> int: + """Tool B""" + return x * 2 + + # Test with auto tool choice + agent_auto = Agent( + agent_name="Auto-Tool-Choice-Agent", + model_name="gpt-4o-mini", + max_loops=1, + tools=[tool_a, tool_b], + tool_choice="auto", + ) + + response_auto = agent_auto.run( + "Calculate something using the available tools" + ) + assert response_auto is not None, "Auto tool choice failed" + + # Test with specific tool choice + agent_specific = Agent( + agent_name="Specific-Tool-Choice-Agent", + model_name="gpt-4o-mini", + max_loops=1, + tools=[tool_a, tool_b], + tool_choice="tool_a", + ) + + response_specific = agent_specific.run( + "Use tool_a with input 5" + ) + assert ( + response_specific is not None + ), "Specific tool choice failed" + + # Test with tool execution enabled/disabled + agent_execute = Agent( + agent_name="Tool-Execute-Agent", + model_name="gpt-4o-mini", + max_loops=1, + tools=[tool_a, tool_b], + execute_tool=True, + ) + + response_execute = agent_execute.run("Execute a tool") + assert ( + response_execute is not None + ), "Tool execution mode failed" + + print("āœ“ Tool choice and execution modes test passed") + + def test_tool_system_prompts(self): + """Test tool system prompts and custom tool prompts""" + print("\nTesting tool system prompts...") + + def calculator_tool(expression: str) -> str: + """Calculate mathematical expressions""" + try: + result = eval(expression) + return str(result) + except Exception: + return "Invalid expression" + + custom_tool_prompt = "You have access to a calculator tool. Use it for mathematical calculations." + + agent = Agent( + agent_name="Tool-Prompt-Test-Agent", + model_name="gpt-4o-mini", + max_loops=1, + tools=[calculator_tool], + tool_system_prompt=custom_tool_prompt, + ) + + # Test that custom tool prompt is set + assert ( + agent.tool_system_prompt == custom_tool_prompt + ), "Custom tool prompt not set" + + # Test tool execution with custom prompt + response = agent.run("Calculate 2 + 2 * 3") + assert ( + response is not None + ), "Tool execution with custom prompt failed" + + print("āœ“ Tool system prompts test passed") + + def test_tool_parallel_execution(self): + """Test parallel tool execution capabilities""" + print("\nTesting parallel tool execution...") + + def slow_tool(x: int) -> int: + """Slow tool that takes time""" + import time + + time.sleep(0.1) # Simulate slow operation + return x * 2 + + def fast_tool(x: int) -> int: + """Fast tool""" + return x + 1 + + agent = Agent( + agent_name="Parallel-Tool-Test-Agent", + model_name="gpt-4o-mini", + max_loops=1, + tools=[slow_tool, fast_tool], + ) + + # Test parallel tool execution + start_time = time.time() + response = agent.run("Use both tools with input 5") + end_time = time.time() + + assert response is not None, "Parallel tool execution failed" + # Should be faster than sequential execution + assert ( + end_time - start_time + ) < 0.5, "Parallel execution took too long" + + print("āœ“ Parallel tool execution test passed") + + def test_tool_validation_and_type_checking(self): + """Test tool validation and type checking""" + print("\nTesting tool validation and type checking...") + + def typed_tool(x: int, y: str, z: bool = False) -> dict: + """Tool with specific type hints""" + return {"x": x, "y": y, "z": z, "result": f"{x} {y} {z}"} + + agent = Agent( + agent_name="Tool-Validation-Test-Agent", + model_name="gpt-4o-mini", + max_loops=1, + tools=[typed_tool], + ) + + # Test tool execution with correct types + response = agent.run( + "Use typed_tool with x=5, y='hello', z=True" + ) + assert response is not None, "Typed tool execution failed" + + # Test tool execution with incorrect types (should handle gracefully) + try: + agent.run("Use typed_tool with incorrect types") + except Exception: + # Expected to handle type errors gracefully pass - custom_event_listener = CustomEventListener() - flow_instance.add_event_listener(custom_event_listener) - - # Ensure that the custom event listener methods are called during a conversation - with mock.patch.object( - custom_event_listener, "on_message_received" - ) as mock_received, mock.patch.object( - custom_event_listener, "on_response_generated" - ) as mock_response: - flow_instance.run("Message 1") - mock_received.assert_called_once() - mock_response.assert_called_once() - + print("āœ“ Tool validation and type checking test passed") -def test_flow_multiple_event_listeners(flow_instance): - # Test multiple event listeners - class FirstEventListener: - def on_message_received(self, message): - pass + def test_tool_caching_and_performance(self): + """Test tool caching and performance optimization""" + print("\nTesting tool caching and performance...") - def on_response_generated(self, response): - pass + call_count = 0 - class SecondEventListener: - def on_message_received(self, message): - pass + def cached_tool(x: int) -> int: + """Tool that should be cached""" + nonlocal call_count + call_count += 1 + return x**2 - def on_response_generated(self, response): - pass + agent = Agent( + agent_name="Tool-Caching-Test-Agent", + model_name="gpt-4o-mini", + max_loops=1, + tools=[cached_tool], + ) - first_event_listener = FirstEventListener() - second_event_listener = SecondEventListener() - flow_instance.add_event_listener(first_event_listener) - flow_instance.add_event_listener(second_event_listener) - - # Ensure that both event listeners receive events during a conversation - with mock.patch.object( - first_event_listener, "on_message_received" - ) as mock_first_received, mock.patch.object( - first_event_listener, "on_response_generated" - ) as mock_first_response, mock.patch.object( - second_event_listener, "on_message_received" - ) as mock_second_received, mock.patch.object( - second_event_listener, "on_response_generated" - ) as mock_second_response: - flow_instance.run("Message 1") - mock_first_received.assert_called_once() - mock_first_response.assert_called_once() - mock_second_received.assert_called_once() - mock_second_response.assert_called_once() - - -# Add more test cases as needed to cover various aspects of your Agent class -def test_flow_error_handling(flow_instance): - # Test error handling and exceptions - with pytest.raises(ValueError): - flow_instance.set_message_delimiter( - "" - ) # Empty delimiter, should raise ValueError - - with pytest.raises(ValueError): - flow_instance.set_message_delimiter( - None - ) # None delimiter, should raise ValueError - - with pytest.raises(ValueError): - flow_instance.set_logger( - "invalid_logger" - ) # Invalid logger type, should raise ValueError - - with pytest.raises(ValueError): - flow_instance.add_context( - None, "value" - ) # None key, should raise ValueError - - with pytest.raises(ValueError): - flow_instance.add_context( - "key", None - ) # None value, should raise ValueError - - with pytest.raises(ValueError): - flow_instance.update_context( - None, "value" - ) # None key, should raise ValueError - - with pytest.raises(ValueError): - flow_instance.update_context( - "key", None - ) # None value, should raise ValueError - - -def test_flow_context_operations(flow_instance): - # Test context operations - flow_instance.add_context("user_id", "12345") - assert flow_instance.get_context("user_id") == "12345" - flow_instance.update_context("user_id", "54321") - assert flow_instance.get_context("user_id") == "54321" - flow_instance.remove_context("user_id") - assert flow_instance.get_context("user_id") is None - - -# Add more test cases as needed to cover various aspects of your Agent class - - -def test_flow_long_messages(flow_instance): - # Test handling of long messages - long_message = "A" * 10000 # Create a very long message - flow_instance.run(long_message) - assert len(flow_instance.get_message_history()) == 1 - assert flow_instance.get_message_history()[0] == long_message - - -def test_flow_custom_response(flow_instance): - # Test custom response generation - def custom_response_generator(message): - if message == "Hello": - return "Hi there!" - elif message == "How are you?": - return "I'm doing well, thank you." - else: - return "I don't understand." - - flow_instance.set_response_generator(custom_response_generator) - - assert flow_instance.run("Hello") == "Hi there!" - assert ( - flow_instance.run("How are you?") - == "I'm doing well, thank you." - ) - assert ( - flow_instance.run("What's your name?") - == "I don't understand." - ) - - -def test_flow_message_validation(flow_instance): - # Test message validation - def custom_message_validator(message): - return len(message) > 0 # Reject empty messages - - flow_instance.set_message_validator(custom_message_validator) - - assert flow_instance.run("Valid message") is not None - assert ( - flow_instance.run("") is None - ) # Empty message should be rejected - assert ( - flow_instance.run(None) is None - ) # None message should be rejected - - -def test_flow_custom_logging(flow_instance): - custom_logger = logger - flow_instance.set_logger(custom_logger) - - with mock.patch.object(custom_logger, "log") as mock_log: - flow_instance.run("Message") - mock_log.assert_called_once_with("Message") - - -def test_flow_performance(flow_instance): - # Test the performance of the Agent class by running a large number of messages - num_messages = 1000 - for i in range(num_messages): - flow_instance.run(f"Message {i}") - assert len(flow_instance.get_message_history()) == num_messages - - -def test_flow_complex_use_case(flow_instance): - # Test a complex use case scenario - flow_instance.add_context("user_id", "12345") - flow_instance.run("Hello") - flow_instance.run("How can I help you?") - assert ( - flow_instance.get_response() == "Please provide more details." - ) - flow_instance.update_context("user_id", "54321") - flow_instance.run("I need help with my order") - assert ( - flow_instance.get_response() - == "Sure, I can assist with that." - ) - flow_instance.reset_conversation() - assert len(flow_instance.get_message_history()) == 0 - assert flow_instance.get_context("user_id") is None + # Test multiple calls to the same tool + agent.run("Use cached_tool with input 5") + agent.run("Use cached_tool with input 5 again") + # Verify tool was called (caching behavior may vary) + assert call_count >= 1, "Tool not called at least once" + + print("āœ“ Tool caching and performance test passed") -# Add more test cases as needed to cover various aspects of your Agent class -def test_flow_context_handling(flow_instance): - # Test context handling - flow_instance.add_context("user_id", "12345") - assert flow_instance.get_context("user_id") == "12345" - flow_instance.update_context("user_id", "54321") - assert flow_instance.get_context("user_id") == "54321" - flow_instance.remove_context("user_id") - assert flow_instance.get_context("user_id") is None + def test_tool_error_recovery(self): + """Test tool error recovery and fallback mechanisms""" + print("\nTesting tool error recovery...") + def unreliable_tool(x: int) -> int: + """Tool that sometimes fails""" + import random -def test_flow_concurrent_requests(flow_instance): - # Test concurrent message processing - import threading + if random.random() < 0.5: + raise Exception("Random failure") + return x * 2 - def send_messages(): - for i in range(100): - flow_instance.run(f"Message {i}") + def fallback_tool(x: int) -> int: + """Fallback tool""" + return x + 10 - threads = [] - for _ in range(5): - thread = threading.Thread(target=send_messages) - threads.append(thread) - thread.start() - - for thread in threads: - thread.join() - - assert len(flow_instance.get_message_history()) == 500 - - -def test_flow_custom_timeout(flow_instance): - # Test custom timeout handling - flow_instance.set_timeout( - 10 - ) # Set a custom timeout of 10 seconds - assert flow_instance.get_timeout() == 10 - - import time - - start_time = time.time() - flow_instance.run("Long-running operation") - end_time = time.time() - execution_time = end_time - start_time - assert execution_time >= 10 # Ensure the timeout was respected - - -# Add more test cases as needed to thoroughly cover your Agent class - - -def test_flow_interactive_run(flow_instance, capsys): - # Test interactive run mode - # Simulate user input and check if the AI responds correctly - user_input = ["Hello", "How can you help me?", "Exit"] - - def simulate_user_input(input_list): - input_index = 0 - while input_index < len(input_list): - user_response = input_list[input_index] - flow_instance.interactive_run(max_loops=1) - - # Capture the AI's response - captured = capsys.readouterr() - ai_response = captured.out.strip() - - assert f"You: {user_response}" in captured.out - assert "AI:" in captured.out - - # Check if the AI's response matches the expected response - expected_response = f"AI: {ai_response}" - assert expected_response in captured.out - - input_index += 1 - - simulate_user_input(user_input) - - -# Assuming you have already defined your Agent class and created an instance for testing - - -def test_flow_agent_history_prompt(flow_instance): - # Test agent history prompt generation - system_prompt = "This is the system prompt." - history = ["User: Hi", "AI: Hello"] - - agent_history_prompt = flow_instance.agent_history_prompt( - system_prompt, history - ) - - assert ( - "SYSTEM_PROMPT: This is the system prompt." - in agent_history_prompt - ) - assert ( - "History: ['User: Hi', 'AI: Hello']" in agent_history_prompt - ) - - -async def test_flow_run_concurrent(flow_instance): - # Test running tasks concurrently - tasks = ["Task 1", "Task 2", "Task 3"] - completed_tasks = await flow_instance.run_concurrent(tasks) - - # Ensure that all tasks are completed - assert len(completed_tasks) == len(tasks) - - -def test_flow_bulk_run(flow_instance): - # Test bulk running of tasks - input_data = [ - {"task": "Task 1", "param1": "value1"}, - {"task": "Task 2", "param2": "value2"}, - {"task": "Task 3", "param3": "value3"}, + agent = Agent( + agent_name="Tool-Recovery-Test-Agent", + model_name="gpt-4o-mini", + max_loops=1, + tools=[unreliable_tool, fallback_tool], + retry_attempts=3, + ) + + # Test error recovery + response = agent.run("Use unreliable_tool with input 5") + assert response is not None, "Tool error recovery failed" + + print("āœ“ Tool error recovery test passed") + + def test_tool_with_different_output_types(self): + """Test tools with different output types""" + print("\nTesting tools with different output types...") + + def json_tool(data: dict) -> str: + """Tool that returns JSON string""" + import json + + return json.dumps(data) + + def yaml_tool(data: dict) -> str: + """Tool that returns YAML string""" + import yaml + + return yaml.dump(data) + + def dict_tool(x: int) -> dict: + """Tool that returns dictionary""" + return {"value": x, "squared": x**2} + + agent = Agent( + agent_name="Output-Types-Test-Agent", + model_name="gpt-4o-mini", + max_loops=1, + tools=[json_tool, yaml_tool, dict_tool], + ) + + # Test JSON tool + response = agent.run( + "Use json_tool with data {'name': 'test', 'value': 123}" + ) + assert response is not None, "JSON tool execution failed" + + # Test YAML tool + response = agent.run( + "Use yaml_tool with data {'key': 'value'}" + ) + assert response is not None, "YAML tool execution failed" + + # Test dict tool + response = agent.run("Use dict_tool with input 5") + assert response is not None, "Dict tool execution failed" + + print("āœ“ Tools with different output types test passed") + + def test_tool_with_async_execution(self): + """Test tools with async execution""" + print("\nTesting tools with async execution...") + + async def async_tool(x: int) -> int: + """Async tool that performs async operation""" + import asyncio + + await asyncio.sleep(0.01) # Simulate async operation + return x * 2 + + def sync_tool(x: int) -> int: + """Sync tool""" + return x + 1 + + agent = Agent( + agent_name="Async-Tool-Test-Agent", + model_name="gpt-4o-mini", + max_loops=1, + tools=[ + sync_tool + ], # Note: async tools need special handling + ) + + # Test sync tool execution + response = agent.run("Use sync_tool with input 5") + assert response is not None, "Sync tool execution failed" + + print("āœ“ Tools with async execution test passed") + + def test_tool_with_file_operations(self): + """Test tools that perform file operations""" + print("\nTesting tools with file operations...") + + import os + import tempfile + + def file_writer_tool(filename: str, content: str) -> str: + """Tool that writes content to a file""" + with open(filename, "w") as f: + f.write(content) + return f"Written {len(content)} characters to {filename}" + + def file_reader_tool(filename: str) -> str: + """Tool that reads content from a file""" + try: + with open(filename, "r") as f: + return f.read() + except FileNotFoundError: + return "File not found" + + with tempfile.TemporaryDirectory() as temp_dir: + test_file = os.path.join(temp_dir, "test.txt") + + agent = Agent( + agent_name="File-Ops-Test-Agent", + model_name="gpt-4o-mini", + max_loops=1, + tools=[file_writer_tool, file_reader_tool], + ) + + # Test file writing + response = agent.run( + f"Use file_writer_tool to write 'Hello World' to {test_file}" + ) + assert ( + response is not None + ), "File writing tool execution failed" + + # Test file reading + response = agent.run( + f"Use file_reader_tool to read from {test_file}" + ) + assert ( + response is not None + ), "File reading tool execution failed" + + print("āœ“ Tools with file operations test passed") + + def test_tool_with_network_operations(self): + """Test tools that perform network operations""" + print("\nTesting tools with network operations...") + + def url_tool(url: str) -> str: + """Tool that processes URLs""" + return f"Processing URL: {url}" + + def api_tool(endpoint: str, method: str = "GET") -> str: + """Tool that simulates API calls""" + return f"API {method} request to {endpoint}" + + agent = Agent( + agent_name="Network-Ops-Test-Agent", + model_name="gpt-4o-mini", + max_loops=1, + tools=[url_tool, api_tool], + ) + + # Test URL tool + response = agent.run( + "Use url_tool with 'https://example.com'" + ) + assert response is not None, "URL tool execution failed" + + # Test API tool + response = agent.run( + "Use api_tool with endpoint '/api/data' and method 'POST'" + ) + assert response is not None, "API tool execution failed" + + print("āœ“ Tools with network operations test passed") + + def test_tool_with_database_operations(self): + """Test tools that perform database operations""" + print("\nTesting tools with database operations...") + + def db_query_tool(query: str) -> str: + """Tool that simulates database queries""" + return f"Executed query: {query}" + + def db_insert_tool(table: str, data: dict) -> str: + """Tool that simulates database inserts""" + return f"Inserted data into {table}: {data}" + + agent = Agent( + agent_name="Database-Ops-Test-Agent", + model_name="gpt-4o-mini", + max_loops=1, + tools=[db_query_tool, db_insert_tool], + ) + + # Test database query + response = agent.run( + "Use db_query_tool with 'SELECT * FROM users'" + ) + assert ( + response is not None + ), "Database query tool execution failed" + + # Test database insert + response = agent.run( + "Use db_insert_tool with table 'users' and data {'name': 'John'}" + ) + assert ( + response is not None + ), "Database insert tool execution failed" + + print("āœ“ Tools with database operations test passed") + + def test_tool_with_machine_learning_operations(self): + """Test tools that perform ML operations""" + print("\nTesting tools with ML operations...") + + def predict_tool(features: list) -> str: + """Tool that simulates ML predictions""" + return f"Prediction for features {features}: 0.85" + + def train_tool(model_name: str, data_size: int) -> str: + """Tool that simulates model training""" + return f"Trained {model_name} with {data_size} samples" + + agent = Agent( + agent_name="ML-Ops-Test-Agent", + model_name="gpt-4o-mini", + max_loops=1, + tools=[predict_tool, train_tool], + ) + + # Test ML prediction + response = agent.run( + "Use predict_tool with features [1, 2, 3, 4]" + ) + assert ( + response is not None + ), "ML prediction tool execution failed" + + # Test ML training + response = agent.run( + "Use train_tool with model 'random_forest' and data_size 1000" + ) + assert ( + response is not None + ), "ML training tool execution failed" + + print("āœ“ Tools with ML operations test passed") + + def test_tool_with_image_processing(self): + """Test tools that perform image processing""" + print("\nTesting tools with image processing...") + + def resize_tool( + image_path: str, width: int, height: int + ) -> str: + """Tool that simulates image resizing""" + return f"Resized {image_path} to {width}x{height}" + + def filter_tool(image_path: str, filter_type: str) -> str: + """Tool that simulates image filtering""" + return f"Applied {filter_type} filter to {image_path}" + + agent = Agent( + agent_name="Image-Processing-Test-Agent", + model_name="gpt-4o-mini", + max_loops=1, + tools=[resize_tool, filter_tool], + ) + + # Test image resizing + response = agent.run( + "Use resize_tool with image 'test.jpg', width 800, height 600" + ) + assert ( + response is not None + ), "Image resize tool execution failed" + + # Test image filtering + response = agent.run( + "Use filter_tool with image 'test.jpg' and filter 'blur'" + ) + assert ( + response is not None + ), "Image filter tool execution failed" + + print("āœ“ Tools with image processing test passed") + + def test_tool_with_text_processing(self): + """Test tools that perform text processing""" + print("\nTesting tools with text processing...") + + def tokenize_tool(text: str) -> list: + """Tool that tokenizes text""" + return text.split() + + def translate_tool(text: str, target_lang: str) -> str: + """Tool that simulates translation""" + return f"Translated '{text}' to {target_lang}" + + def sentiment_tool(text: str) -> str: + """Tool that simulates sentiment analysis""" + return f"Sentiment of '{text}': positive" + + agent = Agent( + agent_name="Text-Processing-Test-Agent", + model_name="gpt-4o-mini", + max_loops=1, + tools=[tokenize_tool, translate_tool, sentiment_tool], + ) + + # Test text tokenization + response = agent.run( + "Use tokenize_tool with 'Hello world this is a test'" + ) + assert ( + response is not None + ), "Text tokenization tool execution failed" + + # Test translation + response = agent.run( + "Use translate_tool with 'Hello' and target_lang 'Spanish'" + ) + assert ( + response is not None + ), "Translation tool execution failed" + + # Test sentiment analysis + response = agent.run( + "Use sentiment_tool with 'I love this product!'" + ) + assert ( + response is not None + ), "Sentiment analysis tool execution failed" + + print("āœ“ Tools with text processing test passed") + + def test_tool_with_mathematical_operations(self): + """Test tools that perform mathematical operations""" + print("\nTesting tools with mathematical operations...") + + def matrix_multiply_tool( + matrix_a: list, matrix_b: list + ) -> list: + """Tool that multiplies matrices""" + # Simple 2x2 matrix multiplication + result = [[0, 0], [0, 0]] + for i in range(2): + for j in range(2): + for k in range(2): + result[i][j] += ( + matrix_a[i][k] * matrix_b[k][j] + ) + return result + + def statistics_tool(data: list) -> dict: + """Tool that calculates statistics""" + return { + "mean": sum(data) / len(data), + "max": max(data), + "min": min(data), + "count": len(data), + } + + def calculus_tool(function: str, x: float) -> str: + """Tool that simulates calculus operations""" + return f"Derivative of {function} at x={x}: 2*x" + + agent = Agent( + agent_name="Math-Ops-Test-Agent", + model_name="gpt-4o-mini", + max_loops=1, + tools=[ + matrix_multiply_tool, + statistics_tool, + calculus_tool, + ], + ) + + # Test matrix multiplication + response = agent.run( + "Use matrix_multiply_tool with [[1,2],[3,4]] and [[5,6],[7,8]]" + ) + assert ( + response is not None + ), "Matrix multiplication tool execution failed" + + # Test statistics + response = agent.run( + "Use statistics_tool with [1, 2, 3, 4, 5]" + ) + assert ( + response is not None + ), "Statistics tool execution failed" + + # Test calculus + response = agent.run("Use calculus_tool with 'x^2' and x=3") + assert response is not None, "Calculus tool execution failed" + + print("āœ“ Tools with mathematical operations test passed") + + +# ============================================================================ +# LLM ARGS AND HANDLING TESTS +# ============================================================================ + + +class TestLLMArgsAndHandling: + """Test LLM arguments and handling functionality""" + + def test_combined_llm_args(self): + """Test that llm_args, tools_list_dictionary, and MCP tools can be combined.""" + print("\nTesting combined LLM args...") + + # Mock tools list dictionary + tools_list = [ + { + "type": "function", + "function": { + "name": "test_function", + "description": "A test function", + "parameters": { + "type": "object", + "properties": { + "test_param": { + "type": "string", + "description": "A test parameter", + } + }, + }, + }, + } + ] + + # Mock llm_args with Azure OpenAI specific parameters + llm_args = { + "api_version": "2024-02-15-preview", + "base_url": "https://your-resource.openai.azure.com/", + "api_key": "your-api-key", + } + + try: + # Test 1: Only llm_args + print("Testing Agent with only llm_args...") + Agent( + agent_name="test-agent-1", + model_name="gpt-4o-mini", + llm_args=llm_args, + ) + print("āœ“ Agent with only llm_args created successfully") + + # Test 2: Only tools_list_dictionary + print("Testing Agent with only tools_list_dictionary...") + Agent( + agent_name="test-agent-2", + model_name="gpt-4o-mini", + tools_list_dictionary=tools_list, + ) + print( + "āœ“ Agent with only tools_list_dictionary created successfully" + ) + + # Test 3: Combined llm_args and tools_list_dictionary + print( + "Testing Agent with combined llm_args and tools_list_dictionary..." + ) + agent3 = Agent( + agent_name="test-agent-3", + model_name="gpt-4o-mini", + llm_args=llm_args, + tools_list_dictionary=tools_list, + ) + print( + "āœ“ Agent with combined llm_args and tools_list_dictionary created successfully" + ) + + # Test 4: Verify that the LLM instance has the correct configuration + print("Verifying LLM configuration...") + + # Check that agent3 has both llm_args and tools configured + assert ( + agent3.llm_args == llm_args + ), "llm_args not preserved" + assert ( + agent3.tools_list_dictionary == tools_list + ), "tools_list_dictionary not preserved" + + # Check that the LLM instance was created + assert agent3.llm is not None, "LLM instance not created" + + print("āœ“ LLM configuration verified successfully") + print("āœ“ Combined LLM args test passed") + + except Exception as e: + print(f"āœ— Combined LLM args test failed: {e}") + raise + + def test_azure_openai_example(self): + """Test the Azure OpenAI example with api_version parameter.""" + print("\nTesting Azure OpenAI example with api_version...") + + try: + # Create an agent with Azure OpenAI configuration + agent = Agent( + agent_name="azure-test-agent", + model_name="azure/gpt-4o", + llm_args={ + "api_version": "2024-02-15-preview", + "base_url": "https://your-resource.openai.azure.com/", + "api_key": "your-api-key", + }, + tools_list_dictionary=[ + { + "type": "function", + "function": { + "name": "get_weather", + "description": "Get weather information", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state", + } + }, + }, + }, + } + ], + ) + + print( + "āœ“ Azure OpenAI agent with combined parameters created successfully" + ) + + # Verify configuration + assert agent.llm_args is not None, "llm_args not set" + assert ( + "api_version" in agent.llm_args + ), "api_version not in llm_args" + assert ( + agent.tools_list_dictionary is not None + ), "tools_list_dictionary not set" + assert ( + len(agent.tools_list_dictionary) > 0 + ), "tools_list_dictionary is empty" + + print("āœ“ Azure OpenAI configuration verified") + print("āœ“ Azure OpenAI example test passed") + + except Exception as e: + print(f"āœ— Azure OpenAI test failed: {e}") + raise + + def test_llm_handling_args_kwargs(self): + """Test that llm_handling properly handles both args and kwargs.""" + print("\nTesting LLM handling args and kwargs...") + + # Create an agent instance + agent = Agent( + agent_name="test-agent", + model_name="gpt-4o-mini", + temperature=0.7, + max_tokens=1000, + ) + + # Test 1: Call llm_handling with kwargs + print("Test 1: Testing kwargs handling...") + try: + # This should work and add the kwargs to additional_args + agent.llm_handling(top_p=0.9, frequency_penalty=0.1) + print("āœ“ kwargs handling works") + except Exception as e: + print(f"āœ— kwargs handling failed: {e}") + raise + + # Test 2: Call llm_handling with args (dictionary) + print("Test 2: Testing args handling with dictionary...") + try: + # This should merge the dictionary into additional_args + additional_config = { + "presence_penalty": 0.2, + "logit_bias": {"123": 1}, + } + agent.llm_handling(additional_config) + print("āœ“ args handling with dictionary works") + except Exception as e: + print(f"āœ— args handling with dictionary failed: {e}") + raise + + # Test 3: Call llm_handling with both args and kwargs + print("Test 3: Testing both args and kwargs...") + try: + # This should handle both + additional_config = {"presence_penalty": 0.3} + agent.llm_handling( + additional_config, top_p=0.8, frequency_penalty=0.2 + ) + print("āœ“ combined args and kwargs handling works") + except Exception as e: + print(f"āœ— combined args and kwargs handling failed: {e}") + raise + + # Test 4: Call llm_handling with non-dictionary args + print("Test 4: Testing non-dictionary args...") + try: + # This should store args under 'additional_args' key + agent.llm_handling( + "some_string", 123, ["list", "of", "items"] + ) + print("āœ“ non-dictionary args handling works") + except Exception as e: + print(f"āœ— non-dictionary args handling failed: {e}") + raise + + print("āœ“ LLM handling args and kwargs test passed") + + +# ============================================================================ +# MAIN TEST RUNNER +# ============================================================================ + + +def run_all_tests(): + """Run all test functions""" + print("Starting Merged Agent Test Suite...\n") + + # Test classes to run + test_classes = [ + TestBasicAgent, + TestAgentFeatures, + TestAgentLogging, + TestCreateAgentsFromYaml, + TestAgentBenchmark, + TestAgentToolUsage, + TestLLMArgsAndHandling, ] - responses = flow_instance.bulk_run(input_data) - - # Ensure that the responses match the input tasks - assert responses[0] == "Response for Task 1" - assert responses[1] == "Response for Task 2" - assert responses[2] == "Response for Task 3" - -def test_flow_from_llm_and_template(): - # Test creating Agent instance from an LLM and a template - llm_instance = mocked_llm # Replace with your LLM class - template = "This is a template for testing." - - flow_instance = Agent.from_llm_and_template( - llm_instance, template - ) - - assert isinstance(flow_instance, Agent) - - -def test_flow_from_llm_and_template_file(): - # Test creating Agent instance from an LLM and a template file - llm_instance = mocked_llm # Replace with your LLM class - template_file = ( - "template.txt" # Create a template file for testing - ) - - flow_instance = Agent.from_llm_and_template_file( - llm_instance, template_file - ) - - assert isinstance(flow_instance, Agent) - - -def test_flow_save_and_load(flow_instance, tmp_path): - # Test saving and loading the agent state - file_path = tmp_path / "flow_state.json" - - # Save the state - flow_instance.save(file_path) - - # Create a new instance and load the state - new_flow_instance = Agent(llm=mocked_llm, max_loops=5) - new_flow_instance.load(file_path) - - # Ensure that the loaded state matches the original state - assert new_flow_instance.memory == flow_instance.memory - - -def test_flow_validate_response(flow_instance): - # Test response validation - valid_response = "This is a valid response." - invalid_response = "Short." - - assert flow_instance.validate_response(valid_response) is True - assert flow_instance.validate_response(invalid_response) is False - - -# Add more test cases as needed for other methods and features of your Agent class - -# Finally, don't forget to run your tests using a testing framework like pytest - -# Assuming you have already defined your Agent class and created an instance for testing - - -def test_flow_print_history_and_memory(capsys, flow_instance): - # Test printing the history and memory of the agent - history = ["User: Hi", "AI: Hello"] - flow_instance.memory = [history] - - flow_instance.print_history_and_memory() - - captured = capsys.readouterr() - assert "Agent History and Memory" in captured.out - assert "Loop 1:" in captured.out - assert "User: Hi" in captured.out - assert "AI: Hello" in captured.out - - -def test_flow_run_with_timeout(flow_instance): - # Test running with a timeout - task = "Task with a long response time" - response = flow_instance.run_with_timeout(task, timeout=1) - - # Ensure that the response is either the actual response or "Timeout" - assert response in ["Actual Response", "Timeout"] + total_tests = 0 + passed_tests = 0 + failed_tests = 0 + + for test_class in test_classes: + print(f"\n{'='*50}") + print(f"Running {test_class.__name__}") + print(f"{'='*50}") + + # Create test instance + test_instance = test_class() + + # Get all test methods + test_methods = [ + method + for method in dir(test_instance) + if method.startswith("test_") + ] + + for test_method in test_methods: + total_tests += 1 + try: + # Run the test method + getattr(test_instance, test_method)() + passed_tests += 1 + print(f"āœ“ {test_method}") + except Exception as e: + failed_tests += 1 + print(f"āœ— {test_method}: {str(e)}") + + # Print summary + print(f"\n{'='*50}") + print("Test Summary") + print(f"{'='*50}") + print(f"Total Tests: {total_tests}") + print(f"Passed: {passed_tests}") + print(f"Failed: {failed_tests}") + print(f"Success Rate: {(passed_tests/total_tests)*100:.2f}%") + + return { + "total": total_tests, + "passed": passed_tests, + "failed": failed_tests, + "success_rate": (passed_tests / total_tests) * 100, + } -# Add more test cases as needed for other methods and features of your Agent class +if __name__ == "__main__": + # Run all tests + results = run_all_tests() -# Finally, don't forget to run your tests using a testing framework like pytest + print(results) diff --git a/tests/structs/test_agent_features.py b/tests/structs/test_agent_features.py deleted file mode 100644 index 22b6c3ea..00000000 --- a/tests/structs/test_agent_features.py +++ /dev/null @@ -1,600 +0,0 @@ -import asyncio -import json -import os -import tempfile -import time - -import yaml -from swarm_models import OpenAIChat - -from swarms import Agent - - -def test_basic_agent_functionality(): - """Test basic agent initialization and simple task execution""" - print("\nTesting basic agent functionality...") - - model = OpenAIChat(model_name="gpt-4.1") - agent = Agent(agent_name="Test-Agent", llm=model, max_loops=1) - - response = agent.run("What is 2+2?") - assert response is not None, "Agent response should not be None" - - # Test agent properties - assert ( - agent.agent_name == "Test-Agent" - ), "Agent name not set correctly" - assert agent.max_loops == 1, "Max loops not set correctly" - assert agent.llm is not None, "LLM not initialized" - - print("āœ“ Basic agent functionality test passed") - - -def test_memory_management(): - """Test agent memory management functionality""" - print("\nTesting memory management...") - - model = OpenAIChat(model_name="gpt-4.1") - agent = Agent( - agent_name="Memory-Test-Agent", - llm=model, - max_loops=1, - context_length=8192, - ) - - # Test adding to memory - agent.add_memory("Test memory entry") - assert ( - "Test memory entry" - in agent.short_memory.return_history_as_string() - ) - - # Test memory query - agent.memory_query("Test query") - - # Test token counting - tokens = agent.check_available_tokens() - assert isinstance(tokens, int), "Token count should be an integer" - - print("āœ“ Memory management test passed") - - -def test_agent_output_formats(): - """Test all available output formats""" - print("\nTesting all output formats...") - - model = OpenAIChat(model_name="gpt-4.1") - test_task = "Say hello!" - - output_types = { - "str": str, - "string": str, - "list": str, # JSON string containing list - "json": str, # JSON string - "dict": dict, - "yaml": str, - } - - for output_type, expected_type in output_types.items(): - agent = Agent( - agent_name=f"{output_type.capitalize()}-Output-Agent", - llm=model, - max_loops=1, - output_type=output_type, - ) - - response = agent.run(test_task) - assert ( - response is not None - ), f"{output_type} output should not be None" - - if output_type == "yaml": - # Verify YAML can be parsed - try: - yaml.safe_load(response) - print(f"āœ“ {output_type} output valid") - except yaml.YAMLError: - assert False, f"Invalid YAML output for {output_type}" - elif output_type in ["json", "list"]: - # Verify JSON can be parsed - try: - json.loads(response) - print(f"āœ“ {output_type} output valid") - except json.JSONDecodeError: - assert False, f"Invalid JSON output for {output_type}" - - print("āœ“ Output formats test passed") - - -def test_agent_state_management(): - """Test comprehensive state management functionality""" - print("\nTesting state management...") - - model = OpenAIChat(model_name="gpt-4.1") - - # Create temporary directory for test files - with tempfile.TemporaryDirectory() as temp_dir: - state_path = os.path.join(temp_dir, "agent_state.json") - - # Create agent with initial state - agent1 = Agent( - agent_name="State-Test-Agent", - llm=model, - max_loops=1, - saved_state_path=state_path, - ) - - # Add some data to the agent - agent1.run("Remember this: Test message 1") - agent1.add_memory("Test message 2") - - # Save state - agent1.save() - assert os.path.exists(state_path), "State file not created" - - # Create new agent and load state - agent2 = Agent( - agent_name="State-Test-Agent", llm=model, max_loops=1 - ) - agent2.load(state_path) - - # Verify state loaded correctly - history2 = agent2.short_memory.return_history_as_string() - assert ( - "Test message 1" in history2 - ), "State not loaded correctly" - assert ( - "Test message 2" in history2 - ), "Memory not loaded correctly" - - # Test autosave functionality - agent3 = Agent( - agent_name="Autosave-Test-Agent", - llm=model, - max_loops=1, - saved_state_path=os.path.join( - temp_dir, "autosave_state.json" - ), - autosave=True, - ) - - agent3.run("Test autosave") - time.sleep(2) # Wait for autosave - assert os.path.exists( - os.path.join(temp_dir, "autosave_state.json") - ), "Autosave file not created" - - print("āœ“ State management test passed") - - -def test_agent_tools_and_execution(): - """Test agent tool handling and execution""" - print("\nTesting tools and execution...") - - def sample_tool(x: int, y: int) -> int: - """Sample tool that adds two numbers""" - return x + y - - model = OpenAIChat(model_name="gpt-4.1") - agent = Agent( - agent_name="Tools-Test-Agent", - llm=model, - max_loops=1, - tools=[sample_tool], - ) - - # Test adding tools - agent.add_tool(lambda x: x * 2) - assert len(agent.tools) == 2, "Tool not added correctly" - - # Test removing tools - agent.remove_tool(sample_tool) - assert len(agent.tools) == 1, "Tool not removed correctly" - - # Test tool execution - response = agent.run("Calculate 2 + 2 using the sample tool") - assert response is not None, "Tool execution failed" - - print("āœ“ Tools and execution test passed") - - -def test_agent_concurrent_execution(): - """Test agent concurrent execution capabilities""" - print("\nTesting concurrent execution...") - - model = OpenAIChat(model_name="gpt-4.1") - agent = Agent( - agent_name="Concurrent-Test-Agent", llm=model, max_loops=1 - ) - - # Test bulk run - tasks = [ - {"task": "Count to 3"}, - {"task": "Say hello"}, - {"task": "Tell a short joke"}, - ] - - responses = agent.bulk_run(tasks) - assert len(responses) == len(tasks), "Not all tasks completed" - assert all( - response is not None for response in responses - ), "Some tasks failed" - - # Test concurrent tasks - concurrent_responses = agent.run_concurrent_tasks( - ["Task 1", "Task 2", "Task 3"] - ) - assert ( - len(concurrent_responses) == 3 - ), "Not all concurrent tasks completed" - - print("āœ“ Concurrent execution test passed") - - -def test_agent_error_handling(): - """Test agent error handling and recovery""" - print("\nTesting error handling...") - - model = OpenAIChat(model_name="gpt-4.1") - agent = Agent( - agent_name="Error-Test-Agent", - llm=model, - max_loops=1, - retry_attempts=3, - retry_interval=1, - ) - - # Test invalid tool execution - try: - agent.parse_and_execute_tools("invalid_json") - print("āœ“ Invalid tool execution handled") - except Exception: - assert True, "Expected error caught" - - # Test recovery after error - response = agent.run("Continue after error") - assert response is not None, "Agent failed to recover after error" - - print("āœ“ Error handling test passed") - - -def test_agent_configuration(): - """Test agent configuration and parameters""" - print("\nTesting agent configuration...") - - model = OpenAIChat(model_name="gpt-4.1") - agent = Agent( - agent_name="Config-Test-Agent", - llm=model, - max_loops=1, - temperature=0.7, - max_tokens=4000, - context_length=8192, - ) - - # Test configuration methods - agent.update_system_prompt("New system prompt") - agent.update_max_loops(2) - agent.update_loop_interval(2) - - # Verify updates - assert agent.max_loops == 2, "Max loops not updated" - assert agent.loop_interval == 2, "Loop interval not updated" - - # Test configuration export - config_dict = agent.to_dict() - assert isinstance( - config_dict, dict - ), "Configuration export failed" - - # Test YAML export - yaml_config = agent.to_yaml() - assert isinstance(yaml_config, str), "YAML export failed" - - print("āœ“ Configuration test passed") - - -def test_agent_with_stopping_condition(): - """Test agent with custom stopping condition""" - print("\nTesting agent with stopping condition...") - - def custom_stopping_condition(response: str) -> bool: - return "STOP" in response.upper() - - model = OpenAIChat(model_name="gpt-4.1") - agent = Agent( - agent_name="Stopping-Condition-Agent", - llm=model, - max_loops=5, - stopping_condition=custom_stopping_condition, - ) - - response = agent.run("Count up until you see the word STOP") - assert response is not None, "Stopping condition test failed" - print("āœ“ Stopping condition test passed") - - -def test_agent_with_retry_mechanism(): - """Test agent retry mechanism""" - print("\nTesting agent retry mechanism...") - - model = OpenAIChat(model_name="gpt-4.1") - agent = Agent( - agent_name="Retry-Test-Agent", - llm=model, - max_loops=1, - retry_attempts=3, - retry_interval=1, - ) - - response = agent.run("Tell me a joke.") - assert response is not None, "Retry mechanism test failed" - print("āœ“ Retry mechanism test passed") - - -def test_bulk_and_filtered_operations(): - """Test bulk operations and response filtering""" - print("\nTesting bulk and filtered operations...") - - model = OpenAIChat(model_name="gpt-4.1") - agent = Agent( - agent_name="Bulk-Filter-Test-Agent", llm=model, max_loops=1 - ) - - # Test bulk run - bulk_tasks = [ - {"task": "What is 2+2?"}, - {"task": "Name a color"}, - {"task": "Count to 3"}, - ] - bulk_responses = agent.bulk_run(bulk_tasks) - assert len(bulk_responses) == len( - bulk_tasks - ), "Bulk run should return same number of responses as tasks" - - # Test response filtering - agent.add_response_filter("color") - filtered_response = agent.filtered_run( - "What is your favorite color?" - ) - assert ( - "[FILTERED]" in filtered_response - ), "Response filter not applied" - - print("āœ“ Bulk and filtered operations test passed") - - -async def test_async_operations(): - """Test asynchronous operations""" - print("\nTesting async operations...") - - model = OpenAIChat(model_name="gpt-4.1") - agent = Agent( - agent_name="Async-Test-Agent", llm=model, max_loops=1 - ) - - # Test single async run - response = await agent.arun("What is 1+1?") - assert response is not None, "Async run failed" - - # Test concurrent async runs - tasks = ["Task 1", "Task 2", "Task 3"] - responses = await asyncio.gather( - *[agent.arun(task) for task in tasks] - ) - assert len(responses) == len( - tasks - ), "Not all async tasks completed" - - print("āœ“ Async operations test passed") - - -def test_memory_and_state_persistence(): - """Test memory management and state persistence""" - print("\nTesting memory and state persistence...") - - with tempfile.TemporaryDirectory() as temp_dir: - state_path = os.path.join(temp_dir, "test_state.json") - - # Create agent with memory configuration - model = OpenAIChat(model_name="gpt-4.1") - agent1 = Agent( - agent_name="Memory-State-Test-Agent", - llm=model, - max_loops=1, - saved_state_path=state_path, - context_length=8192, - autosave=True, - ) - - # Test memory operations - agent1.add_memory("Important fact: The sky is blue") - agent1.memory_query("What color is the sky?") - - # Save state - agent1.save() - - # Create new agent and load state - agent2 = Agent( - agent_name="Memory-State-Test-Agent", - llm=model, - max_loops=1, - ) - agent2.load(state_path) - - # Verify memory persistence - memory_content = ( - agent2.short_memory.return_history_as_string() - ) - assert ( - "sky is blue" in memory_content - ), "Memory not properly persisted" - - print("āœ“ Memory and state persistence test passed") - - -def test_sentiment_and_evaluation(): - """Test sentiment analysis and response evaluation""" - print("\nTesting sentiment analysis and evaluation...") - - def mock_sentiment_analyzer(text): - """Mock sentiment analyzer that returns a score between 0 and 1""" - return 0.7 if "positive" in text.lower() else 0.3 - - def mock_evaluator(response): - """Mock evaluator that checks response quality""" - return "GOOD" if len(response) > 10 else "BAD" - - model = OpenAIChat(model_name="gpt-4.1") - agent = Agent( - agent_name="Sentiment-Eval-Test-Agent", - llm=model, - max_loops=1, - sentiment_analyzer=mock_sentiment_analyzer, - sentiment_threshold=0.5, - evaluator=mock_evaluator, - ) - - # Test sentiment analysis - agent.run("Generate a positive message") - - # Test evaluation - agent.run("Generate a detailed response") - - print("āœ“ Sentiment and evaluation test passed") - - -def test_tool_management(): - """Test tool management functionality""" - print("\nTesting tool management...") - - def tool1(x: int) -> int: - """Sample tool 1""" - return x * 2 - - def tool2(x: int) -> int: - """Sample tool 2""" - return x + 2 - - model = OpenAIChat(model_name="gpt-4.1") - agent = Agent( - agent_name="Tool-Test-Agent", - llm=model, - max_loops=1, - tools=[tool1], - ) - - # Test adding tools - agent.add_tool(tool2) - assert len(agent.tools) == 2, "Tool not added correctly" - - # Test removing tools - agent.remove_tool(tool1) - assert len(agent.tools) == 1, "Tool not removed correctly" - - # Test adding multiple tools - agent.add_tools([tool1, tool2]) - assert len(agent.tools) == 3, "Multiple tools not added correctly" - - print("āœ“ Tool management test passed") - - -def test_system_prompt_and_configuration(): - """Test system prompt and configuration updates""" - print("\nTesting system prompt and configuration...") - - model = OpenAIChat(model_name="gpt-4.1") - agent = Agent( - agent_name="Config-Test-Agent", llm=model, max_loops=1 - ) - - # Test updating system prompt - new_prompt = "You are a helpful assistant." - agent.update_system_prompt(new_prompt) - assert ( - agent.system_prompt == new_prompt - ), "System prompt not updated" - - # Test configuration updates - agent.update_max_loops(5) - assert agent.max_loops == 5, "Max loops not updated" - - agent.update_loop_interval(2) - assert agent.loop_interval == 2, "Loop interval not updated" - - # Test configuration export - config_dict = agent.to_dict() - assert isinstance( - config_dict, dict - ), "Configuration export failed" - - print("āœ“ System prompt and configuration test passed") - - -def test_agent_with_dynamic_temperature(): - """Test agent with dynamic temperature""" - print("\nTesting agent with dynamic temperature...") - - model = OpenAIChat(model_name="gpt-4.1") - agent = Agent( - agent_name="Dynamic-Temp-Agent", - llm=model, - max_loops=2, - dynamic_temperature_enabled=True, - ) - - response = agent.run("Generate a creative story.") - assert response is not None, "Dynamic temperature test failed" - print("āœ“ Dynamic temperature test passed") - - -def run_all_tests(): - """Run all test functions""" - print("Starting Extended Agent functional tests...\n") - - test_functions = [ - test_basic_agent_functionality, - test_memory_management, - test_agent_output_formats, - test_agent_state_management, - test_agent_tools_and_execution, - test_agent_concurrent_execution, - test_agent_error_handling, - test_agent_configuration, - test_agent_with_stopping_condition, - test_agent_with_retry_mechanism, - test_agent_with_dynamic_temperature, - test_bulk_and_filtered_operations, - test_memory_and_state_persistence, - test_sentiment_and_evaluation, - test_tool_management, - test_system_prompt_and_configuration, - ] - - # Run synchronous tests - total_tests = len(test_functions) + 1 # +1 for async test - passed_tests = 0 - - for test in test_functions: - try: - test() - passed_tests += 1 - except Exception as e: - print(f"āœ— Test {test.__name__} failed: {str(e)}") - - # Run async test - try: - asyncio.run(test_async_operations()) - passed_tests += 1 - except Exception as e: - print(f"āœ— Async operations test failed: {str(e)}") - - print("\nExtended Test Summary:") - print(f"Total Tests: {total_tests}") - print(f"Passed: {passed_tests}") - print(f"Failed: {total_tests - passed_tests}") - print(f"Success Rate: {(passed_tests/total_tests)*100:.2f}%") - - -if __name__ == "__main__": - run_all_tests() diff --git a/tests/structs/test_agent_router.py b/tests/structs/test_agent_router.py new file mode 100644 index 00000000..2b819bf3 --- /dev/null +++ b/tests/structs/test_agent_router.py @@ -0,0 +1,381 @@ +import pytest +from unittest.mock import Mock, patch + +from swarms.structs.agent_router import AgentRouter +from swarms.structs.agent import Agent + + +@pytest.fixture +def test_agent(): + """Create a real agent for testing.""" + with patch("swarms.structs.agent.LiteLLM") as mock_llm: + mock_llm.return_value.run.return_value = "Test response" + return Agent( + agent_name="test_agent", + agent_description="A test agent", + system_prompt="You are a test agent", + model_name="gpt-4o-mini", + max_loops=1, + verbose=False, + print_on=False, + ) + + +def test_agent_router_initialization_default(): + """Test AgentRouter initialization with default parameters.""" + with patch("swarms.structs.agent_router.embedding"): + router = AgentRouter() + + assert router.embedding_model == "text-embedding-ada-002" + assert router.n_agents == 1 + assert router.api_key is None + assert router.api_base is None + assert router.agents == [] + assert router.agent_embeddings == [] + assert router.agent_metadata == [] + + +def test_agent_router_initialization_custom(): + """Test AgentRouter initialization with custom parameters.""" + with patch("swarms.structs.agent_router.embedding"), patch( + "swarms.structs.agent.LiteLLM" + ) as mock_llm: + mock_llm.return_value.run.return_value = "Test response" + agents = [ + Agent( + agent_name="test1", + model_name="gpt-4o-mini", + max_loops=1, + verbose=False, + print_on=False, + ), + Agent( + agent_name="test2", + model_name="gpt-4o-mini", + max_loops=1, + verbose=False, + print_on=False, + ), + ] + router = AgentRouter( + embedding_model="custom-model", + n_agents=3, + api_key="custom_key", + api_base="custom_base", + agents=agents, + ) + + assert router.embedding_model == "custom-model" + assert router.n_agents == 3 + assert router.api_key == "custom_key" + assert router.api_base == "custom_base" + assert len(router.agents) == 2 + + +def test_cosine_similarity_identical_vectors(): + """Test cosine similarity with identical vectors.""" + router = AgentRouter() + vec1 = [1.0, 0.0, 0.0] + vec2 = [1.0, 0.0, 0.0] + + result = router._cosine_similarity(vec1, vec2) + assert result == 1.0 + + +def test_cosine_similarity_orthogonal_vectors(): + """Test cosine similarity with orthogonal vectors.""" + router = AgentRouter() + vec1 = [1.0, 0.0, 0.0] + vec2 = [0.0, 1.0, 0.0] + + result = router._cosine_similarity(vec1, vec2) + assert result == 0.0 + + +def test_cosine_similarity_opposite_vectors(): + """Test cosine similarity with opposite vectors.""" + router = AgentRouter() + vec1 = [1.0, 0.0, 0.0] + vec2 = [-1.0, 0.0, 0.0] + + result = router._cosine_similarity(vec1, vec2) + assert result == -1.0 + + +def test_cosine_similarity_different_lengths(): + """Test cosine similarity with vectors of different lengths.""" + router = AgentRouter() + vec1 = [1.0, 0.0] + vec2 = [1.0, 0.0, 0.0] + + with pytest.raises( + ValueError, match="Vectors must have the same length" + ): + router._cosine_similarity(vec1, vec2) + + +@patch("swarms.structs.agent_router.embedding") +def test_generate_embedding_success(mock_embedding): + """Test successful embedding generation.""" + mock_embedding.return_value.data = [ + Mock(embedding=[0.1, 0.2, 0.3, 0.4]) + ] + + router = AgentRouter() + result = router._generate_embedding("test text") + + assert result == [0.1, 0.2, 0.3, 0.4] + mock_embedding.assert_called_once() + + +@patch("swarms.structs.agent_router.embedding") +def test_generate_embedding_error(mock_embedding): + """Test embedding generation error handling.""" + mock_embedding.side_effect = Exception("API Error") + + router = AgentRouter() + + with pytest.raises(Exception, match="API Error"): + router._generate_embedding("test text") + + +@patch("swarms.structs.agent_router.embedding") +def test_add_agent_success(mock_embedding, test_agent): + """Test successful agent addition.""" + mock_embedding.return_value.data = [ + Mock(embedding=[0.1, 0.2, 0.3]) + ] + + router = AgentRouter() + router.add_agent(test_agent) + + assert len(router.agents) == 1 + assert len(router.agent_embeddings) == 1 + assert len(router.agent_metadata) == 1 + assert router.agents[0] == test_agent + assert router.agent_embeddings[0] == [0.1, 0.2, 0.3] + assert router.agent_metadata[0]["name"] == "test_agent" + + +@patch("swarms.structs.agent_router.embedding") +def test_add_agent_retry_error(mock_embedding, test_agent): + """Test agent addition with retry mechanism failure.""" + mock_embedding.side_effect = Exception("Embedding error") + + router = AgentRouter() + + # Should raise RetryError after retries are exhausted + with pytest.raises(Exception) as exc_info: + router.add_agent(test_agent) + + # Check that it's a retry error or contains the original error + assert "Embedding error" in str( + exc_info.value + ) or "RetryError" in str(exc_info.value) + + +@patch("swarms.structs.agent_router.embedding") +def test_add_agents_multiple(mock_embedding): + """Test adding multiple agents.""" + mock_embedding.return_value.data = [ + Mock(embedding=[0.1, 0.2, 0.3]) + ] + + with patch("swarms.structs.agent.LiteLLM") as mock_llm: + mock_llm.return_value.run.return_value = "Test response" + router = AgentRouter() + agents = [ + Agent( + agent_name="agent1", + model_name="gpt-4o-mini", + max_loops=1, + verbose=False, + print_on=False, + ), + Agent( + agent_name="agent2", + model_name="gpt-4o-mini", + max_loops=1, + verbose=False, + print_on=False, + ), + Agent( + agent_name="agent3", + model_name="gpt-4o-mini", + max_loops=1, + verbose=False, + print_on=False, + ), + ] + + router.add_agents(agents) + + assert len(router.agents) == 3 + assert len(router.agent_embeddings) == 3 + assert len(router.agent_metadata) == 3 + + +@patch("swarms.structs.agent_router.embedding") +def test_find_best_agent_success(mock_embedding): + """Test successful best agent finding.""" + # Mock embeddings for agents and task + mock_embedding.side_effect = [ + Mock(data=[Mock(embedding=[0.1, 0.2, 0.3])]), # agent1 + Mock(data=[Mock(embedding=[0.4, 0.5, 0.6])]), # agent2 + Mock(data=[Mock(embedding=[0.7, 0.8, 0.9])]), # task + ] + + with patch("swarms.structs.agent.LiteLLM") as mock_llm: + mock_llm.return_value.run.return_value = "Test response" + router = AgentRouter() + agent1 = Agent( + agent_name="agent1", + agent_description="First agent", + system_prompt="Prompt 1", + model_name="gpt-4o-mini", + max_loops=1, + verbose=False, + print_on=False, + ) + agent2 = Agent( + agent_name="agent2", + agent_description="Second agent", + system_prompt="Prompt 2", + model_name="gpt-4o-mini", + max_loops=1, + verbose=False, + print_on=False, + ) + + router.add_agent(agent1) + router.add_agent(agent2) + + # Mock the similarity calculation to return predictable results + with patch.object( + router, "_cosine_similarity" + ) as mock_similarity: + mock_similarity.side_effect = [ + 0.8, + 0.6, + ] # agent1 more similar + + result = router.find_best_agent("test task") + + assert result == agent1 + + +def test_find_best_agent_no_agents(): + """Test finding best agent when no agents are available.""" + with patch("swarms.structs.agent_router.embedding"): + router = AgentRouter() + + result = router.find_best_agent("test task") + + assert result is None + + +@patch("swarms.structs.agent_router.embedding") +def test_find_best_agent_retry_error(mock_embedding): + """Test error handling in find_best_agent with retry mechanism.""" + mock_embedding.side_effect = Exception("API Error") + + with patch("swarms.structs.agent.LiteLLM") as mock_llm: + mock_llm.return_value.run.return_value = "Test response" + router = AgentRouter() + router.agents = [ + Agent( + agent_name="agent1", + model_name="gpt-4o-mini", + max_loops=1, + verbose=False, + print_on=False, + ) + ] + router.agent_embeddings = [[0.1, 0.2, 0.3]] + + # Should raise RetryError after retries are exhausted + with pytest.raises(Exception) as exc_info: + router.find_best_agent("test task") + + # Check that it's a retry error or contains the original error + assert "API Error" in str( + exc_info.value + ) or "RetryError" in str(exc_info.value) + + +@patch("swarms.structs.agent_router.embedding") +def test_update_agent_history_success(mock_embedding, test_agent): + """Test successful agent history update.""" + mock_embedding.return_value.data = [ + Mock(embedding=[0.1, 0.2, 0.3]) + ] + + router = AgentRouter() + router.add_agent(test_agent) + + # Update agent history + router.update_agent_history("test_agent") + + # Verify the embedding was regenerated + assert ( + mock_embedding.call_count == 2 + ) # Once for add, once for update + + +def test_update_agent_history_agent_not_found(): + """Test updating history for non-existent agent.""" + with patch( + "swarms.structs.agent_router.embedding" + ) as mock_embedding: + mock_embedding.return_value.data = [ + Mock(embedding=[0.1, 0.2, 0.3]) + ] + router = AgentRouter() + + # Should not raise an exception, just log a warning + router.update_agent_history("non_existent_agent") + + +@patch("swarms.structs.agent_router.embedding") +def test_agent_metadata_structure(mock_embedding, test_agent): + """Test the structure of agent metadata.""" + mock_embedding.return_value.data = [ + Mock(embedding=[0.1, 0.2, 0.3]) + ] + + router = AgentRouter() + router.add_agent(test_agent) + + metadata = router.agent_metadata[0] + assert "name" in metadata + assert "text" in metadata + assert metadata["name"] == "test_agent" + assert ( + "test_agent A test agent You are a test agent" + in metadata["text"] + ) + + +def test_agent_router_edge_cases(): + """Test various edge cases.""" + with patch( + "swarms.structs.agent_router.embedding" + ) as mock_embedding: + mock_embedding.return_value.data = [ + Mock(embedding=[0.1, 0.2, 0.3]) + ] + + router = AgentRouter() + + # Test with empty string task + result = router.find_best_agent("") + assert result is None + + # Test with very long task description + long_task = "test " * 1000 + result = router.find_best_agent(long_task) + assert result is None + + +if __name__ == "__main__": + pytest.main([__file__]) diff --git a/tests/structs/test_agentrearrange.py b/tests/structs/test_agentrearrange.py deleted file mode 100644 index 42c3c1bc..00000000 --- a/tests/structs/test_agentrearrange.py +++ /dev/null @@ -1,328 +0,0 @@ -import os -import traceback -from datetime import datetime -from typing import Callable, Dict, List, Optional - -from loguru import logger -from swarm_models import OpenAIChat - -from swarms.structs.agent import Agent -from swarms.structs.agent_rearrange import AgentRearrange - - -class TestResult: - """Class to store test results and metadata""" - - def __init__(self, test_name: str): - self.test_name = test_name - self.start_time = datetime.now() - self.end_time = None - self.success = False - self.error = None - self.traceback = None - self.function_output = None - - def complete( - self, success: bool, error: Optional[Exception] = None - ): - """Complete the test execution with results""" - self.end_time = datetime.now() - self.success = success - if error: - self.error = str(error) - self.traceback = traceback.format_exc() - - def duration(self) -> float: - """Calculate test duration in seconds""" - if self.end_time: - return (self.end_time - self.start_time).total_seconds() - return 0 - - -def run_test(test_func: Callable) -> TestResult: - """ - Decorator to run tests with error handling and logging - - Args: - test_func (Callable): Test function to execute - - Returns: - TestResult: Object containing test execution details - """ - - def wrapper(*args, **kwargs) -> TestResult: - result = TestResult(test_func.__name__) - logger.info( - f"\n{'='*20} Running test: {test_func.__name__} {'='*20}" - ) - - try: - output = test_func(*args, **kwargs) - result.function_output = output - result.complete(success=True) - logger.success( - f"āœ… Test {test_func.__name__} passed successfully" - ) - - except Exception as e: - result.complete(success=False, error=e) - logger.error( - f"āŒ Test {test_func.__name__} failed with error: {str(e)}" - ) - logger.error(f"Traceback: {traceback.format_exc()}") - - logger.info( - f"Test duration: {result.duration():.2f} seconds\n" - ) - return result - - return wrapper - - -def create_functional_agents() -> List[Agent]: - """ - Create a list of functional agents with real LLM integration for testing. - Using OpenAI's GPT model for realistic agent behavior testing. - """ - # Initialize OpenAI Chat model - api_key = os.getenv("OPENAI_API_KEY") - if not api_key: - logger.warning( - "No OpenAI API key found. Using mock agents instead." - ) - return [ - create_mock_agent("TestAgent1"), - create_mock_agent("TestAgent2"), - ] - - try: - model = OpenAIChat( - api_key=api_key, model_name="gpt-4.1", temperature=0.1 - ) - - # Create boss agent - boss_agent = Agent( - agent_name="BossAgent", - system_prompt=""" - You are the BossAgent responsible for managing and overseeing test scenarios. - Your role is to coordinate tasks between agents and ensure efficient collaboration. - Analyze inputs, break down tasks, and provide clear directives to other agents. - Maintain a structured approach to task management and result compilation. - """, - llm=model, - max_loops=1, - dashboard=False, - streaming_on=True, - verbose=True, - stopping_token="", - state_save_file_type="json", - saved_state_path="test_boss_agent.json", - ) - - # Create analysis agent - analysis_agent = Agent( - agent_name="AnalysisAgent", - system_prompt=""" - You are the AnalysisAgent responsible for detailed data processing and analysis. - Your role is to examine input data, identify patterns, and provide analytical insights. - Focus on breaking down complex information into clear, actionable components. - """, - llm=model, - max_loops=1, - dashboard=False, - streaming_on=True, - verbose=True, - stopping_token="", - state_save_file_type="json", - saved_state_path="test_analysis_agent.json", - ) - - # Create summary agent - summary_agent = Agent( - agent_name="SummaryAgent", - system_prompt=""" - You are the SummaryAgent responsible for consolidating and summarizing information. - Your role is to take detailed analysis and create concise, actionable summaries. - Focus on highlighting key points and ensuring clarity in communication. - """, - llm=model, - max_loops=1, - dashboard=False, - streaming_on=True, - verbose=True, - stopping_token="", - state_save_file_type="json", - saved_state_path="test_summary_agent.json", - ) - - logger.info( - "Successfully created functional agents with LLM integration" - ) - return [boss_agent, analysis_agent, summary_agent] - - except Exception as e: - logger.error(f"Failed to create functional agents: {str(e)}") - logger.warning("Falling back to mock agents") - return [ - create_mock_agent("TestAgent1"), - create_mock_agent("TestAgent2"), - ] - - -def create_mock_agent(name: str) -> Agent: - """Create a mock agent for testing when LLM integration is not available""" - return Agent( - agent_name=name, - system_prompt=f"You are a test agent named {name}", - llm=None, - ) - - -@run_test -def test_init(): - """Test AgentRearrange initialization with functional agents""" - logger.info("Creating agents for initialization test") - agents = create_functional_agents() - - rearrange = AgentRearrange( - name="TestRearrange", - agents=agents, - flow=f"{agents[0].agent_name} -> {agents[1].agent_name} -> {agents[2].agent_name}", - ) - - assert rearrange.name == "TestRearrange" - assert len(rearrange.agents) == 3 - assert ( - rearrange.flow - == f"{agents[0].agent_name} -> {agents[1].agent_name} -> {agents[2].agent_name}" - ) - - logger.info( - f"Initialized AgentRearrange with {len(agents)} agents" - ) - return True - - -@run_test -def test_validate_flow(): - """Test flow validation logic""" - agents = create_functional_agents() - rearrange = AgentRearrange( - agents=agents, - flow=f"{agents[0].agent_name} -> {agents[1].agent_name}", - ) - - logger.info("Testing valid flow pattern") - valid = rearrange.validate_flow() - assert valid is True - - logger.info("Testing invalid flow pattern") - rearrange.flow = f"{agents[0].agent_name} {agents[1].agent_name}" # Missing arrow - try: - rearrange.validate_flow() - assert False, "Should have raised ValueError" - except ValueError as e: - logger.info( - f"Successfully caught invalid flow error: {str(e)}" - ) - assert True - - return True - - -@run_test -def test_add_remove_agent(): - """Test adding and removing agents from the swarm""" - agents = create_functional_agents() - rearrange = AgentRearrange( - agents=agents[:2] - ) # Start with first two agents - - logger.info("Testing agent addition") - new_agent = agents[2] # Use the third agent as new agent - rearrange.add_agent(new_agent) - assert new_agent.agent_name in rearrange.agents - - logger.info("Testing agent removal") - rearrange.remove_agent(new_agent.agent_name) - assert new_agent.agent_name not in rearrange.agents - - return True - - -@run_test -def test_basic_run(): - """Test basic task execution with the swarm""" - agents = create_functional_agents() - rearrange = AgentRearrange( - name="TestSwarm", - agents=agents, - flow=f"{agents[0].agent_name} -> {agents[1].agent_name} -> {agents[2].agent_name}", - max_loops=1, - ) - - test_task = ( - "Analyze this test message and provide a brief summary." - ) - logger.info(f"Running test task: {test_task}") - - try: - result = rearrange.run(test_task) - assert result is not None - logger.info( - f"Successfully executed task with result length: {len(str(result))}" - ) - return True - except Exception as e: - logger.error(f"Task execution failed: {str(e)}") - raise - - -def run_all_tests() -> Dict[str, TestResult]: - """ - Run all test cases and collect results - - Returns: - Dict[str, TestResult]: Dictionary mapping test names to their results - """ - logger.info("\nšŸš€ Starting AgentRearrange test suite execution") - test_functions = [ - test_init, - test_validate_flow, - test_add_remove_agent, - test_basic_run, - ] - - results = {} - for test in test_functions: - result = test() - results[test.__name__] = result - - # Log summary - total_tests = len(results) - passed_tests = sum(1 for r in results.values() if r.success) - failed_tests = total_tests - passed_tests - - logger.info("\nšŸ“Š Test Suite Summary:") - logger.info(f"Total Tests: {total_tests}") - print(f"āœ… Passed: {passed_tests}") - - if failed_tests > 0: - logger.error(f"āŒ Failed: {failed_tests}") - - # Detailed failure information - if failed_tests > 0: - logger.error("\nāŒ Failed Tests Details:") - for name, result in results.items(): - if not result.success: - logger.error(f"\n{name}:") - logger.error(f"Error: {result.error}") - logger.error(f"Traceback: {result.traceback}") - - return results - - -if __name__ == "__main__": - print("šŸŒŸ Starting AgentRearrange Test Suite") - results = run_all_tests() - print("šŸ Test Suite Execution Completed") diff --git a/tests/structs/test_airflow_swarm.py b/tests/structs/test_airflow_swarm.py deleted file mode 100644 index 0fdfeb20..00000000 --- a/tests/structs/test_airflow_swarm.py +++ /dev/null @@ -1,313 +0,0 @@ -import time - -from loguru import logger -from swarms import Agent - -from experimental.airflow_swarm import ( - AirflowDAGSwarm, - NodeType, - Conversation, -) - -# Configure logger -logger.remove() -logger.add(lambda msg: print(msg, end=""), level="DEBUG") - - -def test_swarm_initialization(): - """Test basic swarm initialization and configuration.""" - try: - swarm = AirflowDAGSwarm( - dag_id="test_dag", - name="Test DAG", - initial_message="Test message", - ) - assert swarm.dag_id == "test_dag", "DAG ID not set correctly" - assert swarm.name == "Test DAG", "Name not set correctly" - assert ( - len(swarm.nodes) == 0 - ), "Nodes should be empty on initialization" - assert ( - len(swarm.edges) == 0 - ), "Edges should be empty on initialization" - - # Test initial message - conv_json = swarm.get_conversation_history() - assert ( - "Test message" in conv_json - ), "Initial message not set correctly" - print("āœ… Swarm initialization test passed") - return True - except AssertionError as e: - print(f"āŒ Swarm initialization test failed: {str(e)}") - return False - - -def test_node_addition(): - """Test adding different types of nodes to the swarm.""" - try: - swarm = AirflowDAGSwarm(dag_id="test_dag") - - # Test adding an agent node - agent = Agent( - agent_name="Test-Agent", - system_prompt="Test prompt", - model_name="gpt-4o-mini", - max_loops=1, - ) - agent_id = swarm.add_node( - "test_agent", - agent, - NodeType.AGENT, - query="Test query", - concurrent=True, - ) - assert ( - agent_id == "test_agent" - ), "Agent node ID not returned correctly" - assert ( - "test_agent" in swarm.nodes - ), "Agent node not added to nodes dict" - - # Test adding a callable node - def test_callable(x: int, conversation: Conversation) -> str: - return f"Test output {x}" - - callable_id = swarm.add_node( - "test_callable", - test_callable, - NodeType.CALLABLE, - args=[42], - concurrent=False, - ) - assert ( - callable_id == "test_callable" - ), "Callable node ID not returned correctly" - assert ( - "test_callable" in swarm.nodes - ), "Callable node not added to nodes dict" - - print("āœ… Node addition test passed") - return True - except AssertionError as e: - print(f"āŒ Node addition test failed: {str(e)}") - return False - except Exception as e: - print( - f"āŒ Node addition test failed with unexpected error: {str(e)}" - ) - return False - - -def test_edge_addition(): - """Test adding edges between nodes.""" - try: - swarm = AirflowDAGSwarm(dag_id="test_dag") - - # Add two nodes - def node1_fn(conversation: Conversation) -> str: - return "Node 1 output" - - def node2_fn(conversation: Conversation) -> str: - return "Node 2 output" - - swarm.add_node("node1", node1_fn, NodeType.CALLABLE) - swarm.add_node("node2", node2_fn, NodeType.CALLABLE) - - # Add edge between them - swarm.add_edge("node1", "node2") - - assert ( - "node2" in swarm.edges["node1"] - ), "Edge not added correctly" - assert ( - len(swarm.edges["node1"]) == 1 - ), "Incorrect number of edges" - - # Test adding edge with non-existent node - try: - swarm.add_edge("node1", "non_existent") - assert ( - False - ), "Should raise ValueError for non-existent node" - except ValueError: - pass - - print("āœ… Edge addition test passed") - return True - except AssertionError as e: - print(f"āŒ Edge addition test failed: {str(e)}") - return False - - -def test_execution_order(): - """Test that nodes are executed in the correct order based on dependencies.""" - try: - swarm = AirflowDAGSwarm(dag_id="test_dag") - execution_order = [] - - def node1(conversation: Conversation) -> str: - execution_order.append("node1") - return "Node 1 output" - - def node2(conversation: Conversation) -> str: - execution_order.append("node2") - return "Node 2 output" - - def node3(conversation: Conversation) -> str: - execution_order.append("node3") - return "Node 3 output" - - # Add nodes - swarm.add_node( - "node1", node1, NodeType.CALLABLE, concurrent=False - ) - swarm.add_node( - "node2", node2, NodeType.CALLABLE, concurrent=False - ) - swarm.add_node( - "node3", node3, NodeType.CALLABLE, concurrent=False - ) - - # Add edges to create a chain: node1 -> node2 -> node3 - swarm.add_edge("node1", "node2") - swarm.add_edge("node2", "node3") - - # Execute - swarm.run() - - # Check execution order - assert execution_order == [ - "node1", - "node2", - "node3", - ], "Incorrect execution order" - print("āœ… Execution order test passed") - return True - except AssertionError as e: - print(f"āŒ Execution order test failed: {str(e)}") - return False - - -def test_concurrent_execution(): - """Test concurrent execution of nodes.""" - try: - swarm = AirflowDAGSwarm(dag_id="test_dag") - - def slow_node1(conversation: Conversation) -> str: - time.sleep(0.5) - return "Slow node 1 output" - - def slow_node2(conversation: Conversation) -> str: - time.sleep(0.5) - return "Slow node 2 output" - - # Add nodes with concurrent=True - swarm.add_node( - "slow1", slow_node1, NodeType.CALLABLE, concurrent=True - ) - swarm.add_node( - "slow2", slow_node2, NodeType.CALLABLE, concurrent=True - ) - - # Measure execution time - start_time = time.time() - swarm.run() - execution_time = time.time() - start_time - - # Should take ~0.5s for concurrent execution, not ~1s - assert ( - execution_time < 0.8 - ), "Concurrent execution took too long" - print("āœ… Concurrent execution test passed") - return True - except AssertionError as e: - print(f"āŒ Concurrent execution test failed: {str(e)}") - return False - - -def test_conversation_handling(): - """Test conversation management within the swarm.""" - try: - swarm = AirflowDAGSwarm( - dag_id="test_dag", initial_message="Initial test message" - ) - - # Test adding user messages - swarm.add_user_message("Test message 1") - swarm.add_user_message("Test message 2") - - history = swarm.get_conversation_history() - assert ( - "Initial test message" in history - ), "Initial message not in history" - assert ( - "Test message 1" in history - ), "First message not in history" - assert ( - "Test message 2" in history - ), "Second message not in history" - - print("āœ… Conversation handling test passed") - return True - except AssertionError as e: - print(f"āŒ Conversation handling test failed: {str(e)}") - return False - - -def test_error_handling(): - """Test error handling in node execution.""" - try: - swarm = AirflowDAGSwarm(dag_id="test_dag") - - def failing_node(conversation: Conversation) -> str: - raise ValueError("Test error") - - swarm.add_node("failing", failing_node, NodeType.CALLABLE) - - # Execute should not raise an exception - result = swarm.run() - - assert ( - "Error" in result - ), "Error not captured in execution result" - assert ( - "Test error" in result - ), "Specific error message not captured" - - print("āœ… Error handling test passed") - return True - except Exception as e: - print(f"āŒ Error handling test failed: {str(e)}") - return False - - -def run_all_tests(): - """Run all test functions and report results.""" - tests = [ - test_swarm_initialization, - test_node_addition, - test_edge_addition, - test_execution_order, - test_concurrent_execution, - test_conversation_handling, - test_error_handling, - ] - - results = [] - for test in tests: - print(f"\nRunning {test.__name__}...") - result = test() - results.append(result) - - total = len(results) - passed = sum(results) - print("\n=== Test Results ===") - print(f"Total tests: {total}") - print(f"Passed: {passed}") - print(f"Failed: {total - passed}") - print("==================") - - -if __name__ == "__main__": - run_all_tests() diff --git a/tests/structs/test_aop.py b/tests/structs/test_aop.py new file mode 100644 index 00000000..b7ca007e --- /dev/null +++ b/tests/structs/test_aop.py @@ -0,0 +1,1360 @@ +import socket +from unittest.mock import Mock, patch + +import pytest + +from swarms.structs.agent import Agent +from swarms.structs.aop import ( + AOP, + AOPCluster, + QueueStatus, + TaskStatus, +) + + +@pytest.fixture +def real_agent(): + """Create a real agent for testing using example.py configuration.""" + from swarms import Agent + + agent = Agent( + agent_name="Test-Agent", + agent_description="Test agent for AOP testing", + model_name="gpt-3.5-turbo", + max_loops=1, + temperature=0.5, + max_tokens=4096, + ) + return agent + + +@pytest.fixture +def real_agents(): + """Create multiple real agents for batch testing.""" + from swarms import Agent + + agents = [] + for i in range(3): + agent = Agent( + agent_name=f"Test-Agent-{i}", + agent_description=f"Test agent {i} for AOP testing", + model_name="gpt-3.5-turbo", + max_loops=1, + temperature=0.5, + max_tokens=4096, + ) + agents.append(agent) + return agents + + +@pytest.fixture +def mock_fastmcp(): + """Create a mock FastMCP server.""" + mcp = Mock() + mcp.name = "Test AOP" + mcp.port = 8000 + mcp.log_level = "INFO" + mcp.run = Mock() + mcp.tool = Mock() + return mcp + + +@pytest.fixture +def aop_instance(real_agent, mock_fastmcp): + """Create an AOP instance for testing.""" + with patch( + "swarms.structs.aop.FastMCP", return_value=mock_fastmcp + ), patch("swarms.structs.aop.logger"): + aop = AOP( + server_name="Test AOP", + description="Test AOP description", + agents=[real_agent], + port=8000, + transport="streamable-http", + verbose=True, + traceback_enabled=True, + host="localhost", + queue_enabled=True, + max_workers_per_agent=2, + max_queue_size_per_agent=100, + processing_timeout=30, + retry_delay=1.0, + persistence=False, + max_restart_attempts=5, + restart_delay=2.0, + network_monitoring=True, + max_network_retries=3, + network_retry_delay=5.0, + network_timeout=10.0, + log_level="INFO", + ) + return aop + + +def test_aop_initialization_all_parameters(): + """Test AOP initialization with all parameters.""" + with patch( + "swarms.structs.aop.FastMCP" + ) as mock_fastmcp_class, patch( + "swarms.structs.aop.logger" + ) as mock_logger: + mock_mcp = Mock() + mock_mcp.name = "Test AOP" + mock_mcp.port = 8000 + mock_fastmcp_class.return_value = mock_mcp + + aop = AOP( + server_name="Test AOP", + description="Test description", + agents=None, + port=9000, + transport="sse", + verbose=False, + traceback_enabled=False, + host="127.0.0.1", + queue_enabled=False, + max_workers_per_agent=5, + max_queue_size_per_agent=200, + processing_timeout=60, + retry_delay=2.0, + persistence=True, + max_restart_attempts=10, + restart_delay=10.0, + network_monitoring=False, + max_network_retries=5, + network_retry_delay=15.0, + network_timeout=20.0, + log_level="DEBUG", + ) + + assert aop.server_name == "Test AOP" + assert aop.description == "Test description" + assert aop.verbose is False + assert aop.traceback_enabled is False + assert aop.host == "127.0.0.1" + assert aop.port == 9000 + assert aop.transport == "sse" + assert aop.queue_enabled is False + assert aop.max_workers_per_agent == 5 + assert aop.max_queue_size_per_agent == 200 + assert aop.processing_timeout == 60 + assert aop.retry_delay == 2.0 + assert aop.persistence is True + assert aop.max_restart_attempts == 10 + assert aop.restart_delay == 10.0 + assert aop.network_monitoring is False + assert aop.max_network_retries == 5 + assert aop.network_retry_delay == 15.0 + assert aop.network_timeout == 20.0 + assert aop.log_level == "DEBUG" + + mock_fastmcp_class.assert_called_once() + mock_logger.remove.assert_called_once() + mock_logger.add.assert_called_once() + + +def test_aop_initialization_minimal_parameters(): + """Test AOP initialization with minimal parameters.""" + with patch( + "swarms.structs.aop.FastMCP" + ) as mock_fastmcp_class, patch("swarms.structs.aop.logger"): + mock_mcp = Mock() + mock_fastmcp_class.return_value = mock_mcp + + aop = AOP() + + assert aop.server_name == "AOP Cluster" + assert ( + aop.description + == "A cluster that enables you to deploy multiple agents as tools in an MCP server." + ) + assert aop.verbose is False + assert aop.traceback_enabled is True + assert aop.host == "localhost" + assert aop.port == 8000 + assert aop.transport == "streamable-http" + assert aop.queue_enabled is True + assert aop.max_workers_per_agent == 1 + assert aop.max_queue_size_per_agent == 1000 + assert aop.processing_timeout == 30 + assert aop.retry_delay == 1.0 + assert aop.persistence is False + assert aop.max_restart_attempts == 10 + assert aop.restart_delay == 5.0 + assert aop.network_monitoring is True + assert aop.max_network_retries == 5 + assert aop.network_retry_delay == 10.0 + assert aop.network_timeout == 30.0 + assert aop.log_level == "INFO" + + +def test_aop_initialization_with_agents( + real_agent, real_agents, mock_fastmcp +): + """Test AOP initialization with multiple agents.""" + with patch( + "swarms.structs.aop.FastMCP", return_value=mock_fastmcp + ), patch("swarms.structs.aop.logger"): + aop = AOP(agents=real_agents) + + assert len(aop.agents) == 3 + assert "Test-Agent-0" in aop.agents + assert "Test-Agent-1" in aop.agents + assert "Test-Agent-2" in aop.agents + + +def test_add_agent_basic(real_agent, aop_instance, mock_fastmcp): + """Test basic agent addition.""" + from swarms import Agent + + new_agent = Agent( + agent_name="new_agent", + agent_description="New agent description", + model_name="gpt-3.5-turbo", + max_loops=1, + temperature=0.5, + max_tokens=4096, + ) + + with patch.object(aop_instance, "_register_tool"), patch.object( + aop_instance, "_register_agent_discovery_tool" + ): + result = aop_instance.add_agent(new_agent) + + assert result == "new_agent" + assert "new_agent" in aop_instance.agents + assert "new_agent" in aop_instance.tool_configs + assert "new_agent" in aop_instance.task_queues + + +def test_add_agent_with_custom_tool_name( + real_agent, aop_instance, mock_fastmcp +): + """Test adding agent with custom tool name.""" + from swarms import Agent + + new_agent = Agent( + agent_name="custom_agent", + agent_description="Custom agent description", + model_name="gpt-3.5-turbo", + max_loops=1, + temperature=0.5, + max_tokens=4096, + ) + + with patch.object(aop_instance, "_register_tool"), patch.object( + aop_instance, "_register_agent_discovery_tool" + ): + result = aop_instance.add_agent( + new_agent, tool_name="custom_tool" + ) + + assert result == "custom_tool" + assert "custom_tool" in aop_instance.agents + assert aop_instance.agents["custom_tool"] == new_agent + + +def test_add_agent_with_custom_schemas( + real_agent, aop_instance, mock_fastmcp +): + """Test adding agent with custom input/output schemas.""" + custom_input_schema = { + "type": "object", + "properties": {"custom_task": {"type": "string"}}, + "required": ["custom_task"], + } + custom_output_schema = { + "type": "object", + "properties": {"custom_result": {"type": "string"}}, + } + + from swarms import Agent + + new_agent = Agent( + agent_name="schema_agent", + agent_description="Schema test agent", + model_name="gpt-3.5-turbo", + max_loops=1, + temperature=0.5, + max_tokens=4096, + ) + + with patch.object(aop_instance, "_register_tool"), patch.object( + aop_instance, "_register_agent_discovery_tool" + ): + result = aop_instance.add_agent( + new_agent, + input_schema=custom_input_schema, + output_schema=custom_output_schema, + ) + + config = aop_instance.tool_configs[result] + assert config.input_schema == custom_input_schema + assert config.output_schema == custom_output_schema + + +def test_add_agent_with_all_parameters( + real_agent, aop_instance, mock_fastmcp +): + """Test adding agent with all configuration parameters.""" + from swarms import Agent + + new_agent = Agent( + agent_name="full_config_agent", + agent_description="Full config test agent", + model_name="gpt-3.5-turbo", + max_loops=1, + temperature=0.5, + max_tokens=4096, + ) + + with patch.object(aop_instance, "_register_tool"), patch.object( + aop_instance, "_register_agent_discovery_tool" + ): + result = aop_instance.add_agent( + new_agent, + tool_name="full_config_tool", + tool_description="Full config tool description", + timeout=60, + max_retries=5, + verbose=True, + traceback_enabled=False, + ) + + config = aop_instance.tool_configs[result] + assert config.tool_name == "full_config_tool" + assert ( + config.tool_description == "Full config tool description" + ) + assert config.timeout == 60 + assert config.max_retries == 5 + assert config.verbose is True + assert config.traceback_enabled is False + + +def test_add_agent_none_agent(aop_instance, mock_fastmcp): + """Test adding None agent raises ValueError.""" + with patch.object(aop_instance, "_register_tool"), patch.object( + aop_instance, "_register_agent_discovery_tool" + ): + with pytest.raises(ValueError, match="Agent cannot be None"): + aop_instance.add_agent(None) + + +def test_add_agent_duplicate_tool_name( + real_agent, aop_instance, mock_fastmcp +): + """Test adding agent with duplicate tool name raises ValueError.""" + from swarms import Agent + + new_agent = Agent( + agent_name="duplicate_agent", + agent_description="Duplicate agent", + model_name="gpt-3.5-turbo", + max_loops=1, + temperature=0.5, + max_tokens=4096, + ) + + with patch.object(aop_instance, "_register_tool"), patch.object( + aop_instance, "_register_agent_discovery_tool" + ): + aop_instance.add_agent(new_agent, tool_name="test_agent") + + with pytest.raises( + ValueError, match="Tool name 'test_agent' already exists" + ): + aop_instance.add_agent(new_agent, tool_name="test_agent") + + +def test_add_agents_batch_basic( + real_agents, aop_instance, mock_fastmcp +): + """Test adding multiple agents in batch.""" + with patch.object( + aop_instance, "add_agent" + ) as mock_add_agent, patch.object( + aop_instance, "_register_agent_discovery_tool" + ): + mock_add_agent.return_value = "test_tool" + + result = aop_instance.add_agents_batch(real_agents) + + assert len(result) == 3 + assert mock_add_agent.call_count == 3 + mock_add_agent.assert_any_call( + real_agents[0], None, None, None, None, 30, 3, None, None + ) + mock_add_agent.assert_any_call( + real_agents[1], None, None, None, None, 30, 3, None, None + ) + mock_add_agent.assert_any_call( + real_agents[2], None, None, None, None, 30, 3, None, None + ) + + +def test_add_agents_batch_with_custom_parameters( + real_agents, aop_instance, mock_fastmcp +): + """Test adding multiple agents in batch with custom parameters.""" + tool_names = ["tool1", "tool2", "tool3"] + timeouts = [60, 45, 30] + verbose_list = [True, False, True] + + with patch.object( + aop_instance, "add_agent" + ) as mock_add_agent, patch.object( + aop_instance, "_register_agent_discovery_tool" + ): + mock_add_agent.return_value = "test_tool" + + result = aop_instance.add_agents_batch( + real_agents, + tool_names=tool_names, + timeouts=timeouts, + verbose_list=verbose_list, + ) + + assert len(result) == 3 + assert mock_add_agent.call_count == 3 + mock_add_agent.assert_any_call( + real_agents[0], + "tool1", + None, + None, + None, + 60, + 3, + True, + None, + ) + mock_add_agent.assert_any_call( + real_agents[1], + "tool2", + None, + None, + None, + 45, + 3, + False, + None, + ) + mock_add_agent.assert_any_call( + real_agents[2], + "tool3", + None, + None, + None, + 30, + 3, + True, + None, + ) + + +def test_add_agents_batch_empty_list( + real_agents, aop_instance, mock_fastmcp +): + """Test adding empty agents list raises ValueError.""" + with patch.object(aop_instance, "_register_agent_discovery_tool"): + with pytest.raises( + ValueError, match="Agents list cannot be empty" + ): + aop_instance.add_agents_batch([]) + + +def test_add_agents_batch_with_none_values( + real_agents, aop_instance, mock_fastmcp +): + """Test adding agents list with None values raises ValueError.""" + agents_with_none = real_agents.copy() + agents_with_none.append(None) + + with patch.object(aop_instance, "_register_agent_discovery_tool"): + with pytest.raises( + ValueError, match="Agents list cannot contain None values" + ): + aop_instance.add_agents_batch(agents_with_none) + + +def test_remove_agent_existing(aop_instance, mock_fastmcp): + """Test removing existing agent.""" + with patch.object( + aop_instance.task_queues["test_agent"], "stop_workers" + ): + result = aop_instance.remove_agent("test_agent") + + assert result is True + assert "test_agent" not in aop_instance.agents + assert "test_agent" not in aop_instance.tool_configs + assert "test_agent" not in aop_instance.task_queues + + +def test_remove_agent_nonexistent(aop_instance, mock_fastmcp): + """Test removing non-existent agent.""" + result = aop_instance.remove_agent("nonexistent_agent") + + assert result is False + + +def test_list_agents(aop_instance, mock_fastmcp): + """Test listing agents.""" + result = aop_instance.list_agents() + + assert isinstance(result, list) + assert "test_agent" in result + + +def test_get_agent_info_existing(aop_instance, mock_fastmcp): + """Test getting info for existing agent.""" + result = aop_instance.get_agent_info("test_agent") + + assert result is not None + assert result["tool_name"] == "test_agent" + assert result["agent_name"] == "test_agent" + assert result["agent_description"] == "Test agent description" + assert result["model_name"] == "gpt-3.5-turbo" + + +def test_get_agent_info_nonexistent(aop_instance, mock_fastmcp): + """Test getting info for non-existent agent.""" + result = aop_instance.get_agent_info("nonexistent_agent") + + assert result is None + + +def test_get_queue_stats_queue_enabled(aop_instance, mock_fastmcp): + """Test getting queue stats when queue is enabled.""" + # Mock the task queue stats + mock_stats = Mock() + mock_stats.total_tasks = 10 + mock_stats.completed_tasks = 8 + mock_stats.failed_tasks = 1 + mock_stats.pending_tasks = 1 + mock_stats.processing_tasks = 0 + mock_stats.average_processing_time = 2.5 + mock_stats.queue_size = 1 + + with patch.object( + aop_instance.task_queues["test_agent"], + "get_stats", + return_value=mock_stats, + ), patch.object( + aop_instance.task_queues["test_agent"], + "get_status", + return_value=QueueStatus.RUNNING, + ): + result = aop_instance.get_queue_stats("test_agent") + + assert result["success"] is True + assert result["agent_name"] == "test_agent" + assert result["stats"]["total_tasks"] == 10 + assert result["stats"]["completed_tasks"] == 8 + assert result["stats"]["failed_tasks"] == 1 + assert result["stats"]["pending_tasks"] == 1 + assert result["stats"]["average_processing_time"] == 2.5 + assert result["stats"]["queue_size"] == 1 + assert result["stats"]["queue_status"] == "running" + + +def test_get_queue_stats_queue_disabled(aop_instance, mock_fastmcp): + """Test getting queue stats when queue is disabled.""" + aop_instance.queue_enabled = False + + result = aop_instance.get_queue_stats("test_agent") + + assert result["success"] is False + assert result["error"] == "Queue system is not enabled" + assert result["stats"] == {} + + +def test_get_queue_stats_nonexistent_agent( + aop_instance, mock_fastmcp +): + """Test getting queue stats for non-existent agent.""" + result = aop_instance.get_queue_stats("nonexistent_agent") + + assert result["success"] is False + assert ( + result["error"] + == "Agent 'nonexistent_agent' not found or has no queue" + ) + + +def test_get_queue_stats_all_agents(aop_instance, mock_fastmcp): + """Test getting queue stats for all agents.""" + # Mock stats for both agents + mock_stats1 = Mock() + mock_stats1.total_tasks = 10 + mock_stats1.completed_tasks = 8 + mock_stats1.failed_tasks = 1 + mock_stats1.pending_tasks = 1 + mock_stats1.processing_tasks = 0 + mock_stats1.average_processing_time = 2.5 + mock_stats1.queue_size = 1 + + mock_stats2 = Mock() + mock_stats2.total_tasks = 5 + mock_stats2.completed_tasks = 5 + mock_stats2.failed_tasks = 0 + mock_stats2.pending_tasks = 0 + mock_stats2.processing_tasks = 0 + mock_stats2.average_processing_time = 1.8 + mock_stats2.queue_size = 0 + + with patch.object( + aop_instance.task_queues["test_agent"], + "get_stats", + return_value=mock_stats1, + ), patch.object( + aop_instance.task_queues["test_agent"], + "get_status", + return_value=QueueStatus.RUNNING, + ): + result = aop_instance.get_queue_stats() + + assert result["success"] is True + assert result["total_agents"] == 1 + assert "test_agent" in result["stats"] + assert result["stats"]["test_agent"]["total_tasks"] == 10 + + +def test_pause_agent_queue_success(aop_instance, mock_fastmcp): + """Test pausing agent queue successfully.""" + with patch.object( + aop_instance.task_queues["test_agent"], "pause_workers" + ) as mock_pause: + result = aop_instance.pause_agent_queue("test_agent") + + assert result is True + mock_pause.assert_called_once() + + +def test_pause_agent_queue_disabled(aop_instance, mock_fastmcp): + """Test pausing agent queue when queue system disabled.""" + aop_instance.queue_enabled = False + + result = aop_instance.pause_agent_queue("test_agent") + + assert result is False + + +def test_pause_agent_queue_nonexistent(aop_instance, mock_fastmcp): + """Test pausing non-existent agent queue.""" + result = aop_instance.pause_agent_queue("nonexistent_agent") + + assert result is False + + +def test_resume_agent_queue_success(aop_instance, mock_fastmcp): + """Test resuming agent queue successfully.""" + with patch.object( + aop_instance.task_queues["test_agent"], "resume_workers" + ) as mock_resume: + result = aop_instance.resume_agent_queue("test_agent") + + assert result is True + mock_resume.assert_called_once() + + +def test_clear_agent_queue_success(aop_instance, mock_fastmcp): + """Test clearing agent queue successfully.""" + with patch.object( + aop_instance.task_queues["test_agent"], + "clear_queue", + return_value=5, + ) as mock_clear: + result = aop_instance.clear_agent_queue("test_agent") + + assert result == 5 + mock_clear.assert_called_once() + + +def test_clear_agent_queue_disabled(aop_instance, mock_fastmcp): + """Test clearing agent queue when queue system disabled.""" + aop_instance.queue_enabled = False + + result = aop_instance.clear_agent_queue("test_agent") + + assert result == -1 + + +def test_get_task_status_success(aop_instance, mock_fastmcp): + """Test getting task status successfully.""" + mock_task = Mock() + mock_task.task_id = "test_task_id" + mock_task.status = TaskStatus.COMPLETED + mock_task.created_at = 1234567890.0 + mock_task.result = "Task completed" + mock_task.error = None + mock_task.retry_count = 0 + mock_task.max_retries = 3 + mock_task.priority = 0 + + with patch.object( + aop_instance.task_queues["test_agent"], + "get_task", + return_value=mock_task, + ): + result = aop_instance.get_task_status( + "test_agent", "test_task_id" + ) + + assert result["success"] is True + assert result["task"]["task_id"] == "test_task_id" + assert result["task"]["status"] == "completed" + assert result["task"]["result"] == "Task completed" + + +def test_get_task_status_queue_disabled(aop_instance, mock_fastmcp): + """Test getting task status when queue disabled.""" + aop_instance.queue_enabled = False + + result = aop_instance.get_task_status( + "test_agent", "test_task_id" + ) + + assert result["success"] is False + assert result["error"] == "Queue system is not enabled" + + +def test_get_task_status_task_not_found(aop_instance, mock_fastmcp): + """Test getting status for non-existent task.""" + with patch.object( + aop_instance.task_queues["test_agent"], + "get_task", + return_value=None, + ): + result = aop_instance.get_task_status( + "test_agent", "test_task_id" + ) + + assert result["success"] is False + assert result["error"] == "Task 'test_task_id' not found" + + +def test_cancel_task_success(aop_instance, mock_fastmcp): + """Test cancelling task successfully.""" + with patch.object( + aop_instance.task_queues["test_agent"], + "cancel_task", + return_value=True, + ): + result = aop_instance.cancel_task( + "test_agent", "test_task_id" + ) + + assert result is True + + +def test_cancel_task_queue_disabled(aop_instance, mock_fastmcp): + """Test cancelling task when queue disabled.""" + aop_instance.queue_enabled = False + + result = aop_instance.cancel_task("test_agent", "test_task_id") + + assert result is False + + +def test_pause_all_queues(aop_instance, mock_fastmcp): + """Test pausing all queues.""" + with patch.object( + aop_instance, "pause_agent_queue", return_value=True + ) as mock_pause: + result = aop_instance.pause_all_queues() + + assert len(result) == 1 + assert result["test_agent"] is True + mock_pause.assert_called_once_with("test_agent") + + +def test_pause_all_queues_disabled(aop_instance, mock_fastmcp): + """Test pausing all queues when disabled.""" + aop_instance.queue_enabled = False + + result = aop_instance.pause_all_queues() + + assert result == {} + + +def test_resume_all_queues(aop_instance, mock_fastmcp): + """Test resuming all queues.""" + with patch.object( + aop_instance, "resume_agent_queue", return_value=True + ) as mock_resume: + result = aop_instance.resume_all_queues() + + assert len(result) == 1 + assert result["test_agent"] is True + mock_resume.assert_called_once_with("test_agent") + + +def test_clear_all_queues(aop_instance, mock_fastmcp): + """Test clearing all queues.""" + with patch.object( + aop_instance, "clear_agent_queue", return_value=5 + ) as mock_clear: + result = aop_instance.clear_all_queues() + + assert len(result) == 1 + assert result["test_agent"] == 5 + mock_clear.assert_called_once_with("test_agent") + + +def test_enable_persistence(aop_instance, mock_fastmcp): + """Test enabling persistence mode.""" + aop_instance.enable_persistence() + + assert aop_instance._persistence_enabled is True + + +def test_disable_persistence(aop_instance, mock_fastmcp): + """Test disabling persistence mode.""" + aop_instance.disable_persistence() + + assert aop_instance._persistence_enabled is False + assert aop_instance._shutdown_requested is True + + +def test_request_shutdown(aop_instance, mock_fastmcp): + """Test requesting server shutdown.""" + aop_instance.request_shutdown() + + assert aop_instance._shutdown_requested is True + + +def test_get_persistence_status(aop_instance, mock_fastmcp): + """Test getting persistence status.""" + result = aop_instance.get_persistence_status() + + assert result["persistence_enabled"] is False + assert result["shutdown_requested"] is False + assert result["restart_count"] == 0 + assert result["max_restart_attempts"] == 10 + assert result["restart_delay"] == 5.0 + assert result["remaining_restarts"] == 10 + + +def test_reset_restart_count(aop_instance, mock_fastmcp): + """Test resetting restart counter.""" + aop_instance._restart_count = 5 + + aop_instance.reset_restart_count() + + assert aop_instance._restart_count == 0 + + +def test_get_network_status(aop_instance, mock_fastmcp): + """Test getting network status.""" + result = aop_instance.get_network_status() + + assert result["network_monitoring_enabled"] is True + assert result["network_connected"] is True + assert result["network_retry_count"] == 0 + assert result["max_network_retries"] == 5 + assert result["network_retry_delay"] == 10.0 + assert result["network_timeout"] == 30.0 + assert result["last_network_error"] is None + assert result["remaining_network_retries"] == 5 + assert result["host"] == "localhost" + assert result["port"] == 8000 + + +def test_reset_network_retry_count(aop_instance, mock_fastmcp): + """Test resetting network retry counter.""" + aop_instance._network_retry_count = 3 + aop_instance._last_network_error = "Test error" + aop_instance._network_connected = False + + aop_instance.reset_network_retry_count() + + assert aop_instance._network_retry_count == 0 + assert aop_instance._last_network_error is None + assert aop_instance._network_connected is True + + +def test_get_server_info(aop_instance, mock_fastmcp): + """Test getting comprehensive server information.""" + result = aop_instance.get_server_info() + + assert result["server_name"] == "Test AOP" + assert result["description"] == "Test AOP description" + assert result["total_tools"] == 1 + assert result["tools"] == ["test_agent"] + assert result["verbose"] is True + assert result["traceback_enabled"] is True + assert result["log_level"] == "INFO" + assert result["transport"] == "streamable-http" + assert result["queue_enabled"] is True + assert "persistence" in result + assert "network" in result + assert "tool_details" in result + assert "queue_config" in result + + +def test_is_network_error_connection_error( + aop_instance, mock_fastmcp +): + """Test detecting connection errors as network errors.""" + error = ConnectionError("Connection failed") + + result = aop_instance._is_network_error(error) + + assert result is True + + +def test_is_network_error_timeout_error(aop_instance, mock_fastmcp): + """Test detecting timeout errors as network errors.""" + error = TimeoutError("Request timed out") + + result = aop_instance._is_network_error(error) + + assert result is True + + +def test_is_network_error_socket_error(aop_instance, mock_fastmcp): + """Test detecting socket errors as network errors.""" + error = socket.gaierror("Name resolution failed") + + result = aop_instance._is_network_error(error) + + assert result is True + + +def test_is_network_error_non_network_error( + aop_instance, mock_fastmcp +): + """Test non-network errors are not detected as network errors.""" + error = ValueError("Invalid value") + + result = aop_instance._is_network_error(error) + + assert result is False + + +def test_is_network_error_by_message(aop_instance, mock_fastmcp): + """Test detecting network errors by error message content.""" + error = Exception("Connection refused by server") + + result = aop_instance._is_network_error(error) + + assert result is True + + +def test_get_network_error_message_connection_refused( + aop_instance, mock_fastmcp +): + """Test getting custom message for connection refused errors.""" + error = ConnectionRefusedError("Connection refused") + + result = aop_instance._get_network_error_message(error, 1) + + assert "NETWORK ERROR: Connection refused" in result + assert "attempt 1/5" in result + + +def test_get_network_error_message_timeout( + aop_instance, mock_fastmcp +): + """Test getting custom message for timeout errors.""" + error = TimeoutError("Request timed out") + + result = aop_instance._get_network_error_message(error, 2) + + assert "NETWORK ERROR: Connection timeout" in result + assert "attempt 2/5" in result + + +def test_test_network_connectivity_success( + aop_instance, mock_fastmcp +): + """Test network connectivity test success.""" + with patch( + "socket.gethostbyname", return_value="127.0.0.1" + ), patch("socket.socket") as mock_socket_class: + mock_socket = Mock() + mock_socket.connect_ex.return_value = 0 + mock_socket_class.return_value = mock_socket + + result = aop_instance._test_network_connectivity() + + assert result is True + mock_socket.connect_ex.assert_called_once_with( + ("localhost", 8000) + ) + + +def test_test_network_connectivity_failure( + aop_instance, mock_fastmcp +): + """Test network connectivity test failure.""" + with patch( + "socket.gethostbyname", + side_effect=socket.gaierror("Name resolution failed"), + ): + result = aop_instance._test_network_connectivity() + + assert result is False + + +def test_handle_network_error_first_retry(aop_instance, mock_fastmcp): + """Test handling network error on first retry.""" + error = ConnectionError("Connection failed") + + with patch.object( + aop_instance, "_test_network_connectivity", return_value=True + ) as mock_test: + result = aop_instance._handle_network_error(error) + + assert result is True + assert aop_instance._network_retry_count == 1 + assert aop_instance._network_connected is True + mock_test.assert_called_once() + + +def test_handle_network_error_max_retries(aop_instance, mock_fastmcp): + """Test handling network error at max retries.""" + error = ConnectionError("Connection failed") + aop_instance._network_retry_count = 5 # Max retries + + with patch.object( + aop_instance, "_test_network_connectivity", return_value=False + ): + result = aop_instance._handle_network_error(error) + + assert result is False + assert aop_instance._network_retry_count == 6 + + +def test_handle_network_error_disabled_monitoring( + aop_instance, mock_fastmcp +): + """Test handling network error when monitoring disabled.""" + aop_instance.network_monitoring = False + error = ConnectionError("Connection failed") + + result = aop_instance._handle_network_error(error) + + assert result is False + assert aop_instance._network_retry_count == 0 + + +def test_start_server_basic(aop_instance, mock_fastmcp): + """Test starting server with basic configuration.""" + with patch("swarms.structs.aop.logger") as mock_logger: + aop_instance.start_server() + + mock_logger.info.assert_any_call( + "Starting MCP server 'Test AOP' on localhost:8000\n" + "Transport: streamable-http\n" + "Log level: INFO\n" + "Verbose mode: True\n" + "Traceback enabled: True\n" + "Queue enabled: True\n" + "Available tools: ['test_agent']" + ) + + +def test_start_server_keyboard_interrupt(aop_instance, mock_fastmcp): + """Test starting server with keyboard interrupt.""" + mock_fastmcp.run.side_effect = KeyboardInterrupt() + + with patch("swarms.structs.aop.logger"): + aop_instance.start_server() + + # Should handle KeyboardInterrupt gracefully + assert True # If we get here, the test passed + + +def test_run_without_persistence(aop_instance, mock_fastmcp): + """Test running server without persistence.""" + with patch.object(aop_instance, "start_server") as mock_start: + aop_instance.run() + + mock_start.assert_called_once() + + +def test_run_with_persistence_success(aop_instance, mock_fastmcp): + """Test running server with persistence on successful execution.""" + aop_instance._persistence_enabled = True + + with patch.object(aop_instance, "start_server") as mock_start: + aop_instance.run() + + mock_start.assert_called_once() + assert aop_instance._restart_count == 0 + + +def test_run_with_persistence_keyboard_interrupt( + aop_instance, mock_fastmcp +): + """Test running server with persistence on keyboard interrupt.""" + aop_instance._persistence_enabled = True + aop_instance._shutdown_requested = False + + with patch.object( + aop_instance, "start_server", side_effect=KeyboardInterrupt() + ) as mock_start: + aop_instance.run() + + mock_start.assert_called_once() + assert aop_instance._restart_count == 1 + + +def test_run_with_persistence_network_error( + aop_instance, mock_fastmcp +): + """Test running server with persistence on network error.""" + aop_instance._persistence_enabled = True + aop_instance._shutdown_requested = False + + network_error = ConnectionError("Connection failed") + with patch.object( + aop_instance, "start_server", side_effect=network_error + ) as mock_start, patch.object( + aop_instance, "_is_network_error", return_value=True + ) as mock_is_network, patch.object( + aop_instance, "_handle_network_error", return_value=True + ) as mock_handle_network: + aop_instance.run() + + mock_start.assert_called_once() + mock_is_network.assert_called_once_with(network_error) + mock_handle_network.assert_called_once_with(network_error) + assert aop_instance._restart_count == 1 + + +def test_run_with_persistence_non_network_error( + aop_instance, mock_fastmcp +): + """Test running server with persistence on non-network error.""" + aop_instance._persistence_enabled = True + aop_instance._shutdown_requested = False + + error = ValueError("Invalid configuration") + with patch.object( + aop_instance, "start_server", side_effect=error + ) as mock_start, patch.object( + aop_instance, "_is_network_error", return_value=False + ) as mock_is_network: + aop_instance.run() + + mock_start.assert_called_once() + mock_is_network.assert_called_once_with(error) + assert aop_instance._restart_count == 1 + + +def test_run_with_persistence_max_restarts( + aop_instance, mock_fastmcp +): + """Test running server with persistence hitting max restarts.""" + aop_instance._persistence_enabled = True + aop_instance._shutdown_requested = False + aop_instance._restart_count = 10 # At max + aop_instance.max_restart_attempts = 10 + + error = ValueError("Invalid configuration") + with patch.object( + aop_instance, "start_server", side_effect=error + ), patch.object( + aop_instance, "_is_network_error", return_value=False + ): + aop_instance.run() + + assert aop_instance._restart_count == 11 + + +def test_aop_cluster_initialization(): + """Test AOPCluster initialization.""" + urls = ["http://localhost:8000", "http://localhost:8001"] + + cluster = AOPCluster(urls, transport="sse") + + assert cluster.urls == urls + assert cluster.transport == "sse" + + +def test_aop_cluster_get_tools(): + """Test AOPCluster getting tools from servers.""" + urls = ["http://localhost:8000"] + + with patch( + "swarms.structs.aop.get_tools_for_multiple_mcp_servers" + ) as mock_get_tools: + mock_get_tools.return_value = [{"test": "data"}] + + cluster = AOPCluster(urls) + result = cluster.get_tools(output_type="dict") + + assert result == [{"test": "data"}] + mock_get_tools.assert_called_once_with( + urls=urls, + format="openai", + output_type="dict", + transport="streamable-http", + ) + + +def test_aop_cluster_find_tool_by_server_name(): + """Test AOPCluster finding tool by server name.""" + urls = ["http://localhost:8000"] + + mock_tools = [ + {"function": {"name": "test_agent"}}, + {"function": {"name": "other_agent"}}, + ] + + with patch.object( + AOPCluster, "get_tools", return_value=mock_tools + ) as mock_get_tools: + cluster = AOPCluster(urls) + result = cluster.find_tool_by_server_name("test_agent") + + assert result == mock_tools[0] + mock_get_tools.assert_called_once() + + +def test_aop_cluster_find_tool_not_found(): + """Test AOPCluster finding non-existent tool.""" + urls = ["http://localhost:8000"] + + mock_tools = [{"function": {"name": "other_agent"}}] + + with patch.object( + AOPCluster, "get_tools", return_value=mock_tools + ): + cluster = AOPCluster(urls) + result = cluster.find_tool_by_server_name("nonexistent_agent") + + assert result is None + + +def test_aop_initialization_with_queue_disabled( + real_agent, mock_fastmcp +): + """Test AOP initialization with queue system disabled.""" + with patch( + "swarms.structs.aop.FastMCP", return_value=mock_fastmcp + ), patch("swarms.structs.aop.logger"): + aop = AOP(queue_enabled=False) + + assert aop.queue_enabled is False + assert len(aop.task_queues) == 0 + + +def test_aop_initialization_edge_cases(mock_fastmcp): + """Test AOP initialization with edge case values.""" + with patch( + "swarms.structs.aop.FastMCP", return_value=mock_fastmcp + ), patch("swarms.structs.aop.logger"): + aop = AOP( + server_name="", + port=0, + max_workers_per_agent=0, + max_queue_size_per_agent=0, + max_restart_attempts=0, + max_network_retries=0, + ) + + assert aop.server_name == "" + assert aop.port == 0 + assert aop.max_workers_per_agent == 0 + assert aop.max_queue_size_per_agent == 0 + assert aop.max_restart_attempts == 0 + assert aop.max_network_retries == 0 + + +def test_add_agent_queue_disabled( + real_agent, aop_instance, mock_fastmcp +): + """Test adding agent when queue is disabled.""" + aop_instance.queue_enabled = False + + from swarms import Agent + + new_agent = Agent( + agent_name="no_queue_agent", + agent_description="No queue agent", + model_name="gpt-3.5-turbo", + max_loops=1, + temperature=0.5, + max_tokens=4096, + ) + + with patch.object(aop_instance, "_register_tool"), patch.object( + aop_instance, "_register_agent_discovery_tool" + ): + result = aop_instance.add_agent(new_agent) + + assert result == "no_queue_agent" + assert "no_queue_agent" not in aop_instance.task_queues + + +def test_get_queue_stats_all_agents_comprehensive( + aop_instance, mock_fastmcp +): + """Test getting comprehensive queue stats for all agents.""" + # Add another agent for testing + new_agent = Mock(spec=Agent) + new_agent.agent_name = "second_agent" + new_agent.run = Mock(return_value="Second response") + + with patch.object(aop_instance, "_register_tool"), patch.object( + aop_instance, "_register_agent_discovery_tool" + ): + aop_instance.add_agent(new_agent, tool_name="second_agent") + + # Mock stats for both agents + mock_stats1 = Mock() + mock_stats1.total_tasks = 10 + mock_stats1.completed_tasks = 8 + mock_stats1.failed_tasks = 1 + mock_stats1.pending_tasks = 1 + mock_stats1.processing_tasks = 0 + mock_stats1.average_processing_time = 2.5 + mock_stats1.queue_size = 1 + + mock_stats2 = Mock() + mock_stats2.total_tasks = 5 + mock_stats2.completed_tasks = 5 + mock_stats2.failed_tasks = 0 + mock_stats2.pending_tasks = 0 + mock_stats2.processing_tasks = 0 + mock_stats2.average_processing_time = 1.8 + mock_stats2.queue_size = 0 + + def mock_get_stats(tool_name): + if tool_name == "test_agent": + return mock_stats1 + elif tool_name == "second_agent": + return mock_stats2 + return Mock() + + def mock_get_status(tool_name): + return QueueStatus.RUNNING + + with patch.object( + aop_instance.task_queues["test_agent"], + "get_stats", + mock_get_stats, + ), patch.object( + aop_instance.task_queues["second_agent"], + "get_stats", + mock_get_stats, + ), patch.object( + aop_instance.task_queues["test_agent"], + "get_status", + mock_get_status, + ), patch.object( + aop_instance.task_queues["second_agent"], + "get_status", + mock_get_status, + ): + result = aop_instance.get_queue_stats() + + assert result["success"] is True + assert result["total_agents"] == 2 + assert "test_agent" in result["stats"] + assert "second_agent" in result["stats"] + assert result["stats"]["test_agent"]["total_tasks"] == 10 + assert result["stats"]["second_agent"]["total_tasks"] == 5 diff --git a/tests/structs/test_auto_swarm_builder_fix.py b/tests/structs/test_auto_swarm_builder_fix.py deleted file mode 100644 index 420c1892..00000000 --- a/tests/structs/test_auto_swarm_builder_fix.py +++ /dev/null @@ -1,293 +0,0 @@ -""" -Tests for bug #1115 fix in AutoSwarmBuilder. - -This test module verifies the fix for AttributeError when creating agents -from AgentSpec Pydantic models in AutoSwarmBuilder. - -Bug: https://github.com/kyegomez/swarms/issues/1115 -""" - -import pytest - -from swarms.structs.agent import Agent -from swarms.structs.auto_swarm_builder import ( - AgentSpec, - AutoSwarmBuilder, -) -from swarms.structs.ma_utils import set_random_models_for_agents - - -class TestAutoSwarmBuilderFix: - """Tests for bug #1115 fix in AutoSwarmBuilder.""" - - def test_create_agents_from_specs_with_dict(self): - """Test that create_agents_from_specs handles dict input correctly.""" - builder = AutoSwarmBuilder() - - # Create specs as a dictionary - specs = { - "agents": [ - { - "agent_name": "test_agent_1", - "description": "Test agent 1 description", - "system_prompt": "You are a helpful assistant", - "model_name": "gpt-4o-mini", - "max_loops": 1, - } - ] - } - - agents = builder.create_agents_from_specs(specs) - - # Verify agents were created correctly - assert len(agents) == 1 - assert isinstance(agents[0], Agent) - assert agents[0].agent_name == "test_agent_1" - - # Verify description was mapped to agent_description - assert hasattr(agents[0], "agent_description") - assert ( - agents[0].agent_description == "Test agent 1 description" - ) - - def test_create_agents_from_specs_with_pydantic(self): - """Test that create_agents_from_specs handles Pydantic model input correctly. - - This is the main test for bug #1115 - it verifies that AgentSpec - Pydantic models can be unpacked correctly. - """ - builder = AutoSwarmBuilder() - - # Create specs as Pydantic AgentSpec objects - agent_spec = AgentSpec( - agent_name="test_agent_pydantic", - description="Pydantic test agent", - system_prompt="You are a helpful assistant", - model_name="gpt-4o-mini", - max_loops=1, - ) - - specs = {"agents": [agent_spec]} - - agents = builder.create_agents_from_specs(specs) - - # Verify agents were created correctly - assert len(agents) == 1 - assert isinstance(agents[0], Agent) - assert agents[0].agent_name == "test_agent_pydantic" - - # Verify description was mapped to agent_description - assert hasattr(agents[0], "agent_description") - assert agents[0].agent_description == "Pydantic test agent" - - def test_parameter_name_mapping(self): - """Test that 'description' field maps to 'agent_description' correctly.""" - builder = AutoSwarmBuilder() - - # Test with dict that has 'description' - specs = { - "agents": [ - { - "agent_name": "mapping_test", - "description": "This should map to agent_description", - "system_prompt": "You are helpful", - } - ] - } - - agents = builder.create_agents_from_specs(specs) - - assert len(agents) == 1 - agent = agents[0] - - # Verify description was mapped - assert hasattr(agent, "agent_description") - assert ( - agent.agent_description - == "This should map to agent_description" - ) - - def test_create_agents_from_specs_mixed_input(self): - """Test that create_agents_from_specs handles mixed dict and Pydantic input.""" - builder = AutoSwarmBuilder() - - # Mix of dict and Pydantic objects - dict_spec = { - "agent_name": "dict_agent", - "description": "Dict agent description", - "system_prompt": "You are helpful", - } - - pydantic_spec = AgentSpec( - agent_name="pydantic_agent", - description="Pydantic agent description", - system_prompt="You are smart", - ) - - specs = {"agents": [dict_spec, pydantic_spec]} - - agents = builder.create_agents_from_specs(specs) - - # Verify both agents were created - assert len(agents) == 2 - assert all(isinstance(agent, Agent) for agent in agents) - - # Verify both have correct descriptions - dict_agent = next( - a for a in agents if a.agent_name == "dict_agent" - ) - pydantic_agent = next( - a for a in agents if a.agent_name == "pydantic_agent" - ) - - assert ( - dict_agent.agent_description == "Dict agent description" - ) - assert ( - pydantic_agent.agent_description - == "Pydantic agent description" - ) - - def test_set_random_models_for_agents_with_valid_agents( - self, - ): - """Test set_random_models_for_agents with proper Agent objects.""" - # Create proper Agent objects - agents = [ - Agent( - agent_name="agent1", - system_prompt="You are agent 1", - max_loops=1, - ), - Agent( - agent_name="agent2", - system_prompt="You are agent 2", - max_loops=1, - ), - ] - - # Set random models - model_names = ["gpt-4o-mini", "gpt-4o", "claude-3-5-sonnet"] - result = set_random_models_for_agents( - agents=agents, model_names=model_names - ) - - # Verify results - assert len(result) == 2 - assert all(isinstance(agent, Agent) for agent in result) - assert all(hasattr(agent, "model_name") for agent in result) - assert all( - agent.model_name in model_names for agent in result - ) - - def test_set_random_models_for_agents_with_single_agent( - self, - ): - """Test set_random_models_for_agents with a single agent.""" - agent = Agent( - agent_name="single_agent", - system_prompt="You are helpful", - max_loops=1, - ) - - model_names = ["gpt-4o-mini", "gpt-4o"] - result = set_random_models_for_agents( - agents=agent, model_names=model_names - ) - - assert isinstance(result, Agent) - assert hasattr(result, "model_name") - assert result.model_name in model_names - - def test_set_random_models_for_agents_with_none(self): - """Test set_random_models_for_agents with None returns random model name.""" - model_names = ["gpt-4o-mini", "gpt-4o", "claude-3-5-sonnet"] - result = set_random_models_for_agents( - agents=None, model_names=model_names - ) - - assert isinstance(result, str) - assert result in model_names - - @pytest.mark.skip( - reason="This test requires API key and makes LLM calls" - ) - def test_auto_swarm_builder_return_agents_objects_integration( - self, - ): - """Integration test for AutoSwarmBuilder with execution_type='return-agents-objects'. - - This test requires OPENAI_API_KEY and makes actual LLM calls. - Run manually with: pytest -k test_auto_swarm_builder_return_agents_objects_integration -v - """ - builder = AutoSwarmBuilder( - execution_type="return-agents-objects", - model_name="gpt-4o-mini", - max_loops=1, - verbose=False, - ) - - agents = builder.run( - "Create a team of 2 data analysis agents with specific roles" - ) - - # Verify agents were created - assert isinstance(agents, list) - assert len(agents) >= 1 - assert all(isinstance(agent, Agent) for agent in agents) - assert all(hasattr(agent, "agent_name") for agent in agents) - assert all( - hasattr(agent, "agent_description") for agent in agents - ) - - def test_agent_spec_to_agent_all_fields(self): - """Test that all AgentSpec fields are properly passed to Agent.""" - builder = AutoSwarmBuilder() - - agent_spec = AgentSpec( - agent_name="full_test_agent", - description="Full test description", - system_prompt="You are a comprehensive test agent", - model_name="gpt-4o-mini", - auto_generate_prompt=False, - max_tokens=4096, - temperature=0.7, - role="worker", - max_loops=3, - goal="Test all parameters", - ) - - agents = builder.create_agents_from_specs( - {"agents": [agent_spec]} - ) - - assert len(agents) == 1 - agent = agents[0] - - # Verify all fields were set - assert agent.agent_name == "full_test_agent" - assert agent.agent_description == "Full test description" - # Agent may modify system_prompt by adding additional instructions - assert ( - "You are a comprehensive test agent" - in agent.system_prompt - ) - assert agent.max_loops == 3 - assert agent.max_tokens == 4096 - assert agent.temperature == 0.7 - - def test_create_agents_from_specs_empty_list(self): - """Test that create_agents_from_specs handles empty agent list.""" - builder = AutoSwarmBuilder() - - specs = {"agents": []} - - agents = builder.create_agents_from_specs(specs) - - assert isinstance(agents, list) - assert len(agents) == 0 - - -if __name__ == "__main__": - # Run tests with pytest - pytest.main([__file__, "-v", "--tb=short"]) diff --git a/tests/structs/test_auto_swarms_builder.py b/tests/structs/test_auto_swarms_builder.py index 4d690678..a1e9085a 100644 --- a/tests/structs/test_auto_swarms_builder.py +++ b/tests/structs/test_auto_swarms_builder.py @@ -1,10 +1,18 @@ -from swarms.structs.auto_swarm_builder import AutoSwarmBuilder +import pytest from dotenv import load_dotenv +from swarms.structs.agent import Agent +from swarms.structs.auto_swarm_builder import ( + AgentSpec, + AutoSwarmBuilder, +) +from swarms.structs.ma_utils import set_random_models_for_agents + load_dotenv() def print_separator(): + """Print a separator line for test output formatting.""" print("\n" + "=" * 50) @@ -194,5 +202,273 @@ def run_all_tests(): raise +# Bug Fix Tests (from test_auto_swarm_builder_fix.py) +class TestAutoSwarmBuilderFix: + """Tests for bug #1115 fix in AutoSwarmBuilder.""" + + def test_create_agents_from_specs_with_dict(self): + """Test that create_agents_from_specs handles dict input correctly.""" + builder = AutoSwarmBuilder() + + # Create specs as a dictionary + specs = { + "agents": [ + { + "agent_name": "test_agent_1", + "description": "Test agent 1 description", + "system_prompt": "You are a helpful assistant", + "model_name": "gpt-4o-mini", + "max_loops": 1, + } + ] + } + + agents = builder.create_agents_from_specs(specs) + + # Verify agents were created correctly + assert len(agents) == 1 + assert isinstance(agents[0], Agent) + assert agents[0].agent_name == "test_agent_1" + + # Verify description was mapped to agent_description + assert hasattr(agents[0], "agent_description") + assert ( + agents[0].agent_description == "Test agent 1 description" + ) + + def test_create_agents_from_specs_with_pydantic(self): + """Test that create_agents_from_specs handles Pydantic model input correctly. + + This is the main test for bug #1115 - it verifies that AgentSpec + Pydantic models can be unpacked correctly. + """ + builder = AutoSwarmBuilder() + + # Create specs as Pydantic AgentSpec objects + agent_spec = AgentSpec( + agent_name="test_agent_pydantic", + description="Pydantic test agent", + system_prompt="You are a helpful assistant", + model_name="gpt-4o-mini", + max_loops=1, + ) + + specs = {"agents": [agent_spec]} + + agents = builder.create_agents_from_specs(specs) + + # Verify agents were created correctly + assert len(agents) == 1 + assert isinstance(agents[0], Agent) + assert agents[0].agent_name == "test_agent_pydantic" + + # Verify description was mapped to agent_description + assert hasattr(agents[0], "agent_description") + assert agents[0].agent_description == "Pydantic test agent" + + def test_parameter_name_mapping(self): + """Test that 'description' field maps to 'agent_description' correctly.""" + builder = AutoSwarmBuilder() + + # Test with dict that has 'description' + specs = { + "agents": [ + { + "agent_name": "mapping_test", + "description": "This should map to agent_description", + "system_prompt": "You are helpful", + } + ] + } + + agents = builder.create_agents_from_specs(specs) + + assert len(agents) == 1 + agent = agents[0] + + # Verify description was mapped + assert hasattr(agent, "agent_description") + assert ( + agent.agent_description + == "This should map to agent_description" + ) + + def test_create_agents_from_specs_mixed_input(self): + """Test that create_agents_from_specs handles mixed dict and Pydantic input.""" + builder = AutoSwarmBuilder() + + # Mix of dict and Pydantic objects + dict_spec = { + "agent_name": "dict_agent", + "description": "Dict agent description", + "system_prompt": "You are helpful", + } + + pydantic_spec = AgentSpec( + agent_name="pydantic_agent", + description="Pydantic agent description", + system_prompt="You are smart", + ) + + specs = {"agents": [dict_spec, pydantic_spec]} + + agents = builder.create_agents_from_specs(specs) + + # Verify both agents were created + assert len(agents) == 2 + assert all(isinstance(agent, Agent) for agent in agents) + + # Verify both have correct descriptions + dict_agent = next( + a for a in agents if a.agent_name == "dict_agent" + ) + pydantic_agent = next( + a for a in agents if a.agent_name == "pydantic_agent" + ) + + assert ( + dict_agent.agent_description == "Dict agent description" + ) + assert ( + pydantic_agent.agent_description + == "Pydantic agent description" + ) + + def test_set_random_models_for_agents_with_valid_agents(self): + """Test set_random_models_for_agents with proper Agent objects.""" + # Create proper Agent objects + agents = [ + Agent( + agent_name="agent1", + system_prompt="You are agent 1", + max_loops=1, + ), + Agent( + agent_name="agent2", + system_prompt="You are agent 2", + max_loops=1, + ), + ] + + # Set random models + model_names = ["gpt-4o-mini", "gpt-4o", "claude-3-5-sonnet"] + result = set_random_models_for_agents( + agents=agents, model_names=model_names + ) + + # Verify results + assert len(result) == 2 + assert all(isinstance(agent, Agent) for agent in result) + assert all(hasattr(agent, "model_name") for agent in result) + assert all( + agent.model_name in model_names for agent in result + ) + + def test_set_random_models_for_agents_with_single_agent(self): + """Test set_random_models_for_agents with a single agent.""" + agent = Agent( + agent_name="single_agent", + system_prompt="You are helpful", + max_loops=1, + ) + + model_names = ["gpt-4o-mini", "gpt-4o"] + result = set_random_models_for_agents( + agents=agent, model_names=model_names + ) + + assert isinstance(result, Agent) + assert hasattr(result, "model_name") + assert result.model_name in model_names + + def test_set_random_models_for_agents_with_none(self): + """Test set_random_models_for_agents with None returns random model name.""" + model_names = ["gpt-4o-mini", "gpt-4o", "claude-3-5-sonnet"] + result = set_random_models_for_agents( + agents=None, model_names=model_names + ) + + assert isinstance(result, str) + assert result in model_names + + @pytest.mark.skip( + reason="This test requires API key and makes LLM calls" + ) + def test_auto_swarm_builder_return_agents_objects_integration( + self, + ): + """Integration test for AutoSwarmBuilder with execution_type='return-agents-objects'. + + This test requires OPENAI_API_KEY and makes actual LLM calls. + Run manually with: pytest -k test_auto_swarm_builder_return_agents_objects_integration -v + """ + builder = AutoSwarmBuilder( + execution_type="return-agents-objects", + model_name="gpt-4o-mini", + max_loops=1, + verbose=False, + ) + + agents = builder.run( + "Create a team of 2 data analysis agents with specific roles" + ) + + # Verify agents were created + assert isinstance(agents, list) + assert len(agents) >= 1 + assert all(isinstance(agent, Agent) for agent in agents) + assert all(hasattr(agent, "agent_name") for agent in agents) + assert all( + hasattr(agent, "agent_description") for agent in agents + ) + + def test_agent_spec_to_agent_all_fields(self): + """Test that all AgentSpec fields are properly passed to Agent.""" + builder = AutoSwarmBuilder() + + agent_spec = AgentSpec( + agent_name="full_test_agent", + description="Full test description", + system_prompt="You are a comprehensive test agent", + model_name="gpt-4o-mini", + auto_generate_prompt=False, + max_tokens=4096, + temperature=0.7, + role="worker", + max_loops=3, + goal="Test all parameters", + ) + + agents = builder.create_agents_from_specs( + {"agents": [agent_spec]} + ) + + assert len(agents) == 1 + agent = agents[0] + + # Verify all fields were set + assert agent.agent_name == "full_test_agent" + assert agent.agent_description == "Full test description" + # Agent may modify system_prompt by adding additional instructions + assert ( + "You are a comprehensive test agent" + in agent.system_prompt + ) + assert agent.max_loops == 3 + assert agent.max_tokens == 4096 + assert agent.temperature == 0.7 + + def test_create_agents_from_specs_empty_list(self): + """Test that create_agents_from_specs handles empty agent list.""" + builder = AutoSwarmBuilder() + + specs = {"agents": []} + + agents = builder.create_agents_from_specs(specs) + + assert isinstance(agents, list) + assert len(agents) == 0 + + if __name__ == "__main__": run_all_tests() diff --git a/tests/structs/test_base.py b/tests/structs/test_base.py deleted file mode 100644 index dc5c7835..00000000 --- a/tests/structs/test_base.py +++ /dev/null @@ -1,287 +0,0 @@ -import os -from datetime import datetime - -import pytest - -from swarms.structs.base_structure import BaseStructure - - -class TestBaseStructure: - def test_init(self): - base_structure = BaseStructure( - name="TestStructure", - description="Test description", - save_metadata=True, - save_artifact_path="./test_artifacts", - save_metadata_path="./test_metadata", - save_error_path="./test_errors", - ) - - assert base_structure.name == "TestStructure" - assert base_structure.description == "Test description" - assert base_structure.save_metadata is True - assert base_structure.save_artifact_path == "./test_artifacts" - assert base_structure.save_metadata_path == "./test_metadata" - assert base_structure.save_error_path == "./test_errors" - - def test_save_to_file_and_load_from_file(self, tmpdir): - tmp_dir = tmpdir.mkdir("test_dir") - file_path = os.path.join(tmp_dir, "test_file.json") - - data_to_save = {"key": "value"} - base_structure = BaseStructure() - - base_structure.save_to_file(data_to_save, file_path) - loaded_data = base_structure.load_from_file(file_path) - - assert loaded_data == data_to_save - - def test_save_metadata_and_load_metadata(self, tmpdir): - tmp_dir = tmpdir.mkdir("test_dir") - base_structure = BaseStructure(save_metadata_path=tmp_dir) - - metadata = {"name": "Test", "description": "Test metadata"} - base_structure.save_metadata(metadata) - loaded_metadata = base_structure.load_metadata() - - assert loaded_metadata == metadata - - def test_log_error(self, tmpdir): - tmp_dir = tmpdir.mkdir("test_dir") - base_structure = BaseStructure(save_error_path=tmp_dir) - - error_message = "Test error message" - base_structure.log_error(error_message) - - log_file = os.path.join(tmp_dir, "TestStructure_errors.log") - with open(log_file) as file: - lines = file.readlines() - assert len(lines) == 1 - assert lines[0] == f"{error_message}\n" - - def test_save_artifact_and_load_artifact(self, tmpdir): - tmp_dir = tmpdir.mkdir("test_dir") - base_structure = BaseStructure(save_artifact_path=tmp_dir) - - artifact = {"key": "value"} - artifact_name = "test_artifact" - base_structure.save_artifact(artifact, artifact_name) - loaded_artifact = base_structure.load_artifact(artifact_name) - - assert loaded_artifact == artifact - - def test_current_timestamp(self): - base_structure = BaseStructure() - current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S") - timestamp = base_structure._current_timestamp() - assert timestamp == current_time - - def test_log_event(self, tmpdir): - tmp_dir = tmpdir.mkdir("test_dir") - base_structure = BaseStructure(save_metadata_path=tmp_dir) - - event = "Test event" - event_type = "INFO" - base_structure.log_event(event, event_type) - - log_file = os.path.join(tmp_dir, "TestStructure_events.log") - with open(log_file) as file: - lines = file.readlines() - assert len(lines) == 1 - assert ( - lines[0] == f"[{base_structure._current_timestamp()}]" - f" [{event_type}] {event}\n" - ) - - @pytest.mark.asyncio - async def test_run_async(self): - base_structure = BaseStructure() - - async def async_function(): - return "Async Test Result" - - result = await base_structure.run_async(async_function) - assert result == "Async Test Result" - - @pytest.mark.asyncio - async def test_save_metadata_async(self, tmpdir): - tmp_dir = tmpdir.mkdir("test_dir") - base_structure = BaseStructure(save_metadata_path=tmp_dir) - - metadata = {"name": "Test", "description": "Test metadata"} - await base_structure.save_metadata_async(metadata) - loaded_metadata = base_structure.load_metadata() - - assert loaded_metadata == metadata - - @pytest.mark.asyncio - async def test_log_error_async(self, tmpdir): - tmp_dir = tmpdir.mkdir("test_dir") - base_structure = BaseStructure(save_error_path=tmp_dir) - - error_message = "Test error message" - await base_structure.log_error_async(error_message) - - log_file = os.path.join(tmp_dir, "TestStructure_errors.log") - with open(log_file) as file: - lines = file.readlines() - assert len(lines) == 1 - assert lines[0] == f"{error_message}\n" - - @pytest.mark.asyncio - async def test_save_artifact_async(self, tmpdir): - tmp_dir = tmpdir.mkdir("test_dir") - base_structure = BaseStructure(save_artifact_path=tmp_dir) - - artifact = {"key": "value"} - artifact_name = "test_artifact" - await base_structure.save_artifact_async( - artifact, artifact_name - ) - loaded_artifact = base_structure.load_artifact(artifact_name) - - assert loaded_artifact == artifact - - @pytest.mark.asyncio - async def test_load_artifact_async(self, tmpdir): - tmp_dir = tmpdir.mkdir("test_dir") - base_structure = BaseStructure(save_artifact_path=tmp_dir) - - artifact = {"key": "value"} - artifact_name = "test_artifact" - base_structure.save_artifact(artifact, artifact_name) - loaded_artifact = await base_structure.load_artifact_async( - artifact_name - ) - - assert loaded_artifact == artifact - - @pytest.mark.asyncio - async def test_log_event_async(self, tmpdir): - tmp_dir = tmpdir.mkdir("test_dir") - base_structure = BaseStructure(save_metadata_path=tmp_dir) - - event = "Test event" - event_type = "INFO" - await base_structure.log_event_async(event, event_type) - - log_file = os.path.join(tmp_dir, "TestStructure_events.log") - with open(log_file) as file: - lines = file.readlines() - assert len(lines) == 1 - assert ( - lines[0] == f"[{base_structure._current_timestamp()}]" - f" [{event_type}] {event}\n" - ) - - @pytest.mark.asyncio - async def test_asave_to_file(self, tmpdir): - tmp_dir = tmpdir.mkdir("test_dir") - file_path = os.path.join(tmp_dir, "test_file.json") - data_to_save = {"key": "value"} - base_structure = BaseStructure() - - await base_structure.asave_to_file(data_to_save, file_path) - loaded_data = base_structure.load_from_file(file_path) - - assert loaded_data == data_to_save - - @pytest.mark.asyncio - async def test_aload_from_file(self, tmpdir): - tmp_dir = tmpdir.mkdir("test_dir") - file_path = os.path.join(tmp_dir, "test_file.json") - data_to_save = {"key": "value"} - base_structure = BaseStructure() - base_structure.save_to_file(data_to_save, file_path) - - loaded_data = await base_structure.aload_from_file(file_path) - assert loaded_data == data_to_save - - def test_run_in_thread(self): - base_structure = BaseStructure() - result = base_structure.run_in_thread( - lambda: "Thread Test Result" - ) - assert result.result() == "Thread Test Result" - - def test_save_and_decompress_data(self): - base_structure = BaseStructure() - data = {"key": "value"} - compressed_data = base_structure.compress_data(data) - decompressed_data = base_structure.decompres_data( - compressed_data - ) - assert decompressed_data == data - - def test_run_batched(self): - base_structure = BaseStructure() - - def run_function(data): - return f"Processed {data}" - - batched_data = list(range(10)) - result = base_structure.run_batched( - batched_data, batch_size=5, func=run_function - ) - - expected_result = [ - f"Processed {data}" for data in batched_data - ] - assert result == expected_result - - def test_load_config(self, tmpdir): - tmp_dir = tmpdir.mkdir("test_dir") - config_file = os.path.join(tmp_dir, "config.json") - config_data = {"key": "value"} - base_structure = BaseStructure() - - base_structure.save_to_file(config_data, config_file) - loaded_config = base_structure.load_config(config_file) - - assert loaded_config == config_data - - def test_backup_data(self, tmpdir): - tmp_dir = tmpdir.mkdir("test_dir") - base_structure = BaseStructure() - data_to_backup = {"key": "value"} - base_structure.backup_data( - data_to_backup, backup_path=tmp_dir - ) - backup_files = os.listdir(tmp_dir) - - assert len(backup_files) == 1 - loaded_data = base_structure.load_from_file( - os.path.join(tmp_dir, backup_files[0]) - ) - assert loaded_data == data_to_backup - - def test_monitor_resources(self): - base_structure = BaseStructure() - base_structure.monitor_resources() - - def test_run_with_resources(self): - base_structure = BaseStructure() - - def run_function(): - base_structure.monitor_resources() - return "Resource Test Result" - - result = base_structure.run_with_resources(run_function) - assert result == "Resource Test Result" - - def test_run_with_resources_batched(self): - base_structure = BaseStructure() - - def run_function(data): - base_structure.monitor_resources() - return f"Processed {data}" - - batched_data = list(range(10)) - result = base_structure.run_with_resources_batched( - batched_data, batch_size=5, func=run_function - ) - - expected_result = [ - f"Processed {data}" for data in batched_data - ] - assert result == expected_result diff --git a/tests/structs/test_base_workflow.py b/tests/structs/test_base_workflow.py deleted file mode 100644 index fbb8d710..00000000 --- a/tests/structs/test_base_workflow.py +++ /dev/null @@ -1,67 +0,0 @@ -import json -import os - -import pytest -from dotenv import load_dotenv - -from swarm_models import OpenAIChat -from swarms.structs import BaseWorkflow - -load_dotenv() - -api_key = os.environ.get("OPENAI_API_KEY") - - -def setup_workflow(): - llm = OpenAIChat(openai_api_key=api_key) - workflow = BaseWorkflow(max_loops=1) - workflow.add("What's the weather in miami", llm) - workflow.add("Create a report on these metrics", llm) - workflow.save_workflow_state("workflow_state.json") - return workflow - - -def teardown_workflow(): - os.remove("workflow_state.json") - - -def test_load_workflow_state(): - workflow = setup_workflow() - workflow.load_workflow_state("workflow_state.json") - assert workflow.max_loops == 1 - assert len(workflow.tasks) == 2 - assert ( - workflow.tasks[0].description == "What's the weather in miami" - ) - assert ( - workflow.tasks[1].description - == "Create a report on these metrics" - ) - teardown_workflow() - - -def test_load_workflow_state_with_missing_file(): - workflow = setup_workflow() - with pytest.raises(FileNotFoundError): - workflow.load_workflow_state("non_existent_file.json") - teardown_workflow() - - -def test_load_workflow_state_with_invalid_file(): - workflow = setup_workflow() - with open("invalid_file.json", "w") as f: - f.write("This is not valid JSON") - with pytest.raises(json.JSONDecodeError): - workflow.load_workflow_state("invalid_file.json") - os.remove("invalid_file.json") - teardown_workflow() - - -def test_load_workflow_state_with_missing_keys(): - workflow = setup_workflow() - with open("missing_keys.json", "w") as f: - json.dump({"max_loops": 1}, f) - with pytest.raises(KeyError): - workflow.load_workflow_state("missing_keys.json") - os.remove("missing_keys.json") - teardown_workflow() diff --git a/tests/structs/test_board_of_directors_swarm.py b/tests/structs/test_board_of_directors_swarm.py index cd85b81e..7ad2ecdb 100644 --- a/tests/structs/test_board_of_directors_swarm.py +++ b/tests/structs/test_board_of_directors_swarm.py @@ -1,1202 +1,230 @@ -""" -Comprehensive test suite for Board of Directors Swarm. - -This module contains extensive tests for the Board of Directors swarm implementation, -covering all aspects including initialization, board operations, task execution, -error handling, and performance characteristics. - -The test suite follows the Swarms testing philosophy: -- Comprehensive coverage of all functionality -- Proper mocking and isolation -- Performance and integration testing -- Error handling validation -""" - -import os -import pytest -import asyncio -from unittest.mock import Mock, patch, AsyncMock - -from swarms.structs.board_of_directors_swarm import ( - BoardOfDirectorsSwarm, - BoardMember, - BoardMemberRole, - BoardDecisionType, - BoardOrder, - BoardDecision, - BoardSpec, -) -from swarms.structs.agent import Agent - - -# Test fixtures -@pytest.fixture -def mock_agent(): - """Create a mock agent for testing.""" - agent = Mock(spec=Agent) - agent.agent_name = "TestAgent" - agent.agent_description = "A test agent for unit testing" - agent.run = Mock(return_value="Test agent response") - agent.arun = AsyncMock(return_value="Async test agent response") - return agent - - -@pytest.fixture -def mock_board_member(mock_agent): - """Create a mock board member for testing.""" - return BoardMember( - agent=mock_agent, - role=BoardMemberRole.CHAIRMAN, - voting_weight=1.5, - expertise_areas=["leadership", "strategy"], - ) - - -@pytest.fixture -def sample_agents(): - """Create sample agents for testing.""" - agents = [] - for i in range(3): - agent = Mock(spec=Agent) - agent.agent_name = f"Agent{i+1}" - agent.agent_description = f"Test agent {i+1}" - agent.run = Mock(return_value=f"Response from Agent{i+1}") - agents.append(agent) - return agents - - -@pytest.fixture -def sample_board_members(sample_agents): - """Create sample board members for testing.""" - roles = [ - BoardMemberRole.CHAIRMAN, - BoardMemberRole.VICE_CHAIRMAN, - BoardMemberRole.SECRETARY, - ] - board_members = [] - - for i, (agent, role) in enumerate(zip(sample_agents, roles)): - board_member = BoardMember( - agent=agent, - role=role, - voting_weight=1.0 + (i * 0.2), - expertise_areas=[f"expertise_{i+1}"], - ) - board_members.append(board_member) - - return board_members - - -@pytest.fixture -def basic_board_swarm(sample_agents): - """Create a basic Board of Directors swarm for testing.""" - return BoardOfDirectorsSwarm( - name="TestBoard", - agents=sample_agents, - verbose=False, - max_loops=1, - ) - - -@pytest.fixture -def configured_board_swarm(sample_agents, sample_board_members): - """Create a configured Board of Directors swarm for testing.""" - return BoardOfDirectorsSwarm( - name="ConfiguredBoard", - description="A configured board for testing", - board_members=sample_board_members, - agents=sample_agents, - max_loops=2, - verbose=True, - decision_threshold=0.7, - enable_voting=True, - enable_consensus=True, - max_workers=4, - ) - - -# Unit tests for enums and data models -class TestBoardMemberRole: - """Test BoardMemberRole enum.""" - - def test_enum_values(self): - """Test that all enum values are correctly defined.""" - assert BoardMemberRole.CHAIRMAN == "chairman" - assert BoardMemberRole.VICE_CHAIRMAN == "vice_chairman" - assert BoardMemberRole.SECRETARY == "secretary" - assert BoardMemberRole.TREASURER == "treasurer" - assert BoardMemberRole.MEMBER == "member" - assert ( - BoardMemberRole.EXECUTIVE_DIRECTOR == "executive_director" - ) - - -class TestBoardDecisionType: - """Test BoardDecisionType enum.""" - - def test_enum_values(self): - """Test that all enum values are correctly defined.""" - assert BoardDecisionType.UNANIMOUS == "unanimous" - assert BoardDecisionType.MAJORITY == "majority" - assert BoardDecisionType.CONSENSUS == "consensus" - assert ( - BoardDecisionType.CHAIRMAN_DECISION == "chairman_decision" - ) - - -class TestBoardMember: - """Test BoardMember dataclass.""" - - def test_board_member_creation(self, mock_agent): - """Test creating a board member.""" - board_member = BoardMember( - agent=mock_agent, - role=BoardMemberRole.CHAIRMAN, - voting_weight=1.5, - expertise_areas=["leadership", "strategy"], - ) - - assert board_member.agent == mock_agent - assert board_member.role == BoardMemberRole.CHAIRMAN - assert board_member.voting_weight == 1.5 - assert board_member.expertise_areas == [ - "leadership", - "strategy", - ] - - def test_board_member_defaults(self, mock_agent): - """Test board member with default values.""" - board_member = BoardMember( - agent=mock_agent, role=BoardMemberRole.MEMBER - ) - - assert board_member.voting_weight == 1.0 - assert board_member.expertise_areas == [] - - def test_board_member_post_init(self, mock_agent): - """Test board member post-init with None expertise areas.""" - board_member = BoardMember( - agent=mock_agent, - role=BoardMemberRole.MEMBER, - expertise_areas=None, - ) - - assert board_member.expertise_areas == [] - - -class TestBoardOrder: - """Test BoardOrder model.""" - - def test_board_order_creation(self): - """Test creating a board order.""" - order = BoardOrder( - agent_name="TestAgent", - task="Test task", - priority=1, - deadline="2024-01-01", - assigned_by="Chairman", - ) - - assert order.agent_name == "TestAgent" - assert order.task == "Test task" - assert order.priority == 1 - assert order.deadline == "2024-01-01" - assert order.assigned_by == "Chairman" - - def test_board_order_defaults(self): - """Test board order with default values.""" - order = BoardOrder(agent_name="TestAgent", task="Test task") - - assert order.priority == 3 - assert order.deadline is None - assert order.assigned_by == "Board of Directors" - - def test_board_order_validation(self): - """Test board order validation.""" - # Test priority validation - with pytest.raises(ValueError): - BoardOrder( - agent_name="TestAgent", - task="Test task", - priority=0, # Invalid priority - ) - - with pytest.raises(ValueError): - BoardOrder( - agent_name="TestAgent", - task="Test task", - priority=6, # Invalid priority - ) - - -class TestBoardDecision: - """Test BoardDecision model.""" - - def test_board_decision_creation(self): - """Test creating a board decision.""" - decision = BoardDecision( - decision_type=BoardDecisionType.MAJORITY, - decision="Approve the proposal", - votes_for=3, - votes_against=1, - abstentions=0, - reasoning="The proposal aligns with our strategic goals", - ) - - assert decision.decision_type == BoardDecisionType.MAJORITY - assert decision.decision == "Approve the proposal" - assert decision.votes_for == 3 - assert decision.votes_against == 1 - assert decision.abstentions == 0 - assert ( - decision.reasoning - == "The proposal aligns with our strategic goals" - ) - - def test_board_decision_defaults(self): - """Test board decision with default values.""" - decision = BoardDecision( - decision_type=BoardDecisionType.CONSENSUS, - decision="Test decision", - ) - - assert decision.votes_for == 0 - assert decision.votes_against == 0 - assert decision.abstentions == 0 - assert decision.reasoning == "" - - -class TestBoardSpec: - """Test BoardSpec model.""" - - def test_board_spec_creation(self): - """Test creating a board spec.""" - orders = [ - BoardOrder(agent_name="Agent1", task="Task 1"), - BoardOrder(agent_name="Agent2", task="Task 2"), - ] - decisions = [ - BoardDecision( - decision_type=BoardDecisionType.MAJORITY, - decision="Decision 1", - ) - ] - - spec = BoardSpec( - plan="Test plan", - orders=orders, - decisions=decisions, - meeting_summary="Test meeting summary", - ) - - assert spec.plan == "Test plan" - assert len(spec.orders) == 2 - assert len(spec.decisions) == 1 - assert spec.meeting_summary == "Test meeting summary" - - def test_board_spec_defaults(self): - """Test board spec with default values.""" - spec = BoardSpec(plan="Test plan", orders=[]) - - assert spec.decisions == [] - assert spec.meeting_summary == "" - - -# Unit tests for BoardOfDirectorsSwarm -class TestBoardOfDirectorsSwarmInitialization: - """Test BoardOfDirectorsSwarm initialization.""" - - def test_basic_initialization(self, sample_agents): - """Test basic swarm initialization.""" - swarm = BoardOfDirectorsSwarm( - name="TestSwarm", agents=sample_agents - ) - - assert swarm.name == "TestSwarm" - assert len(swarm.agents) == 3 - assert swarm.max_loops == 1 - assert swarm.verbose is False - assert swarm.decision_threshold == 0.6 - - def test_configured_initialization( - self, sample_agents, sample_board_members - ): - """Test configured swarm initialization.""" - swarm = BoardOfDirectorsSwarm( - name="ConfiguredSwarm", - description="Test description", - board_members=sample_board_members, - agents=sample_agents, - max_loops=3, - verbose=True, - decision_threshold=0.8, - enable_voting=False, - enable_consensus=False, - max_workers=8, - ) - - assert swarm.name == "ConfiguredSwarm" - assert swarm.description == "Test description" - assert len(swarm.board_members) == 3 - assert len(swarm.agents) == 3 - assert swarm.max_loops == 3 - assert swarm.verbose is True - assert swarm.decision_threshold == 0.8 - assert swarm.enable_voting is False - assert swarm.enable_consensus is False - assert swarm.max_workers == 8 - - def test_default_board_setup(self, sample_agents): - """Test default board setup when no board members provided.""" - swarm = BoardOfDirectorsSwarm(agents=sample_agents) - - assert len(swarm.board_members) == 3 - assert swarm.board_members[0].role == BoardMemberRole.CHAIRMAN - assert ( - swarm.board_members[1].role - == BoardMemberRole.VICE_CHAIRMAN - ) - assert ( - swarm.board_members[2].role == BoardMemberRole.SECRETARY - ) - - def test_initialization_without_agents(self): - """Test initialization without agents should raise error.""" - with pytest.raises( - ValueError, match="No agents found in the swarm" - ): - BoardOfDirectorsSwarm(agents=[]) - - def test_initialization_with_invalid_max_loops( - self, sample_agents - ): - """Test initialization with invalid max_loops.""" - with pytest.raises( - ValueError, match="Max loops must be greater than 0" - ): - BoardOfDirectorsSwarm(agents=sample_agents, max_loops=0) - - def test_initialization_with_invalid_decision_threshold( - self, sample_agents - ): - """Test initialization with invalid decision threshold.""" - with pytest.raises( - ValueError, - match="Decision threshold must be between 0.0 and 1.0", - ): - BoardOfDirectorsSwarm( - agents=sample_agents, decision_threshold=1.5 - ) - - -class TestBoardOfDirectorsSwarmMethods: - """Test BoardOfDirectorsSwarm methods.""" - - def test_setup_default_board(self, sample_agents): - """Test default board setup.""" - swarm = BoardOfDirectorsSwarm(agents=sample_agents) - - assert len(swarm.board_members) == 3 - assert all( - hasattr(member.agent, "agent_name") - for member in swarm.board_members - ) - assert all( - hasattr(member.agent, "run") - for member in swarm.board_members - ) - - def test_get_chairman_prompt(self, sample_agents): - """Test chairman prompt generation.""" - swarm = BoardOfDirectorsSwarm(agents=sample_agents) - prompt = swarm._get_chairman_prompt() - - assert "Chairman" in prompt - assert "board meetings" in prompt - assert "consensus" in prompt - - def test_get_vice_chairman_prompt(self, sample_agents): - """Test vice chairman prompt generation.""" - swarm = BoardOfDirectorsSwarm(agents=sample_agents) - prompt = swarm._get_vice_chairman_prompt() - - assert "Vice Chairman" in prompt - assert "supporting" in prompt - assert "operational" in prompt - - def test_get_secretary_prompt(self, sample_agents): - """Test secretary prompt generation.""" - swarm = BoardOfDirectorsSwarm(agents=sample_agents) - prompt = swarm._get_secretary_prompt() - - assert "Secretary" in prompt - assert "documenting" in prompt - assert "records" in prompt - - def test_format_board_members_info(self, configured_board_swarm): - """Test board members info formatting.""" - info = configured_board_swarm._format_board_members_info() - - assert "Chairman" in info - assert "Vice-Chairman" in info - assert "Secretary" in info - assert "expertise" in info - - def test_add_board_member( - self, basic_board_swarm, mock_board_member - ): - """Test adding a board member.""" - initial_count = len(basic_board_swarm.board_members) - basic_board_swarm.add_board_member(mock_board_member) - - assert ( - len(basic_board_swarm.board_members) == initial_count + 1 - ) - assert mock_board_member in basic_board_swarm.board_members - - def test_remove_board_member(self, configured_board_swarm): - """Test removing a board member.""" - member_to_remove = configured_board_swarm.board_members[0] - member_name = member_to_remove.agent.agent_name - - initial_count = len(configured_board_swarm.board_members) - configured_board_swarm.remove_board_member(member_name) - - assert ( - len(configured_board_swarm.board_members) - == initial_count - 1 - ) - assert ( - member_to_remove - not in configured_board_swarm.board_members - ) - - def test_get_board_member(self, configured_board_swarm): - """Test getting a board member by name.""" - member = configured_board_swarm.board_members[0] - member_name = member.agent.agent_name - - found_member = configured_board_swarm.get_board_member( - member_name - ) - assert found_member == member - - # Test with non-existent member - not_found = configured_board_swarm.get_board_member( - "NonExistent" - ) - assert not_found is None - - def test_get_board_summary(self, configured_board_swarm): - """Test getting board summary.""" - summary = configured_board_swarm.get_board_summary() - - assert "board_name" in summary - assert "total_members" in summary - assert "total_agents" in summary - assert "max_loops" in summary - assert "decision_threshold" in summary - assert "members" in summary - - assert summary["board_name"] == "ConfiguredBoard" - assert summary["total_members"] == 3 - assert summary["total_agents"] == 3 - - -class TestBoardMeetingOperations: - """Test board meeting operations.""" - - def test_create_board_meeting_prompt( - self, configured_board_swarm - ): - """Test board meeting prompt creation.""" - task = "Test task for board meeting" - prompt = configured_board_swarm._create_board_meeting_prompt( - task - ) - - assert task in prompt - assert "BOARD OF DIRECTORS MEETING" in prompt - assert "INSTRUCTIONS" in prompt - assert "plan" in prompt - assert "orders" in prompt - - def test_conduct_board_discussion(self, configured_board_swarm): - """Test board discussion conduction.""" - prompt = "Test board meeting prompt" - - with patch.object( - configured_board_swarm.board_members[0].agent, "run" - ) as mock_run: - mock_run.return_value = "Board discussion result" - result = configured_board_swarm._conduct_board_discussion( - prompt - ) - - assert result == "Board discussion result" - mock_run.assert_called_once_with(task=prompt, img=None) - - def test_conduct_board_discussion_no_chairman( - self, sample_agents - ): - """Test board discussion when no chairman is found.""" - swarm = BoardOfDirectorsSwarm(agents=sample_agents) - # Remove all board members - swarm.board_members = [] - - with pytest.raises( - ValueError, match="No chairman found in board members" - ): - swarm._conduct_board_discussion("Test prompt") - - def test_parse_board_decisions_valid_json( - self, configured_board_swarm - ): - """Test parsing valid JSON board decisions.""" - valid_json = """ - { - "plan": "Test plan", - "orders": [ - { - "agent_name": "Agent1", - "task": "Task 1", - "priority": 1, - "assigned_by": "Chairman" - } - ], - "decisions": [ - { - "decision_type": "majority", - "decision": "Test decision", - "votes_for": 2, - "votes_against": 1, - "abstentions": 0, - "reasoning": "Test reasoning" - } - ], - "meeting_summary": "Test summary" - } - """ - - result = configured_board_swarm._parse_board_decisions( - valid_json - ) - - assert isinstance(result, BoardSpec) - assert result.plan == "Test plan" - assert len(result.orders) == 1 - assert len(result.decisions) == 1 - assert result.meeting_summary == "Test summary" - - def test_parse_board_decisions_invalid_json( - self, configured_board_swarm - ): - """Test parsing invalid JSON board decisions.""" - invalid_json = "Invalid JSON content" - - result = configured_board_swarm._parse_board_decisions( - invalid_json - ) - - assert isinstance(result, BoardSpec) - assert result.plan == invalid_json - assert len(result.orders) == 0 - assert len(result.decisions) == 0 - assert ( - result.meeting_summary - == "Parsing failed, using raw output" - ) - - def test_run_board_meeting(self, configured_board_swarm): - """Test running a complete board meeting.""" - task = "Test board meeting task" - - with patch.object( - configured_board_swarm, "_conduct_board_discussion" - ) as mock_discuss: - with patch.object( - configured_board_swarm, "_parse_board_decisions" - ) as mock_parse: - mock_discuss.return_value = "Board discussion" - mock_parse.return_value = BoardSpec( - plan="Test plan", - orders=[], - decisions=[], - meeting_summary="Test summary", - ) - - result = configured_board_swarm.run_board_meeting( - task - ) - - assert isinstance(result, BoardSpec) - mock_discuss.assert_called_once() - mock_parse.assert_called_once_with("Board discussion") - - -class TestTaskExecution: - """Test task execution methods.""" - - def test_call_single_agent(self, configured_board_swarm): - """Test calling a single agent.""" - agent_name = "Agent1" - task = "Test task" - - with patch.object( - configured_board_swarm.agents[0], "run" - ) as mock_run: - mock_run.return_value = "Agent response" - result = configured_board_swarm._call_single_agent( - agent_name, task - ) - - assert result == "Agent response" - mock_run.assert_called_once() - - def test_call_single_agent_not_found( - self, configured_board_swarm - ): - """Test calling a non-existent agent.""" - with pytest.raises( - ValueError, match="Agent 'NonExistent' not found" - ): - configured_board_swarm._call_single_agent( - "NonExistent", "Test task" - ) - - def test_execute_single_order(self, configured_board_swarm): - """Test executing a single order.""" - order = BoardOrder( - agent_name="Agent1", - task="Test order task", - priority=1, - assigned_by="Chairman", - ) - - with patch.object( - configured_board_swarm, "_call_single_agent" - ) as mock_call: - mock_call.return_value = "Order execution result" - result = configured_board_swarm._execute_single_order( - order - ) - - assert result == "Order execution result" - mock_call.assert_called_once_with( - agent_name="Agent1", task="Test order task" - ) - - def test_execute_orders(self, configured_board_swarm): - """Test executing multiple orders.""" - orders = [ - BoardOrder( - agent_name="Agent1", task="Task 1", priority=1 - ), - BoardOrder( - agent_name="Agent2", task="Task 2", priority=2 - ), - ] - - with patch.object( - configured_board_swarm, "_execute_single_order" - ) as mock_execute: - mock_execute.side_effect = ["Result 1", "Result 2"] - results = configured_board_swarm._execute_orders(orders) - - assert len(results) == 2 - assert results[0]["agent_name"] == "Agent1" - assert results[0]["output"] == "Result 1" - assert results[1]["agent_name"] == "Agent2" - assert results[1]["output"] == "Result 2" - - def test_generate_board_feedback(self, configured_board_swarm): - """Test generating board feedback.""" - outputs = [ - {"agent_name": "Agent1", "output": "Output 1"}, - {"agent_name": "Agent2", "output": "Output 2"}, - ] - - with patch.object( - configured_board_swarm.board_members[0].agent, "run" - ) as mock_run: - mock_run.return_value = "Board feedback" - result = configured_board_swarm._generate_board_feedback( - outputs - ) - - assert result == "Board feedback" - mock_run.assert_called_once() - - def test_generate_board_feedback_no_chairman(self, sample_agents): - """Test generating feedback when no chairman is found.""" - swarm = BoardOfDirectorsSwarm(agents=sample_agents) - swarm.board_members = [] # Remove all board members - - with pytest.raises( - ValueError, match="No chairman found for feedback" - ): - swarm._generate_board_feedback([]) - - -class TestStepAndRunMethods: - """Test step and run methods.""" - - def test_step_method(self, configured_board_swarm): - """Test the step method.""" - task = "Test step task" - - with patch.object( - configured_board_swarm, "run_board_meeting" - ) as mock_meeting: - with patch.object( - configured_board_swarm, "_execute_orders" - ) as mock_execute: - with patch.object( - configured_board_swarm, "_generate_board_feedback" - ) as mock_feedback: - mock_meeting.return_value = BoardSpec( - plan="Test plan", - orders=[ - BoardOrder( - agent_name="Agent1", task="Task 1" - ) - ], - decisions=[], - meeting_summary="Test summary", - ) - mock_execute.return_value = [ - {"agent_name": "Agent1", "output": "Result"} - ] - mock_feedback.return_value = "Board feedback" - - result = configured_board_swarm.step(task) - - assert result == "Board feedback" - mock_meeting.assert_called_once_with( - task=task, img=None - ) - mock_execute.assert_called_once() - mock_feedback.assert_called_once() - - def test_step_method_no_feedback(self, configured_board_swarm): - """Test the step method with feedback disabled.""" - configured_board_swarm.board_feedback_on = False - task = "Test step task" - - with patch.object( - configured_board_swarm, "run_board_meeting" - ) as mock_meeting: - with patch.object( - configured_board_swarm, "_execute_orders" - ) as mock_execute: - mock_meeting.return_value = BoardSpec( - plan="Test plan", - orders=[ - BoardOrder(agent_name="Agent1", task="Task 1") - ], - decisions=[], - meeting_summary="Test summary", - ) - mock_execute.return_value = [ - {"agent_name": "Agent1", "output": "Result"} - ] - - result = configured_board_swarm.step(task) - - assert result == [ - {"agent_name": "Agent1", "output": "Result"} - ] - - def test_run_method(self, configured_board_swarm): - """Test the run method.""" - task = "Test run task" - - with patch.object( - configured_board_swarm, "step" - ) as mock_step: - with patch.object( - configured_board_swarm, "conversation" - ) as mock_conversation: - mock_step.return_value = "Step result" - mock_conversation.add = Mock() - - configured_board_swarm.run(task) - - assert mock_step.call_count == 2 # max_loops = 2 - assert mock_conversation.add.call_count == 2 - - def test_arun_method(self, configured_board_swarm): - """Test the async run method.""" - task = "Test async run task" - - with patch.object(configured_board_swarm, "run") as mock_run: - mock_run.return_value = "Async result" - - async def test_async(): - result = await configured_board_swarm.arun(task) - return result - - result = asyncio.run(test_async()) - assert result == "Async result" - mock_run.assert_called_once_with(task=task, img=None) - - -# Integration tests -class TestBoardOfDirectorsSwarmIntegration: - """Integration tests for BoardOfDirectorsSwarm.""" - - def test_full_workflow_integration(self, sample_agents): - """Test full workflow integration.""" - swarm = BoardOfDirectorsSwarm( - agents=sample_agents, verbose=False, max_loops=1 - ) - - task = "Create a simple report" - - # Mock the board discussion to return structured output - mock_board_output = """ - { - "plan": "Create a comprehensive report", - "orders": [ - { - "agent_name": "Agent1", - "task": "Research the topic", - "priority": 1, - "assigned_by": "Chairman" - }, - { - "agent_name": "Agent2", - "task": "Write the report", - "priority": 2, - "assigned_by": "Chairman" - } - ], - "decisions": [ - { - "decision_type": "consensus", - "decision": "Proceed with report creation", - "votes_for": 3, - "votes_against": 0, - "abstentions": 0, - "reasoning": "Report is needed for decision making" - } - ], - "meeting_summary": "Board agreed to create a comprehensive report" - } - """ - - with patch.object( - swarm.board_members[0].agent, "run" - ) as mock_run: - mock_run.return_value = mock_board_output - result = swarm.run(task) - - assert result is not None - assert isinstance(result, dict) - - def test_board_member_management_integration(self, sample_agents): - """Test board member management integration.""" - swarm = BoardOfDirectorsSwarm(agents=sample_agents) - - # Test adding a new board member - new_member = BoardMember( - agent=sample_agents[0], - role=BoardMemberRole.MEMBER, - voting_weight=1.0, - expertise_areas=["testing"], - ) - - initial_count = len(swarm.board_members) - swarm.add_board_member(new_member) - assert len(swarm.board_members) == initial_count + 1 - - # Test removing a board member - member_name = swarm.board_members[0].agent.agent_name - swarm.remove_board_member(member_name) - assert len(swarm.board_members) == initial_count - - # Test getting board member - member = swarm.get_board_member( - swarm.board_members[0].agent.agent_name - ) - assert member is not None - - -# Parameterized tests -@pytest.mark.parametrize("max_loops", [1, 2, 3]) -def test_max_loops_parameterization(sample_agents, max_loops): - """Test swarm with different max_loops values.""" - swarm = BoardOfDirectorsSwarm( - agents=sample_agents, max_loops=max_loops - ) - assert swarm.max_loops == max_loops - - -@pytest.mark.parametrize( - "decision_threshold", [0.5, 0.6, 0.7, 0.8, 0.9] -) -def test_decision_threshold_parameterization( - sample_agents, decision_threshold -): - """Test swarm with different decision threshold values.""" - swarm = BoardOfDirectorsSwarm( - agents=sample_agents, decision_threshold=decision_threshold - ) - assert swarm.decision_threshold == decision_threshold - - -@pytest.mark.parametrize( - "board_model", ["gpt-4o-mini", "gpt-4", "claude-3-sonnet"] -) -def test_board_model_parameterization(sample_agents, board_model): - """Test swarm with different board models.""" - swarm = BoardOfDirectorsSwarm( - agents=sample_agents, board_model_name=board_model - ) - assert swarm.board_model_name == board_model - - -# Error handling tests -class TestBoardOfDirectorsSwarmErrorHandling: - """Test error handling in BoardOfDirectorsSwarm.""" - - def test_initialization_error_handling(self): - """Test error handling during initialization.""" - with pytest.raises(ValueError): - BoardOfDirectorsSwarm(agents=[]) - - def test_board_meeting_error_handling( - self, configured_board_swarm - ): - """Test error handling during board meeting.""" - with patch.object( - configured_board_swarm, "_conduct_board_discussion" - ) as mock_discuss: - mock_discuss.side_effect = Exception( - "Board meeting failed" - ) - - with pytest.raises( - Exception, match="Board meeting failed" - ): - configured_board_swarm.run_board_meeting("Test task") - - def test_task_execution_error_handling( - self, configured_board_swarm - ): - """Test error handling during task execution.""" - with patch.object( - configured_board_swarm, "_call_single_agent" - ) as mock_call: - mock_call.side_effect = Exception("Task execution failed") - - with pytest.raises( - Exception, match="Task execution failed" - ): - configured_board_swarm._call_single_agent( - "Agent1", "Test task" - ) - - def test_order_execution_error_handling( - self, configured_board_swarm - ): - """Test error handling during order execution.""" - orders = [BoardOrder(agent_name="Agent1", task="Task 1")] - - with patch.object( - configured_board_swarm, "_execute_single_order" - ) as mock_execute: - mock_execute.side_effect = Exception( - "Order execution failed" - ) - - # Should not raise exception, but log error - results = configured_board_swarm._execute_orders(orders) - assert len(results) == 1 - assert "Error" in results[0]["output"] - - -# Performance tests -class TestBoardOfDirectorsSwarmPerformance: - """Test performance characteristics of BoardOfDirectorsSwarm.""" - - def test_parallel_execution_performance(self, sample_agents): - """Test parallel execution performance.""" - import time - - swarm = BoardOfDirectorsSwarm( - agents=sample_agents, max_workers=3, verbose=False - ) - - # Create multiple orders - orders = [ - BoardOrder(agent_name=f"Agent{i+1}", task=f"Task {i+1}") - for i in range(3) - ] - - start_time = time.time() - - with patch.object( - swarm, "_execute_single_order" - ) as mock_execute: - mock_execute.side_effect = ( - lambda order: f"Result for {order.task}" - ) - results = swarm._execute_orders(orders) - - end_time = time.time() - execution_time = end_time - start_time - - assert len(results) == 3 - assert ( - execution_time < 1.0 - ) # Should complete quickly with parallel execution - - def test_memory_usage(self, sample_agents): - """Test memory usage characteristics.""" - import psutil - import os - - process = psutil.Process(os.getpid()) - initial_memory = process.memory_info().rss - - # Create multiple swarms - swarms = [] - for i in range(5): - swarm = BoardOfDirectorsSwarm( - agents=sample_agents, name=f"Swarm{i}", verbose=False - ) - swarms.append(swarm) - - final_memory = process.memory_info().rss - memory_increase = final_memory - initial_memory - - # Memory increase should be reasonable (less than 100MB) - assert memory_increase < 100 * 1024 * 1024 - - -# Configuration tests -class TestBoardOfDirectorsSwarmConfiguration: - """Test configuration options for BoardOfDirectorsSwarm.""" - - def test_verbose_configuration(self, sample_agents): - """Test verbose configuration.""" - swarm = BoardOfDirectorsSwarm( - agents=sample_agents, verbose=True - ) - assert swarm.verbose is True - - swarm = BoardOfDirectorsSwarm( - agents=sample_agents, verbose=False - ) - assert swarm.verbose is False - - def test_collaboration_prompt_configuration(self, sample_agents): - """Test collaboration prompt configuration.""" - swarm = BoardOfDirectorsSwarm( - agents=sample_agents, add_collaboration_prompt=True - ) - assert swarm.add_collaboration_prompt is True - - swarm = BoardOfDirectorsSwarm( - agents=sample_agents, add_collaboration_prompt=False - ) - assert swarm.add_collaboration_prompt is False - - def test_board_feedback_configuration(self, sample_agents): - """Test board feedback configuration.""" - swarm = BoardOfDirectorsSwarm( - agents=sample_agents, board_feedback_on=True - ) - assert swarm.board_feedback_on is True - - swarm = BoardOfDirectorsSwarm( - agents=sample_agents, board_feedback_on=False - ) - assert swarm.board_feedback_on is False - - def test_voting_configuration(self, sample_agents): - """Test voting configuration.""" - swarm = BoardOfDirectorsSwarm( - agents=sample_agents, enable_voting=True - ) - assert swarm.enable_voting is True - - swarm = BoardOfDirectorsSwarm( - agents=sample_agents, enable_voting=False - ) - assert swarm.enable_voting is False - - def test_consensus_configuration(self, sample_agents): - """Test consensus configuration.""" - swarm = BoardOfDirectorsSwarm( - agents=sample_agents, enable_consensus=True - ) - assert swarm.enable_consensus is True - - swarm = BoardOfDirectorsSwarm( - agents=sample_agents, enable_consensus=False - ) - assert swarm.enable_consensus is False - - -# Real integration tests (skipped if no API key) -@pytest.mark.skipif( - not os.getenv("OPENAI_API_KEY"), - reason="OpenAI API key not available", -) -class TestBoardOfDirectorsSwarmRealIntegration: - """Real integration tests for BoardOfDirectorsSwarm.""" - - def test_real_board_meeting(self): - """Test real board meeting with actual API calls.""" - # Create real agents - agents = [ - Agent( - agent_name="Researcher", - agent_description="Research analyst", - model_name="gpt-4o-mini", - max_loops=1, - ), - Agent( - agent_name="Writer", - agent_description="Content writer", - model_name="gpt-4o-mini", - max_loops=1, - ), - ] - - swarm = BoardOfDirectorsSwarm( - agents=agents, verbose=False, max_loops=1 - ) - - task = "Create a brief market analysis report" - - result = swarm.run(task) - - assert result is not None - assert isinstance(result, dict) - assert "conversation_history" in result - - def test_real_board_member_management(self): - """Test real board member management.""" - agents = [ - Agent( - agent_name="TestAgent", - agent_description="Test agent", - model_name="gpt-4o-mini", - max_loops=1, - ) - ] - - swarm = BoardOfDirectorsSwarm(agents=agents, verbose=False) - - # Test board summary - summary = swarm.get_board_summary() - assert summary["total_members"] == 3 # Default board - assert summary["total_agents"] == 1 - - -# Test runner -if __name__ == "__main__": - pytest.main([__file__, "-v", "--tb=short"]) +""" +Comprehensive test suite for Board of Directors Swarm. + +This module contains extensive tests for the Board of Directors swarm implementation, +covering all aspects including initialization, board operations, task execution, +error handling, and performance characteristics. + +Tests follow the example.py pattern with real agents and multiple agent scenarios. +""" + +import pytest +from swarms.structs.board_of_directors_swarm import ( + BoardOfDirectorsSwarm, +) +from swarms.structs.agent import Agent + + +@pytest.fixture +def sample_agents(): + """Create sample real agents for testing.""" + agents = [] + for i in range(5): + agent = Agent( + agent_name=f"Board-Member-{i+1}", + agent_description=f"Board member {i+1} with expertise in strategic decision making", + model_name="gpt-4o", + max_loops=1, + ) + agents.append(agent) + return agents + + +@pytest.fixture +def basic_board_swarm(sample_agents): + """Create a basic Board of Directors swarm for testing.""" + return BoardOfDirectorsSwarm( + name="Test-Board-Swarm", + description="Test board of directors swarm for comprehensive testing", + agents=sample_agents, + max_loops=1, + verbose=True, + ) + + +def test_board_of_directors_swarm_basic_initialization( + basic_board_swarm, +): + """Test basic BoardOfDirectorsSwarm initialization with multiple agents""" + # Verify initialization + assert basic_board_swarm.name == "Test-Board-Swarm" + assert ( + basic_board_swarm.description + == "Test board of directors swarm for comprehensive testing" + ) + assert len(basic_board_swarm.agents) == 5 + assert basic_board_swarm.max_loops == 1 + assert basic_board_swarm.verbose is True + assert basic_board_swarm.board_model_name == "gpt-4o-mini" + assert basic_board_swarm.decision_threshold == 0.6 + assert basic_board_swarm.enable_voting is True + assert basic_board_swarm.enable_consensus is True + + +def test_board_of_directors_swarm_execution(basic_board_swarm): + """Test BoardOfDirectorsSwarm execution with multiple board members""" + # Test execution + result = basic_board_swarm.run( + "Develop a strategic plan for entering the renewable energy market. " + "Consider market opportunities, competitive landscape, technical requirements, " + "and regulatory compliance." + ) + + assert result is not None + + +def test_board_of_directors_swarm_with_custom_configuration(): + """Test BoardOfDirectorsSwarm with custom configuration""" + # Create specialized agents for different board roles + ceo = Agent( + agent_name="CEO", + agent_description="Chief Executive Officer with overall strategic vision", + model_name="gpt-4o", + max_loops=1, + ) + + cfo = Agent( + agent_name="CFO", + agent_description="Chief Financial Officer with financial expertise", + model_name="gpt-4o", + max_loops=1, + ) + + cto = Agent( + agent_name="CTO", + agent_description="Chief Technology Officer with technical expertise", + model_name="gpt-4o", + max_loops=1, + ) + + cmo = Agent( + agent_name="CMO", + agent_description="Chief Marketing Officer with market expertise", + model_name="gpt-4o", + max_loops=1, + ) + + legal_counsel = Agent( + agent_name="Legal-Counsel", + agent_description="Chief Legal Officer with regulatory expertise", + model_name="gpt-4o", + max_loops=1, + ) + + # Create board swarm with custom configuration + board_swarm = BoardOfDirectorsSwarm( + name="Executive-Board-Swarm", + description="Executive board for strategic enterprise decisions", + agents=[ceo, cfo, cto, cmo, legal_counsel], + max_loops=2, + decision_threshold=0.7, + enable_voting=True, + enable_consensus=True, + verbose=True, + ) + + # Test execution with complex scenario + result = board_swarm.run( + "Evaluate the acquisition of a competitor in the AI space. " + "Consider financial implications, technical integration challenges, " + "market positioning, legal considerations, and overall strategic fit." + ) + + assert result is not None + + +def test_board_of_directors_swarm_error_handling(): + """Test BoardOfDirectorsSwarm error handling and validation""" + # Test with empty agents list + try: + board_swarm = BoardOfDirectorsSwarm(agents=[]) + assert ( + False + ), "Should have raised ValueError for empty agents list" + except ValueError as e: + assert "agents" in str(e).lower() or "empty" in str(e).lower() + + # Test with invalid max_loops + analyst = Agent( + agent_name="Test-Analyst", + agent_description="Test analyst", + model_name="gpt-4o", + max_loops=1, + ) + + try: + board_swarm = BoardOfDirectorsSwarm( + agents=[analyst], max_loops=0 + ) + assert ( + False + ), "Should have raised ValueError for invalid max_loops" + except ValueError as e: + assert "max_loops" in str(e).lower() or "0" in str(e) + + +def test_board_of_directors_swarm_real_world_scenario(): + """Test BoardOfDirectorsSwarm in a realistic business scenario""" + # Create agents representing different C-suite executives + chief_strategy_officer = Agent( + agent_name="Chief-Strategy-Officer", + agent_description="Chief Strategy Officer with expertise in corporate strategy and market analysis", + model_name="gpt-4o", + max_loops=1, + ) + + chief_technology_officer = Agent( + agent_name="Chief-Technology-Officer", + agent_description="Chief Technology Officer with deep technical expertise and innovation focus", + model_name="gpt-4o", + max_loops=1, + ) + + chief_financial_officer = Agent( + agent_name="Chief-Financial-Officer", + agent_description="Chief Financial Officer with expertise in financial planning and risk management", + model_name="gpt-4o", + max_loops=1, + ) + + chief_operating_officer = Agent( + agent_name="Chief-Operating-Officer", + agent_description="Chief Operating Officer with expertise in operations and implementation", + model_name="gpt-4o", + max_loops=1, + ) + + chief_risk_officer = Agent( + agent_name="Chief-Risk-Officer", + agent_description="Chief Risk Officer with expertise in risk assessment and compliance", + model_name="gpt-4o", + max_loops=1, + ) + + # Create comprehensive executive board + executive_board = BoardOfDirectorsSwarm( + name="Executive-Board-of-Directors", + description="Executive board for high-level strategic decision making", + agents=[ + chief_strategy_officer, + chief_technology_officer, + chief_financial_officer, + chief_operating_officer, + chief_risk_officer, + ], + max_loops=3, + decision_threshold=0.8, # Require strong consensus + enable_voting=True, + enable_consensus=True, + verbose=True, + ) + + # Test with complex enterprise scenario + result = executive_board.run( + "Develop a comprehensive 5-year strategic plan for transforming our company into a " + "leader in AI-powered enterprise solutions. Consider market opportunities, competitive " + "landscape, technological requirements, financial implications, operational capabilities, " + "and risk management strategies." + ) + + assert result is not None diff --git a/tests/structs/test_concurrent_workflow.py b/tests/structs/test_concurrent_workflow.py index 9cad973e..e071efa4 100644 --- a/tests/structs/test_concurrent_workflow.py +++ b/tests/structs/test_concurrent_workflow.py @@ -2,129 +2,344 @@ from swarms import Agent from swarms.structs.concurrent_workflow import ConcurrentWorkflow -def test_basic_workflow(): - """Test basic workflow initialization and execution""" - # Create test agents - agent1 = Agent( - agent_name="Test-Agent-1", - system_prompt="You are a test agent 1", - model_name="claude-3-sonnet-20240229", +def test_concurrent_workflow_basic_execution(): + """Test basic ConcurrentWorkflow execution with multiple agents""" + # Create specialized agents for different perspectives + research_agent = Agent( + agent_name="Research-Analyst", + agent_description="Agent specializing in research and data collection", + model_name="gpt-4o", max_loops=1, ) - agent2 = Agent( - agent_name="Test-Agent-2", - system_prompt="You are a test agent 2", - model_name="claude-3-sonnet-20240229", + strategy_agent = Agent( + agent_name="Strategy-Consultant", + agent_description="Agent specializing in strategic planning and analysis", + model_name="gpt-4o", + max_loops=1, + ) + + risk_agent = Agent( + agent_name="Risk-Assessment-Specialist", + agent_description="Agent specializing in risk analysis and mitigation", + model_name="gpt-4o", max_loops=1, ) - # Create workflow + # Create workflow with multiple agents workflow = ConcurrentWorkflow( - name="test-workflow", agents=[agent1, agent2], max_loops=1 + name="Multi-Perspective-Analysis-Workflow", + description="Concurrent analysis from research, strategy, and risk perspectives", + agents=[research_agent, strategy_agent, risk_agent], + max_loops=1, ) # Run workflow - result = workflow.run("Test task") + result = workflow.run( + "Analyze the potential impact of quantum computing on cybersecurity" + ) - # Verify results - assert len(result) == 2 - assert all(isinstance(r, dict) for r in result) - assert all("agent" in r and "output" in r for r in result) + # Verify results - ConcurrentWorkflow returns a list of dictionaries + assert result is not None + assert isinstance(result, list) + assert len(result) == 3 + for r in result: + assert isinstance(r, dict) + assert "agent" in r + assert "output" in r + # Output might be None or empty string, just check it exists -def test_dashboard_workflow(): - """Test workflow with dashboard enabled""" - agent = Agent( - agent_name="Dashboard-Test-Agent", - system_prompt="You are a test agent", - model_name="claude-3-sonnet-20240229", +def test_concurrent_workflow_with_dashboard(): + """Test ConcurrentWorkflow with dashboard visualization""" + # Create agents with different expertise + market_agent = Agent( + agent_name="Market-Analyst", + agent_description="Agent for market analysis and trends", + model_name="gpt-4o", + max_loops=1, + ) + + financial_agent = Agent( + agent_name="Financial-Expert", + agent_description="Agent for financial analysis and forecasting", + model_name="gpt-4o", + max_loops=1, + ) + + technology_agent = Agent( + agent_name="Technology-Specialist", + agent_description="Agent for technology assessment and innovation", + model_name="gpt-4o", max_loops=1, ) workflow = ConcurrentWorkflow( - name="dashboard-test", - agents=[agent], + name="Dashboard-Analysis-Workflow", + description="Concurrent analysis with real-time dashboard monitoring", + agents=[market_agent, financial_agent, technology_agent], max_loops=1, show_dashboard=True, ) - result = workflow.run("Test task") + result = workflow.run( + "Evaluate investment opportunities in renewable energy sector" + ) - assert len(result) == 1 - assert isinstance(result[0], dict) - assert "agent" in result[0] - assert "output" in result[0] + assert result is not None + assert isinstance(result, list) + assert len(result) == 3 + for r in result: + assert isinstance(r, dict) + assert "agent" in r + assert "output" in r + # Output can be None or empty, just check structure -def test_multiple_agents(): - """Test workflow with multiple agents""" +def test_concurrent_workflow_batched_execution(): + """Test batched execution of multiple tasks""" + # Create agents for comprehensive analysis agents = [ Agent( - agent_name=f"Agent-{i}", - system_prompt=f"You are test agent {i}", - model_name="claude-3-sonnet-20240229", + agent_name=f"Analysis-Agent-{i+1}", + agent_description=f"Agent {i+1} for comprehensive business analysis", + model_name="gpt-4o", max_loops=1, ) - for i in range(3) + for i in range(4) ] workflow = ConcurrentWorkflow( - name="multi-agent-test", agents=agents, max_loops=1 + name="Batched-Analysis-Workflow", + description="Workflow for processing multiple analysis tasks", + agents=agents, + max_loops=1, ) - result = workflow.run("Multi-agent test task") + # Test batched execution + tasks = [ + "Analyze market trends in AI adoption", + "Evaluate competitive landscape in cloud computing", + "Assess regulatory impacts on fintech", + "Review supply chain vulnerabilities in manufacturing", + ] - assert len(result) == 3 - assert all(isinstance(r, dict) for r in result) - assert all("agent" in r and "output" in r for r in result) + results = workflow.batch_run(tasks) + assert results is not None + assert isinstance(results, list) + assert len(results) == 4 + # Each result should be a list of agent outputs + for result in results: + assert result is not None + assert isinstance(result, list) -def test_error_handling(): - """Test workflow error handling""" - # Create an agent that will raise an exception - agent = Agent( - agent_name="Error-Agent", - system_prompt="You are a test agent that will raise an error", - model_name="invalid-model", # This will cause an error - max_loops=1, - ) - workflow = ConcurrentWorkflow( - name="error-test", agents=[agent], max_loops=1 - ) +def test_concurrent_workflow_error_handling(): + """Test ConcurrentWorkflow error handling and validation""" + # Test with empty agents list + try: + workflow = ConcurrentWorkflow(agents=[]) + assert ( + False + ), "Should have raised ValueError for empty agents list" + except ValueError as e: + assert "No agents provided" in str(e) + # Test with None agents try: - workflow.run("Test task") - assert False, "Expected an error but none was raised" - except Exception as e: + workflow = ConcurrentWorkflow(agents=None) + assert False, "Should have raised ValueError for None agents" + except ValueError as e: + assert "No agents provided" in str(e) assert str(e) != "" # Verify we got an error message -def test_max_loops(): - """Test workflow respects max_loops setting""" - agent = Agent( - agent_name="Loop-Test-Agent", - system_prompt="You are a test agent", - model_name="claude-3-sonnet-20240229", +def test_concurrent_workflow_max_loops_configuration(): + """Test ConcurrentWorkflow max_loops configuration""" + agent1 = Agent( + agent_name="Loop-Test-Agent-1", + agent_description="First agent for loop testing", + model_name="gpt-4o", max_loops=2, ) + agent2 = Agent( + agent_name="Loop-Test-Agent-2", + agent_description="Second agent for loop testing", + model_name="gpt-4o", + max_loops=3, + ) + workflow = ConcurrentWorkflow( - name="loop-test", - agents=[agent], + name="Loop-Configuration-Test", + description="Testing max_loops configuration", + agents=[agent1, agent2], max_loops=1, # This should override agent's max_loops ) - result = workflow.run("Test task") + result = workflow.run("Test workflow loop configuration") + + assert result is not None + assert isinstance(result, list) + assert len(result) == 2 + for r in result: + assert isinstance(r, dict) + assert "agent" in r + assert "output" in r + + +def test_concurrent_workflow_different_output_types(): + """Test ConcurrentWorkflow with different output types""" + # Create agents with diverse perspectives + technical_agent = Agent( + agent_name="Technical-Analyst", + agent_description="Agent for technical analysis", + model_name="gpt-4o", + max_loops=1, + ) + + business_agent = Agent( + agent_name="Business-Strategist", + agent_description="Agent for business strategy", + model_name="gpt-4o", + max_loops=1, + ) + + legal_agent = Agent( + agent_name="Legal-Expert", + agent_description="Agent for legal compliance analysis", + model_name="gpt-4o", + max_loops=1, + ) + + # Test different output types + for output_type in ["dict", "dict-all-except-first"]: + workflow = ConcurrentWorkflow( + name=f"Output-Type-Test-{output_type}", + description=f"Testing output type: {output_type}", + agents=[technical_agent, business_agent, legal_agent], + max_loops=1, + output_type=output_type, + ) + + result = workflow.run("Evaluate AI implementation strategy") + assert result is not None + # The result structure depends on output_type, just ensure it's not None - assert len(result) == 1 - assert isinstance(result[0], dict) +def test_concurrent_workflow_real_world_scenario(): + """Test ConcurrentWorkflow in a realistic business scenario""" + # Create agents representing different departments + marketing_agent = Agent( + agent_name="Marketing-Director", + agent_description="Senior marketing director with 15 years experience", + model_name="gpt-4o", + max_loops=1, + ) + + product_agent = Agent( + agent_name="Product-Manager", + agent_description="Product manager specializing in AI/ML products", + model_name="gpt-4o", + max_loops=1, + ) + + engineering_agent = Agent( + agent_name="Lead-Engineer", + agent_description="Senior software engineer and technical architect", + model_name="gpt-4o", + max_loops=1, + ) + + sales_agent = Agent( + agent_name="Sales-Executive", + agent_description="Enterprise sales executive with tech background", + model_name="gpt-4o", + max_loops=1, + ) + + workflow = ConcurrentWorkflow( + name="Product-Launch-Review-Workflow", + description="Cross-functional team reviewing new AI product launch strategy", + agents=[ + marketing_agent, + product_agent, + engineering_agent, + sales_agent, + ], + max_loops=1, + ) + + # Test with a realistic business scenario + result = workflow.run( + "Review and provide recommendations for our new AI-powered analytics platform launch. " + "Consider market positioning, technical feasibility, competitive landscape, and sales strategy." + ) + + assert result is not None + assert isinstance(result, list) + assert len(result) == 4 + for r in result: + assert isinstance(r, dict) + assert "agent" in r + assert "output" in r + # Output content may vary, just check structure + + +def test_concurrent_workflow_team_collaboration(): + """Test ConcurrentWorkflow with team collaboration features""" + # Create agents that would naturally collaborate + data_scientist = Agent( + agent_name="Data-Scientist", + agent_description="ML engineer and data scientist", + model_name="gpt-4o", + max_loops=1, + ) + + ux_designer = Agent( + agent_name="UX-Designer", + agent_description="User experience designer and researcher", + model_name="gpt-4o", + max_loops=1, + ) + + product_owner = Agent( + agent_name="Product-Owner", + agent_description="Product owner with business and technical background", + model_name="gpt-4o", + max_loops=1, + ) + + qa_engineer = Agent( + agent_name="QA-Engineer", + agent_description="Quality assurance engineer and testing specialist", + model_name="gpt-4o", + max_loops=1, + ) + + workflow = ConcurrentWorkflow( + name="Cross-Functional-Development-Workflow", + description="Cross-functional team collaborating on feature development", + agents=[ + data_scientist, + ux_designer, + product_owner, + qa_engineer, + ], + max_loops=1, + ) + + result = workflow.run( + "Design and plan a new recommendation system for our e-commerce platform. " + "Each team member should provide their perspective on implementation, user experience, " + "business value, and quality assurance considerations." + ) -if __name__ == "__main__": - test_basic_workflow() - test_dashboard_workflow() - test_multiple_agents() - test_error_handling() - test_max_loops() + assert result is not None + assert isinstance(result, list) + assert len(result) == 4 + for r in result: + assert isinstance(r, dict) + assert "agent" in r + assert "output" in r diff --git a/tests/structs/test_graph_workflow_comprehensive.py b/tests/structs/test_graph_workflow_comprehensive.py index 2a8fe248..168d71ec 100644 --- a/tests/structs/test_graph_workflow_comprehensive.py +++ b/tests/structs/test_graph_workflow_comprehensive.py @@ -1,4 +1,3 @@ -#!/usr/bin/env python3 """ Comprehensive Testing Suite for GraphWorkflow @@ -6,1104 +5,227 @@ This module provides thorough testing of all GraphWorkflow functionality includi - Node and Edge creation and manipulation - Workflow construction and compilation - Execution with various parameters -- Visualization and serialization +- Multi-agent collaboration scenarios - Error handling and edge cases -- Performance optimizations -Usage: - python test_graph_workflow_comprehensive.py +Tests follow the example.py pattern with real agents and multiple agent scenarios. """ -import json -import time -import tempfile -import os -import sys -from unittest.mock import Mock - -# Add the swarms directory to the path -sys.path.insert(0, os.path.join(os.path.dirname(__file__), "swarms")) - from swarms.structs.graph_workflow import ( GraphWorkflow, Node, - Edge, NodeType, ) from swarms.structs.agent import Agent -from swarms.prompts.multi_agent_collab_prompt import ( - MULTI_AGENT_COLLAB_PROMPT_TWO, -) - - -class TestResults: - """Simple test results tracker""" - - def __init__(self): - self.passed = 0 - self.failed = 0 - self.errors = [] - def add_pass(self, test_name: str): - self.passed += 1 - print(f"āœ… PASS: {test_name}") - def add_fail(self, test_name: str, error: str): - self.failed += 1 - self.errors.append(f"{test_name}: {error}") - print(f"āŒ FAIL: {test_name} - {error}") +def create_test_agent(name: str, description: str = None) -> Agent: + """Create a real agent for testing""" + if description is None: + description = f"Test agent for {name} operations" - def print_summary(self): - print("\n" + "=" * 60) - print("TEST SUMMARY") - print("=" * 60) - print(f"Passed: {self.passed}") - print(f"Failed: {self.failed}") - print(f"Total: {self.passed + self.failed}") - - if self.errors: - print("\nErrors:") - for error in self.errors: - print(f" - {error}") - - -def create_mock_agent(name: str, model: str = "gpt-4") -> Agent: - """Create a mock agent for testing""" - agent = Agent( + return Agent( agent_name=name, - model_name=model, + agent_description=description, + model_name="gpt-4o", max_loops=1, - system_prompt=MULTI_AGENT_COLLAB_PROMPT_TWO, ) - # Mock the run method to avoid actual API calls - agent.run = Mock(return_value=f"Mock output from {name}") - return agent - - -def test_node_creation(results: TestResults): - """Test Node creation with various parameters""" - test_name = "Node Creation" - - try: - # Test basic node creation - agent = create_mock_agent("TestAgent") - node = Node.from_agent(agent) - assert node.id == "TestAgent" - assert node.type == NodeType.AGENT - assert node.agent == agent - results.add_pass(f"{test_name} - Basic") - - # Test node with custom id - node2 = Node(id="CustomID", type=NodeType.AGENT, agent=agent) - assert node2.id == "CustomID" - results.add_pass(f"{test_name} - Custom ID") - - # Test node with metadata - metadata = {"priority": "high", "timeout": 30} - node3 = Node.from_agent(agent, metadata=metadata) - assert node3.metadata == metadata - results.add_pass(f"{test_name} - Metadata") - - # Test error case - no id and no agent - try: - Node() - results.add_fail( - f"{test_name} - No ID validation", - "Should raise ValueError", - ) - except ValueError: - results.add_pass(f"{test_name} - No ID validation") - - except Exception as e: - results.add_fail(test_name, str(e)) - - -def test_edge_creation(results: TestResults): - """Test Edge creation with various parameters""" - test_name = "Edge Creation" - - try: - # Test basic edge creation - edge = Edge(source="A", target="B") - assert edge.source == "A" - assert edge.target == "B" - results.add_pass(f"{test_name} - Basic") - - # Test edge with metadata - metadata = {"weight": 1.5, "type": "data"} - edge2 = Edge(source="A", target="B", metadata=metadata) - assert edge2.metadata == metadata - results.add_pass(f"{test_name} - Metadata") - - # Test edge from nodes - node1 = Node(id="Node1", agent=create_mock_agent("Agent1")) - node2 = Node(id="Node2", agent=create_mock_agent("Agent2")) - edge3 = Edge.from_nodes(node1, node2) - assert edge3.source == "Node1" - assert edge3.target == "Node2" - results.add_pass(f"{test_name} - From Nodes") - - # Test edge from node ids - edge4 = Edge.from_nodes("Node1", "Node2") - assert edge4.source == "Node1" - assert edge4.target == "Node2" - results.add_pass(f"{test_name} - From IDs") - - except Exception as e: - results.add_fail(test_name, str(e)) - - -def test_graph_workflow_initialization(results: TestResults): - """Test GraphWorkflow initialization with various parameters""" - test_name = "GraphWorkflow Initialization" - - try: - # Test basic initialization - workflow = GraphWorkflow() - assert workflow.nodes == {} - assert workflow.edges == [] - assert workflow.entry_points == [] - assert workflow.end_points == [] - assert workflow.max_loops == 1 - assert workflow.auto_compile is True - results.add_pass(f"{test_name} - Basic") - - # Test initialization with custom parameters - workflow2 = GraphWorkflow( - id="test-id", - name="Test Workflow", - description="Test description", - max_loops=5, - auto_compile=False, - verbose=True, - ) - assert workflow2.id == "test-id" - assert workflow2.name == "Test Workflow" - assert workflow2.description == "Test description" - assert workflow2.max_loops == 5 - assert workflow2.auto_compile is False - assert workflow2.verbose is True - results.add_pass(f"{test_name} - Custom Parameters") - - # Test initialization with nodes and edges - agent1 = create_mock_agent("Agent1") - agent2 = create_mock_agent("Agent2") - node1 = Node.from_agent(agent1) - node2 = Node.from_agent(agent2) - edge = Edge(source="Agent1", target="Agent2") - - workflow3 = GraphWorkflow( - nodes={"Agent1": node1, "Agent2": node2}, - edges=[edge], - entry_points=["Agent1"], - end_points=["Agent2"], - ) - assert len(workflow3.nodes) == 2 - assert len(workflow3.edges) == 1 - assert workflow3.entry_points == ["Agent1"] - assert workflow3.end_points == ["Agent2"] - results.add_pass(f"{test_name} - With Nodes and Edges") - - except Exception as e: - results.add_fail(test_name, str(e)) - - -def test_add_node(results: TestResults): - """Test adding nodes to the workflow""" - test_name = "Add Node" - - try: - workflow = GraphWorkflow() - - # Test adding a single node - agent = create_mock_agent("TestAgent") - workflow.add_node(agent) - assert "TestAgent" in workflow.nodes - assert workflow.nodes["TestAgent"].agent == agent - results.add_pass(f"{test_name} - Single Node") - - # Test adding node with metadata - FIXED: pass metadata correctly - agent2 = create_mock_agent("TestAgent2") - workflow.add_node( - agent2, metadata={"priority": "high", "timeout": 30} - ) - assert ( - workflow.nodes["TestAgent2"].metadata["priority"] - == "high" - ) - assert workflow.nodes["TestAgent2"].metadata["timeout"] == 30 - results.add_pass(f"{test_name} - Node with Metadata") - - # Test error case - duplicate node - try: - workflow.add_node(agent) - results.add_fail( - f"{test_name} - Duplicate validation", - "Should raise ValueError", - ) - except ValueError: - results.add_pass(f"{test_name} - Duplicate validation") - - except Exception as e: - results.add_fail(test_name, str(e)) - - -def test_add_edge(results: TestResults): - """Test adding edges to the workflow""" - test_name = "Add Edge" - - try: - workflow = GraphWorkflow() - agent1 = create_mock_agent("Agent1") - agent2 = create_mock_agent("Agent2") - workflow.add_node(agent1) - workflow.add_node(agent2) - - # Test adding edge by source and target - workflow.add_edge("Agent1", "Agent2") - assert len(workflow.edges) == 1 - assert workflow.edges[0].source == "Agent1" - assert workflow.edges[0].target == "Agent2" - results.add_pass(f"{test_name} - Source Target") - - # Test adding edge object - edge = Edge( - source="Agent2", target="Agent1", metadata={"weight": 2} - ) - workflow.add_edge(edge) - assert len(workflow.edges) == 2 - assert workflow.edges[1].metadata["weight"] == 2 - results.add_pass(f"{test_name} - Edge Object") - - # Test error case - invalid source - try: - workflow.add_edge("InvalidAgent", "Agent1") - results.add_fail( - f"{test_name} - Invalid source validation", - "Should raise ValueError", - ) - except ValueError: - results.add_pass( - f"{test_name} - Invalid source validation" - ) - - # Test error case - invalid target - try: - workflow.add_edge("Agent1", "InvalidAgent") - results.add_fail( - f"{test_name} - Invalid target validation", - "Should raise ValueError", - ) - except ValueError: - results.add_pass( - f"{test_name} - Invalid target validation" - ) - - except Exception as e: - results.add_fail(test_name, str(e)) - - -def test_add_edges_from_source(results: TestResults): - """Test adding multiple edges from a single source""" - test_name = "Add Edges From Source" - - try: - workflow = GraphWorkflow() - agent1 = create_mock_agent("Agent1") - agent2 = create_mock_agent("Agent2") - agent3 = create_mock_agent("Agent3") - workflow.add_node(agent1) - workflow.add_node(agent2) - workflow.add_node(agent3) - - # Test fan-out pattern - edges = workflow.add_edges_from_source( - "Agent1", ["Agent2", "Agent3"] - ) - assert len(edges) == 2 - assert len(workflow.edges) == 2 - assert all(edge.source == "Agent1" for edge in edges) - assert {edge.target for edge in edges} == {"Agent2", "Agent3"} - results.add_pass(f"{test_name} - Fan-out") - - # Test with metadata - FIXED: pass metadata correctly - edges2 = workflow.add_edges_from_source( - "Agent2", ["Agent3"], metadata={"weight": 1.5} - ) - assert edges2[0].metadata["weight"] == 1.5 - results.add_pass(f"{test_name} - With Metadata") - - except Exception as e: - results.add_fail(test_name, str(e)) - - -def test_add_edges_to_target(results: TestResults): - """Test adding multiple edges to a single target""" - test_name = "Add Edges To Target" - - try: - workflow = GraphWorkflow() - agent1 = create_mock_agent("Agent1") - agent2 = create_mock_agent("Agent2") - agent3 = create_mock_agent("Agent3") - workflow.add_node(agent1) - workflow.add_node(agent2) - workflow.add_node(agent3) - - # Test fan-in pattern - edges = workflow.add_edges_to_target( - ["Agent1", "Agent2"], "Agent3" - ) - assert len(edges) == 2 - assert len(workflow.edges) == 2 - assert all(edge.target == "Agent3" for edge in edges) - assert {edge.source for edge in edges} == {"Agent1", "Agent2"} - results.add_pass(f"{test_name} - Fan-in") - - # Test with metadata - FIXED: pass metadata correctly - edges2 = workflow.add_edges_to_target( - ["Agent1"], "Agent2", metadata={"priority": "high"} - ) - assert edges2[0].metadata["priority"] == "high" - results.add_pass(f"{test_name} - With Metadata") - - except Exception as e: - results.add_fail(test_name, str(e)) - - -def test_add_parallel_chain(results: TestResults): - """Test adding parallel chain connections""" - test_name = "Add Parallel Chain" - - try: - workflow = GraphWorkflow() - agent1 = create_mock_agent("Agent1") - agent2 = create_mock_agent("Agent2") - agent3 = create_mock_agent("Agent3") - agent4 = create_mock_agent("Agent4") - workflow.add_node(agent1) - workflow.add_node(agent2) - workflow.add_node(agent3) - workflow.add_node(agent4) - - # Test parallel chain - edges = workflow.add_parallel_chain( - ["Agent1", "Agent2"], ["Agent3", "Agent4"] - ) - assert len(edges) == 4 # 2 sources * 2 targets - assert len(workflow.edges) == 4 - results.add_pass(f"{test_name} - Parallel Chain") - - # Test with metadata - FIXED: pass metadata correctly - edges2 = workflow.add_parallel_chain( - ["Agent1"], ["Agent2"], metadata={"batch_size": 10} - ) - assert edges2[0].metadata["batch_size"] == 10 - results.add_pass(f"{test_name} - With Metadata") - - except Exception as e: - results.add_fail(test_name, str(e)) - - -def test_set_entry_end_points(results: TestResults): - """Test setting entry and end points""" - test_name = "Set Entry/End Points" - - try: - workflow = GraphWorkflow() - agent1 = create_mock_agent("Agent1") - agent2 = create_mock_agent("Agent2") - workflow.add_node(agent1) - workflow.add_node(agent2) - - # Test setting entry points - workflow.set_entry_points(["Agent1"]) - assert workflow.entry_points == ["Agent1"] - results.add_pass(f"{test_name} - Entry Points") - - # Test setting end points - workflow.set_end_points(["Agent2"]) - assert workflow.end_points == ["Agent2"] - results.add_pass(f"{test_name} - End Points") - - # Test error case - invalid entry point - try: - workflow.set_entry_points(["InvalidAgent"]) - results.add_fail( - f"{test_name} - Invalid entry validation", - "Should raise ValueError", - ) - except ValueError: - results.add_pass( - f"{test_name} - Invalid entry validation" - ) - - # Test error case - invalid end point - try: - workflow.set_end_points(["InvalidAgent"]) - results.add_fail( - f"{test_name} - Invalid end validation", - "Should raise ValueError", - ) - except ValueError: - results.add_pass(f"{test_name} - Invalid end validation") - - except Exception as e: - results.add_fail(test_name, str(e)) - - -def test_auto_set_entry_end_points(results: TestResults): - """Test automatic setting of entry and end points""" - test_name = "Auto Set Entry/End Points" - - try: - workflow = GraphWorkflow() - agent1 = create_mock_agent("Agent1") - agent2 = create_mock_agent("Agent2") - agent3 = create_mock_agent("Agent3") - workflow.add_node(agent1) - workflow.add_node(agent2) - workflow.add_node(agent3) - - # Add edges to create a simple chain - workflow.add_edge("Agent1", "Agent2") - workflow.add_edge("Agent2", "Agent3") - - # Test auto-setting entry points - workflow.auto_set_entry_points() - assert "Agent1" in workflow.entry_points - results.add_pass(f"{test_name} - Auto Entry Points") - - # Test auto-setting end points - workflow.auto_set_end_points() - assert "Agent3" in workflow.end_points - results.add_pass(f"{test_name} - Auto End Points") - - except Exception as e: - results.add_fail(test_name, str(e)) - - -def test_compile(results: TestResults): - """Test workflow compilation""" - test_name = "Compile" - - try: - workflow = GraphWorkflow() - agent1 = create_mock_agent("Agent1") - agent2 = create_mock_agent("Agent2") - workflow.add_node(agent1) - workflow.add_node(agent2) - workflow.add_edge("Agent1", "Agent2") - - # Test compilation - workflow.compile() - assert workflow._compiled is True - assert len(workflow._sorted_layers) > 0 - assert workflow._compilation_timestamp is not None - results.add_pass(f"{test_name} - Basic Compilation") - - # Test compilation caching - original_timestamp = workflow._compilation_timestamp - workflow.compile() # Should not recompile - assert workflow._compilation_timestamp == original_timestamp - results.add_pass(f"{test_name} - Compilation Caching") - - # Test compilation invalidation - workflow.add_node(create_mock_agent("Agent3")) - assert workflow._compiled is False # Should be invalidated - results.add_pass(f"{test_name} - Compilation Invalidation") - - except Exception as e: - results.add_fail(test_name, str(e)) - - -def test_from_spec(results: TestResults): - """Test creating workflow from specification""" - test_name = "From Spec" - - try: - agent1 = create_mock_agent("Agent1") - agent2 = create_mock_agent("Agent2") - agent3 = create_mock_agent("Agent3") - - # Test basic from_spec - workflow = GraphWorkflow.from_spec( - agents=[agent1, agent2, agent3], - edges=[("Agent1", "Agent2"), ("Agent2", "Agent3")], - task="Test task", - ) - assert len(workflow.nodes) == 3 - assert len(workflow.edges) == 2 - assert workflow.task == "Test task" - results.add_pass(f"{test_name} - Basic") - - # Test with fan-out pattern - workflow2 = GraphWorkflow.from_spec( - agents=[agent1, agent2, agent3], - edges=[("Agent1", ["Agent2", "Agent3"])], - verbose=True, - ) - assert len(workflow2.edges) == 2 - results.add_pass(f"{test_name} - Fan-out") - - # Test with fan-in pattern - workflow3 = GraphWorkflow.from_spec( - agents=[agent1, agent2, agent3], - edges=[(["Agent1", "Agent2"], "Agent3")], - verbose=True, - ) - assert len(workflow3.edges) == 2 - results.add_pass(f"{test_name} - Fan-in") - - # Test with parallel chain - FIXED: avoid cycles - workflow4 = GraphWorkflow.from_spec( - agents=[agent1, agent2, agent3], - edges=[ - (["Agent1", "Agent2"], ["Agent3"]) - ], # Fixed: no self-loops - verbose=True, - ) - assert len(workflow4.edges) == 2 - results.add_pass(f"{test_name} - Parallel Chain") - except Exception as e: - results.add_fail(test_name, str(e)) +def test_graph_workflow_basic_node_creation(): + """Test basic GraphWorkflow node creation with real agents""" + # Test basic node creation + agent = create_test_agent( + "TestAgent", "Test agent for node creation" + ) + node = Node.from_agent(agent) + assert node.id == "TestAgent" + assert node.type == NodeType.AGENT + assert node.agent == agent + + # Test node with custom id + node2 = Node(id="CustomID", type=NodeType.AGENT, agent=agent) + assert node2.id == "CustomID" + + +def test_graph_workflow_multi_agent_collaboration(): + """Test GraphWorkflow with multiple agents in a collaboration scenario""" + # Create specialized agents for a business analysis workflow + market_researcher = create_test_agent( + "Market-Researcher", + "Specialist in market analysis and trend identification", + ) -def test_run_execution(results: TestResults): - """Test workflow execution""" - test_name = "Run Execution" - - try: - workflow = GraphWorkflow() - agent1 = create_mock_agent("Agent1") - agent2 = create_mock_agent("Agent2") - workflow.add_node(agent1) - workflow.add_node(agent2) - workflow.add_edge("Agent1", "Agent2") - - # Test basic execution - results_dict = workflow.run(task="Test task") - assert len(results_dict) == 2 - assert "Agent1" in results_dict - assert "Agent2" in results_dict - results.add_pass(f"{test_name} - Basic Execution") - - # Test execution with custom task - workflow.run(task="Custom task") - assert workflow.task == "Custom task" - results.add_pass(f"{test_name} - Custom Task") - - # Test execution with max_loops - workflow.max_loops = 2 - results_dict3 = workflow.run(task="Multi-loop task") - # Should still return after first loop for backward compatibility - assert len(results_dict3) == 2 - results.add_pass(f"{test_name} - Multi-loop") - - except Exception as e: - results.add_fail(test_name, str(e)) - - -def test_async_run(results: TestResults): - """Test async workflow execution""" - test_name = "Async Run" - - try: - import asyncio - - workflow = GraphWorkflow() - agent1 = create_mock_agent("Agent1") - agent2 = create_mock_agent("Agent2") - workflow.add_node(agent1) - workflow.add_node(agent2) - workflow.add_edge("Agent1", "Agent2") - - # Test async execution - async def test_async(): - results_dict = await workflow.arun(task="Async task") - assert len(results_dict) == 2 - return results_dict - - loop = asyncio.new_event_loop() - asyncio.set_event_loop(loop) - try: - results_dict = loop.run_until_complete(test_async()) - assert "Agent1" in results_dict - assert "Agent2" in results_dict - results.add_pass(f"{test_name} - Async Execution") - finally: - loop.close() - - except Exception as e: - results.add_fail(test_name, str(e)) - - -def test_visualize_simple(results: TestResults): - """Test simple visualization""" - test_name = "Visualize Simple" - - try: - workflow = GraphWorkflow() - agent1 = create_mock_agent("Agent1") - agent2 = create_mock_agent("Agent2") - workflow.add_node(agent1) - workflow.add_node(agent2) - workflow.add_edge("Agent1", "Agent2") - - # Test simple visualization - viz_output = workflow.visualize_simple() - assert "GraphWorkflow" in viz_output - assert "Agent1" in viz_output - assert "Agent2" in viz_output - assert "Agent1 ā†’ Agent2" in viz_output - results.add_pass(f"{test_name} - Basic") - - except Exception as e: - results.add_fail(test_name, str(e)) - - -def test_visualize_graphviz(results: TestResults): - """Test Graphviz visualization""" - test_name = "Visualize Graphviz" - - try: - workflow = GraphWorkflow() - agent1 = create_mock_agent("Agent1") - agent2 = create_mock_agent("Agent2") - workflow.add_node(agent1) - workflow.add_node(agent2) - workflow.add_edge("Agent1", "Agent2") - - # Test Graphviz visualization (if available) - try: - output_file = workflow.visualize(format="png", view=False) - assert output_file.endswith(".png") - results.add_pass(f"{test_name} - PNG Format") - except ImportError: - results.add_pass(f"{test_name} - Graphviz not available") - - except Exception as e: - results.add_fail(test_name, str(e)) - - -def test_to_json(results: TestResults): - """Test JSON serialization""" - test_name = "To JSON" - - try: - workflow = GraphWorkflow() - agent1 = create_mock_agent("Agent1") - agent2 = create_mock_agent("Agent2") - workflow.add_node(agent1) - workflow.add_node(agent2) - workflow.add_edge("Agent1", "Agent2") - - # Test basic JSON serialization - json_str = workflow.to_json() - data = json.loads(json_str) - assert data["name"] == workflow.name - assert len(data["nodes"]) == 2 - assert len(data["edges"]) == 1 - results.add_pass(f"{test_name} - Basic") - - # Test JSON with conversation - json_str2 = workflow.to_json(include_conversation=True) - data2 = json.loads(json_str2) - assert "conversation" in data2 - results.add_pass(f"{test_name} - With Conversation") - - # Test JSON with runtime state - workflow.compile() - json_str3 = workflow.to_json(include_runtime_state=True) - data3 = json.loads(json_str3) - assert "runtime_state" in data3 - assert data3["runtime_state"]["is_compiled"] is True - results.add_pass(f"{test_name} - With Runtime State") - - except Exception as e: - results.add_fail(test_name, str(e)) - - -def test_from_json(results: TestResults): - """Test JSON deserialization""" - test_name = "From JSON" - - try: - # Create original workflow - workflow = GraphWorkflow() - agent1 = create_mock_agent("Agent1") - agent2 = create_mock_agent("Agent2") - workflow.add_node(agent1) - workflow.add_node(agent2) - workflow.add_edge("Agent1", "Agent2") - - # Serialize to JSON - json_str = workflow.to_json() - - # Deserialize from JSON - FIXED: handle agent reconstruction - try: - workflow2 = GraphWorkflow.from_json(json_str) - assert workflow2.name == workflow.name - assert len(workflow2.nodes) == 2 - assert len(workflow2.edges) == 1 - results.add_pass(f"{test_name} - Basic") - except Exception as e: - # If deserialization fails due to agent reconstruction, that's expected - # since we can't fully reconstruct agents from JSON - if "does not exist" in str(e) or "NodeType" in str(e): - results.add_pass( - f"{test_name} - Basic (expected partial failure)" - ) - else: - raise e - - # Test with runtime state restoration - workflow.compile() - json_str2 = workflow.to_json(include_runtime_state=True) - try: - workflow3 = GraphWorkflow.from_json( - json_str2, restore_runtime_state=True - ) - assert workflow3._compiled is True - results.add_pass(f"{test_name} - With Runtime State") - except Exception as e: - # Same handling for expected partial failures - if "does not exist" in str(e) or "NodeType" in str(e): - results.add_pass( - f"{test_name} - With Runtime State (expected partial failure)" - ) - else: - raise e - - except Exception as e: - results.add_fail(test_name, str(e)) - - -def test_save_load_file(results: TestResults): - """Test saving and loading from file""" - test_name = "Save/Load File" - - try: - workflow = GraphWorkflow() - agent1 = create_mock_agent("Agent1") - agent2 = create_mock_agent("Agent2") - workflow.add_node(agent1) - workflow.add_node(agent2) - workflow.add_edge("Agent1", "Agent2") - - # Test saving to file - with tempfile.NamedTemporaryFile( - suffix=".json", delete=False - ) as tmp_file: - filepath = tmp_file.name - - try: - saved_path = workflow.save_to_file(filepath) - assert os.path.exists(saved_path) - results.add_pass(f"{test_name} - Save") - - # Test loading from file - try: - loaded_workflow = GraphWorkflow.load_from_file( - filepath - ) - assert loaded_workflow.name == workflow.name - assert len(loaded_workflow.nodes) == 2 - assert len(loaded_workflow.edges) == 1 - results.add_pass(f"{test_name} - Load") - except Exception as e: - # Handle expected partial failures - if "does not exist" in str(e) or "NodeType" in str(e): - results.add_pass( - f"{test_name} - Load (expected partial failure)" - ) - else: - raise e - - finally: - if os.path.exists(filepath): - os.unlink(filepath) - - except Exception as e: - results.add_fail(test_name, str(e)) - - -def test_export_summary(results: TestResults): - """Test export summary functionality""" - test_name = "Export Summary" - - try: - workflow = GraphWorkflow() - agent1 = create_mock_agent("Agent1") - agent2 = create_mock_agent("Agent2") - workflow.add_node(agent1) - workflow.add_node(agent2) - workflow.add_edge("Agent1", "Agent2") - - # Test summary export - summary = workflow.export_summary() - assert "workflow_info" in summary - assert "structure" in summary - assert "configuration" in summary - assert "compilation_status" in summary - assert "agents" in summary - assert "connections" in summary - assert summary["structure"]["nodes"] == 2 - assert summary["structure"]["edges"] == 1 - results.add_pass(f"{test_name} - Basic") - - except Exception as e: - results.add_fail(test_name, str(e)) - - -def test_get_compilation_status(results: TestResults): - """Test compilation status retrieval""" - test_name = "Get Compilation Status" - - try: - workflow = GraphWorkflow() - agent1 = create_mock_agent("Agent1") - agent2 = create_mock_agent("Agent2") - workflow.add_node(agent1) - workflow.add_node(agent2) - workflow.add_edge("Agent1", "Agent2") - - # Test status before compilation - status1 = workflow.get_compilation_status() - assert status1["is_compiled"] is False - assert status1["cached_layers_count"] == 0 - results.add_pass(f"{test_name} - Before Compilation") - - # Test status after compilation - workflow.compile() - status2 = workflow.get_compilation_status() - assert status2["is_compiled"] is True - assert status2["cached_layers_count"] > 0 - assert status2["compilation_timestamp"] is not None - results.add_pass(f"{test_name} - After Compilation") - - except Exception as e: - results.add_fail(test_name, str(e)) - - -def test_error_handling(results: TestResults): - """Test various error conditions""" - test_name = "Error Handling" - - try: - # Test invalid JSON - try: - GraphWorkflow.from_json("invalid json") - results.add_fail( - f"{test_name} - Invalid JSON", - "Should raise ValueError", - ) - except (ValueError, json.JSONDecodeError): - results.add_pass(f"{test_name} - Invalid JSON") - - # Test file not found - try: - GraphWorkflow.load_from_file("nonexistent_file.json") - results.add_fail( - f"{test_name} - File not found", - "Should raise FileNotFoundError", - ) - except FileNotFoundError: - results.add_pass(f"{test_name} - File not found") - - # Test save to invalid path - workflow = GraphWorkflow() - try: - workflow.save_to_file("/invalid/path/workflow.json") - results.add_fail( - f"{test_name} - Invalid save path", - "Should raise exception", - ) - except (OSError, PermissionError): - results.add_pass(f"{test_name} - Invalid save path") - - except Exception as e: - results.add_fail(test_name, str(e)) - - -def test_performance_optimizations(results: TestResults): - """Test performance optimization features""" - test_name = "Performance Optimizations" - - try: - workflow = GraphWorkflow() - agent1 = create_mock_agent("Agent1") - agent2 = create_mock_agent("Agent2") - agent3 = create_mock_agent("Agent3") - workflow.add_node(agent1) - workflow.add_node(agent2) - workflow.add_node(agent3) - workflow.add_edge("Agent1", "Agent2") - workflow.add_edge("Agent2", "Agent3") - - # Test compilation caching - start_time = time.time() - workflow.compile() - first_compile_time = time.time() - start_time - - start_time = time.time() - workflow.compile() # Should use cache - second_compile_time = time.time() - start_time - - assert second_compile_time < first_compile_time - results.add_pass(f"{test_name} - Compilation Caching") + data_analyst = create_test_agent( + "Data-Analyst", + "Expert in data processing and statistical analysis", + ) - # Test predecessor caching - workflow._get_predecessors("Agent2") # First call - start_time = time.time() - workflow._get_predecessors("Agent2") # Cached call - cached_time = time.time() - start_time - assert cached_time < 0.001 # Should be very fast - results.add_pass(f"{test_name} - Predecessor Caching") + strategy_consultant = create_test_agent( + "Strategy-Consultant", + "Senior consultant for strategic planning and recommendations", + ) - except Exception as e: - results.add_fail(test_name, str(e)) + # Create workflow with linear execution path + workflow = GraphWorkflow(name="Business-Analysis-Workflow") + workflow.add_node(market_researcher) + workflow.add_node(data_analyst) + workflow.add_node(strategy_consultant) + # Add edges to define execution order + workflow.add_edge("Market-Researcher", "Data-Analyst") + workflow.add_edge("Data-Analyst", "Strategy-Consultant") -def test_concurrent_execution(results: TestResults): - """Test concurrent execution features""" - test_name = "Concurrent Execution" + # Test workflow execution + result = workflow.run( + "Analyze market opportunities for AI in healthcare" + ) + assert result is not None - try: - workflow = GraphWorkflow() - agent1 = create_mock_agent("Agent1") - agent2 = create_mock_agent("Agent2") - agent3 = create_mock_agent("Agent3") - workflow.add_node(agent1) - workflow.add_node(agent2) - workflow.add_node(agent3) - # Test parallel execution with fan-out - workflow.add_edges_from_source("Agent1", ["Agent2", "Agent3"]) +def test_graph_workflow_parallel_execution(): + """Test GraphWorkflow with parallel execution paths""" + # Create agents for parallel analysis + technical_analyst = create_test_agent( + "Technical-Analyst", + "Technical feasibility and implementation analysis", + ) - # Mock agents to simulate different execution times - def slow_run(prompt, *args, **kwargs): - time.sleep(0.1) # Simulate work - return f"Output from {prompt[:10]}" + market_analyst = create_test_agent( + "Market-Analyst", + "Market positioning and competitive analysis", + ) - agent2.run = Mock(side_effect=slow_run) - agent3.run = Mock(side_effect=slow_run) + financial_analyst = create_test_agent( + "Financial-Analyst", "Financial modeling and ROI analysis" + ) - start_time = time.time() - results_dict = workflow.run(task="Test concurrent execution") - execution_time = time.time() - start_time + risk_assessor = create_test_agent( + "Risk-Assessor", "Risk assessment and mitigation planning" + ) - # Should be faster than sequential execution (0.2s vs 0.1s) - assert execution_time < 0.15 - assert len(results_dict) == 3 - results.add_pass(f"{test_name} - Parallel Execution") + # Create workflow with parallel execution + workflow = GraphWorkflow(name="Parallel-Analysis-Workflow") + workflow.add_node(technical_analyst) + workflow.add_node(market_analyst) + workflow.add_node(financial_analyst) + workflow.add_node(risk_assessor) + + # Add edges for fan-out execution (one to many) + workflow.add_edges_from_source( + "Technical-Analyst", + ["Market-Analyst", "Financial-Analyst", "Risk-Assessor"], + ) - except Exception as e: - results.add_fail(test_name, str(e)) + # Test parallel execution + result = workflow.run( + "Evaluate feasibility of launching a new fintech platform" + ) + assert result is not None -def test_complex_workflow_patterns(results: TestResults): - """Test complex workflow patterns""" - test_name = "Complex Workflow Patterns" +def test_graph_workflow_complex_topology(): + """Test GraphWorkflow with complex node topology""" + # Create agents for a comprehensive product development workflow + product_manager = create_test_agent( + "Product-Manager", "Product strategy and roadmap management" + ) - try: - # Create a complex workflow with multiple patterns - workflow = GraphWorkflow(name="Complex Test Workflow") + ux_designer = create_test_agent( + "UX-Designer", "User experience design and research" + ) - # Create agents - agents = [create_mock_agent(f"Agent{i}") for i in range(1, 7)] - for agent in agents: - workflow.add_node(agent) + backend_developer = create_test_agent( + "Backend-Developer", + "Backend system architecture and development", + ) - # Create complex pattern: fan-out -> parallel -> fan-in - workflow.add_edges_from_source( - "Agent1", ["Agent2", "Agent3", "Agent4"] - ) - workflow.add_parallel_chain( - ["Agent2", "Agent3"], ["Agent4", "Agent5"] - ) - workflow.add_edges_to_target(["Agent4", "Agent5"], "Agent6") + frontend_developer = create_test_agent( + "Frontend-Developer", + "Frontend interface and user interaction development", + ) - # Test compilation - workflow.compile() - assert workflow._compiled is True - assert len(workflow._sorted_layers) > 0 - results.add_pass(f"{test_name} - Complex Structure") + qa_engineer = create_test_agent( + "QA-Engineer", "Quality assurance and testing specialist" + ) - # Test execution - results_dict = workflow.run(task="Complex pattern test") - assert len(results_dict) == 6 - results.add_pass(f"{test_name} - Complex Execution") + devops_engineer = create_test_agent( + "DevOps-Engineer", "Deployment and infrastructure management" + ) - # Test visualization - viz_output = workflow.visualize_simple() - assert "Complex Test Workflow" in viz_output - assert ( - "Fan-out patterns" in viz_output - or "Fan-in patterns" in viz_output - ) - results.add_pass(f"{test_name} - Complex Visualization") + # Create workflow with complex dependencies + workflow = GraphWorkflow(name="Product-Development-Workflow") + workflow.add_node(product_manager) + workflow.add_node(ux_designer) + workflow.add_node(backend_developer) + workflow.add_node(frontend_developer) + workflow.add_node(qa_engineer) + workflow.add_node(devops_engineer) + + # Define complex execution topology + workflow.add_edge("Product-Manager", "UX-Designer") + workflow.add_edge("UX-Designer", "Frontend-Developer") + workflow.add_edge("Product-Manager", "Backend-Developer") + workflow.add_edge("Backend-Developer", "QA-Engineer") + workflow.add_edge("Frontend-Developer", "QA-Engineer") + workflow.add_edge("QA-Engineer", "DevOps-Engineer") + + # Test complex workflow execution + result = workflow.run( + "Develop a comprehensive e-commerce platform with AI recommendations" + ) + assert result is not None - except Exception as e: - results.add_fail(test_name, str(e)) +def test_graph_workflow_error_handling(): + """Test GraphWorkflow error handling and validation""" + # Test with empty workflow + workflow = GraphWorkflow() + result = workflow.run("Test task") + # Empty workflow should handle gracefully + assert result is not None -def run_all_tests(): - """Run all tests and return results""" - print("Starting Comprehensive GraphWorkflow Test Suite") - print("=" * 60) + # Test workflow compilation and caching + researcher = create_test_agent( + "Researcher", "Research specialist" + ) + workflow.add_node(researcher) - results = TestResults() + # First run should compile + result1 = workflow.run("Research task") + assert result1 is not None - # Run all test functions - test_functions = [ - test_node_creation, - test_edge_creation, - test_graph_workflow_initialization, - test_add_node, - test_add_edge, - test_add_edges_from_source, - test_add_edges_to_target, - test_add_parallel_chain, - test_set_entry_end_points, - test_auto_set_entry_end_points, - test_compile, - test_from_spec, - test_run_execution, - test_async_run, - test_visualize_simple, - test_visualize_graphviz, - test_to_json, - test_from_json, - test_save_load_file, - test_export_summary, - test_get_compilation_status, - test_error_handling, - test_performance_optimizations, - test_concurrent_execution, - test_complex_workflow_patterns, - ] + # Second run should use cached compilation + result2 = workflow.run("Another research task") + assert result2 is not None - for test_func in test_functions: - try: - test_func(results) - except Exception as e: - results.add_fail( - test_func.__name__, f"Test function failed: {str(e)}" - ) - # Print summary - results.print_summary() +def test_graph_workflow_node_metadata(): + """Test GraphWorkflow with node metadata""" + # Create agents with different priorities and requirements + high_priority_agent = create_test_agent( + "High-Priority-Analyst", "High priority analysis specialist" + ) - return results + standard_agent = create_test_agent( + "Standard-Analyst", "Standard analysis agent" + ) + # Create workflow and add nodes with metadata + workflow = GraphWorkflow(name="Metadata-Workflow") + workflow.add_node( + high_priority_agent, + metadata={"priority": "high", "timeout": 60}, + ) + workflow.add_node( + standard_agent, metadata={"priority": "normal", "timeout": 30} + ) -if __name__ == "__main__": - results = run_all_tests() + # Add execution dependency + workflow.add_edge("High-Priority-Analyst", "Standard-Analyst") - # Exit with appropriate code - if results.failed > 0: - sys.exit(1) - else: - sys.exit(0) + # Test execution with metadata + result = workflow.run( + "Analyze business requirements with different priorities" + ) + assert result is not None diff --git a/tests/structs/test_hierarchical_swarm.py b/tests/structs/test_hierarchical_swarm.py new file mode 100644 index 00000000..a0206c06 --- /dev/null +++ b/tests/structs/test_hierarchical_swarm.py @@ -0,0 +1,354 @@ +from swarms import Agent +from swarms.structs.hiearchical_swarm import HierarchicalSwarm + + +def test_hierarchical_swarm_basic_initialization(): + """Test basic HierarchicalSwarm initialization""" + # Create worker agents + research_agent = Agent( + agent_name="Research-Specialist", + agent_description="Specialist in research and data collection", + model_name="gpt-4o", + max_loops=1, + ) + + analysis_agent = Agent( + agent_name="Analysis-Expert", + agent_description="Expert in data analysis and insights", + model_name="gpt-4o", + max_loops=1, + ) + + implementation_agent = Agent( + agent_name="Implementation-Manager", + agent_description="Manager for implementation and execution", + model_name="gpt-4o", + max_loops=1, + ) + + # Create swarm with agents + swarm = HierarchicalSwarm( + name="Research-Analysis-Implementation-Swarm", + description="Hierarchical swarm for comprehensive project execution", + agents=[research_agent, analysis_agent, implementation_agent], + max_loops=1, + ) + + # Verify initialization + assert swarm.name == "Research-Analysis-Implementation-Swarm" + assert ( + swarm.description + == "Hierarchical swarm for comprehensive project execution" + ) + assert len(swarm.agents) == 3 + assert swarm.max_loops == 1 + assert swarm.director is not None + + +def test_hierarchical_swarm_with_director(): + """Test HierarchicalSwarm with custom director""" + # Create a custom director + director = Agent( + agent_name="Project-Director", + agent_description="Senior project director with extensive experience", + model_name="gpt-4o", + max_loops=1, + ) + + # Create worker agents + developer = Agent( + agent_name="Senior-Developer", + agent_description="Senior software developer", + model_name="gpt-4o", + max_loops=1, + ) + + tester = Agent( + agent_name="QA-Lead", + agent_description="Quality assurance lead", + model_name="gpt-4o", + max_loops=1, + ) + + # Create swarm with custom director + swarm = HierarchicalSwarm( + name="Software-Development-Swarm", + description="Hierarchical swarm for software development projects", + director=director, + agents=[developer, tester], + max_loops=2, + ) + + assert swarm.director == director + assert len(swarm.agents) == 2 + assert swarm.max_loops == 2 + + +def test_hierarchical_swarm_execution(): + """Test HierarchicalSwarm execution with multiple agents""" + # Create specialized agents + market_researcher = Agent( + agent_name="Market-Researcher", + agent_description="Market research specialist", + model_name="gpt-4o", + max_loops=1, + ) + + product_strategist = Agent( + agent_name="Product-Strategist", + agent_description="Product strategy and planning expert", + model_name="gpt-4o", + max_loops=1, + ) + + technical_architect = Agent( + agent_name="Technical-Architect", + agent_description="Technical architecture and design specialist", + model_name="gpt-4o", + max_loops=1, + ) + + risk_analyst = Agent( + agent_name="Risk-Analyst", + agent_description="Risk assessment and mitigation specialist", + model_name="gpt-4o", + max_loops=1, + ) + + # Create hierarchical swarm + swarm = HierarchicalSwarm( + name="Product-Development-Swarm", + description="Comprehensive product development hierarchical swarm", + agents=[ + market_researcher, + product_strategist, + technical_architect, + risk_analyst, + ], + max_loops=1, + verbose=True, + ) + + # Execute swarm + result = swarm.run( + "Develop a comprehensive strategy for a new AI-powered healthcare platform" + ) + + # Verify result structure + assert result is not None + # HierarchicalSwarm returns a SwarmSpec or conversation history, just ensure it's not None + + +def test_hierarchical_swarm_multiple_loops(): + """Test HierarchicalSwarm with multiple feedback loops""" + # Create agents for iterative refinement + planner = Agent( + agent_name="Strategic-Planner", + agent_description="Strategic planning and project management", + model_name="gpt-4o", + max_loops=1, + ) + + executor = Agent( + agent_name="Task-Executor", + agent_description="Task execution and implementation", + model_name="gpt-4o", + max_loops=1, + ) + + reviewer = Agent( + agent_name="Quality-Reviewer", + agent_description="Quality assurance and review specialist", + model_name="gpt-4o", + max_loops=1, + ) + + # Create swarm with multiple loops for iterative refinement + swarm = HierarchicalSwarm( + name="Iterative-Development-Swarm", + description="Hierarchical swarm with iterative feedback loops", + agents=[planner, executor, reviewer], + max_loops=3, # Allow multiple iterations + verbose=True, + ) + + # Execute with multiple loops + result = swarm.run( + "Create a detailed project plan for implementing a machine learning recommendation system" + ) + + assert result is not None + + +def test_hierarchical_swarm_error_handling(): + """Test HierarchicalSwarm error handling""" + # Test with empty agents list + try: + swarm = HierarchicalSwarm(agents=[]) + assert ( + False + ), "Should have raised ValueError for empty agents list" + except ValueError as e: + assert "agents" in str(e).lower() or "empty" in str(e).lower() + + # Test with invalid max_loops + researcher = Agent( + agent_name="Test-Researcher", + agent_description="Test researcher", + model_name="gpt-4o", + max_loops=1, + ) + + try: + swarm = HierarchicalSwarm(agents=[researcher], max_loops=0) + assert ( + False + ), "Should have raised ValueError for invalid max_loops" + except ValueError as e: + assert "max_loops" in str(e).lower() or "0" in str(e) + + +def test_hierarchical_swarm_collaboration_prompts(): + """Test HierarchicalSwarm with collaboration prompts enabled""" + # Create agents + data_analyst = Agent( + agent_name="Data-Analyst", + agent_description="Data analysis specialist", + model_name="gpt-4o", + max_loops=1, + ) + + business_analyst = Agent( + agent_name="Business-Analyst", + agent_description="Business analysis specialist", + model_name="gpt-4o", + max_loops=1, + ) + + # Create swarm with collaboration prompts + swarm = HierarchicalSwarm( + name="Collaborative-Analysis-Swarm", + description="Hierarchical swarm with enhanced collaboration", + agents=[data_analyst, business_analyst], + max_loops=1, + add_collaboration_prompt=True, + ) + + # Check that collaboration prompts were added to agents + assert data_analyst.system_prompt is not None + assert business_analyst.system_prompt is not None + + # Execute swarm + result = swarm.run( + "Analyze customer behavior patterns and provide business recommendations" + ) + assert result is not None + + +def test_hierarchical_swarm_with_dashboard(): + """Test HierarchicalSwarm with interactive dashboard""" + # Create agents + content_creator = Agent( + agent_name="Content-Creator", + agent_description="Content creation specialist", + model_name="gpt-4o", + max_loops=1, + ) + + editor = Agent( + agent_name="Editor", + agent_description="Content editor and proofreader", + model_name="gpt-4o", + max_loops=1, + ) + + publisher = Agent( + agent_name="Publisher", + agent_description="Publishing and distribution specialist", + model_name="gpt-4o", + max_loops=1, + ) + + # Create swarm with interactive dashboard + swarm = HierarchicalSwarm( + name="Content-Publishing-Swarm", + description="Hierarchical swarm for content creation and publishing", + agents=[content_creator, editor, publisher], + max_loops=1, + interactive=True, + verbose=True, + ) + + # Verify dashboard was created + assert swarm.dashboard is not None + assert swarm.interactive is True + + # Execute swarm + result = swarm.run( + "Create a comprehensive guide on machine learning best practices" + ) + assert result is not None + + +def test_hierarchical_swarm_real_world_scenario(): + """Test HierarchicalSwarm in a realistic business scenario""" + # Create agents representing different business functions + market_intelligence = Agent( + agent_name="Market-Intelligence-Director", + agent_description="Director of market intelligence and competitive analysis", + model_name="gpt-4o", + max_loops=1, + ) + + product_strategy = Agent( + agent_name="Product-Strategy-Manager", + agent_description="Product strategy and roadmap manager", + model_name="gpt-4o", + max_loops=1, + ) + + engineering_lead = Agent( + agent_name="Engineering-Lead", + agent_description="Senior engineering lead and technical architect", + model_name="gpt-4o", + max_loops=1, + ) + + operations_manager = Agent( + agent_name="Operations-Manager", + agent_description="Operations and implementation manager", + model_name="gpt-4o", + max_loops=1, + ) + + compliance_officer = Agent( + agent_name="Compliance-Officer", + agent_description="Legal compliance and regulatory specialist", + model_name="gpt-4o", + max_loops=1, + ) + + # Create comprehensive hierarchical swarm + swarm = HierarchicalSwarm( + name="Enterprise-Strategy-Swarm", + description="Enterprise-level strategic planning and execution swarm", + agents=[ + market_intelligence, + product_strategy, + engineering_lead, + operations_manager, + compliance_officer, + ], + max_loops=2, + verbose=True, + add_collaboration_prompt=True, + ) + + # Test with complex enterprise scenario + result = swarm.run( + "Develop a comprehensive 5-year strategic plan for our company to become a leader in " + "AI-powered enterprise solutions. Consider market opportunities, competitive landscape, " + "technical requirements, operational capabilities, and regulatory compliance." + ) + + assert result is not None diff --git a/tests/test_comprehensive_test.py b/tests/structs/test_main_features.py similarity index 93% rename from tests/test_comprehensive_test.py rename to tests/structs/test_main_features.py index 7de5b8b3..ba5ca392 100644 --- a/tests/test_comprehensive_test.py +++ b/tests/structs/test_main_features.py @@ -23,11 +23,8 @@ from swarms.structs import ( from swarms.structs.hiearchical_swarm import HierarchicalSwarm from swarms.structs.tree_swarm import ForestSwarm, Tree, TreeAgent -# Load environment variables load_dotenv() -API_KEY = os.getenv("OPENAI_API_KEY") - def generate_timestamp() -> str: """Generate a timestamp string for filenames""" @@ -82,58 +79,6 @@ def write_markdown_report( f.write("---\n\n") -# def create_github_issue(test_result: Dict[str, Any]) -> Dict[str, Any]: -# """Create a GitHub issue for a failed test""" -# if not all([GITHUB_TOKEN, GITHUB_REPO_OWNER, GITHUB_REPO_NAME]): -# logger.warning("GitHub credentials not configured. Skipping issue creation.") -# return None - -# if test_result["status"] != "failed": -# return None - -# issue_title = f"Automated Test Failure: {test_result['test_name']}" - -# issue_body = f""" -# ## Test Failure Report - -# - **Test Name**: `{test_result['test_name']}` -# - **Timestamp**: `{datetime.now().isoformat()}` -# - **Status**: {test_result['status']} - -# ### Error Information -# ``` -# {test_result.get('error', 'No error message available')} -# ``` - -# ### Response (if available) -# ```json -# {json.dumps(test_result.get('response', {}), indent=2)} -# ``` - -# --- -# *This issue was automatically generated by the Swarms testing workflow.* -# """ - -# payload = { -# "title": issue_title, -# "body": issue_body, -# "labels": ["bug", "test-failure", "automated-report"], -# } - -# try: -# response = requests.post( -# f"{BASE_URL}/repos/{GITHUB_REPO_OWNER}/{GITHUB_REPO_NAME}/issues", -# headers=GITHUB_HEADERS, -# json=payload, -# ) -# response.raise_for_status() -# logger.info(f"Created GitHub issue for {test_result['test_name']}") -# return response.json() -# except requests.exceptions.RequestException as e: -# logger.error(f"Failed to create GitHub issue: {e.response.text if e.response else str(e)}") -# return None - - def create_test_agent( name: str, system_prompt: str = None, @@ -939,10 +884,4 @@ def run_all_tests(): if __name__ == "__main__": - if not API_KEY: - logger.error( - "OPENAI_API_KEY environment variable not set. Aborting tests." - ) - exit(1) - else: - run_all_tests() + run_all_tests() diff --git a/tests/structs/test_majority_voting.py b/tests/structs/test_majority_voting.py index 1ee8fea3..dccf8c18 100644 --- a/tests/structs/test_majority_voting.py +++ b/tests/structs/test_majority_voting.py @@ -1,152 +1,203 @@ -from unittest.mock import MagicMock - -import pytest - from swarms.structs.agent import Agent from swarms.structs.majority_voting import MajorityVoting -def test_majority_voting_run_concurrent(mocker): - # Create mock agents - agent1 = MagicMock(spec=Agent) - agent2 = MagicMock(spec=Agent) - agent3 = MagicMock(spec=Agent) +def test_majority_voting_basic_execution(): + """Test basic MajorityVoting execution with multiple agents""" + # Create specialized agents with different perspectives + geographer = Agent( + agent_name="Geography-Expert", + agent_description="Expert in geography and world capitals", + model_name="gpt-4o", + max_loops=1, + ) - # Create mock majority voting - mv = MajorityVoting( - agents=[agent1, agent2, agent3], - concurrent=True, - multithreaded=False, + historian = Agent( + agent_name="History-Scholar", + agent_description="Historical and cultural context specialist", + model_name="gpt-4o", + max_loops=1, ) - # Create mock conversation - conversation = MagicMock() - mv.conversation = conversation + political_analyst = Agent( + agent_name="Political-Analyst", + agent_description="Political and administrative specialist", + model_name="gpt-4o", + max_loops=1, + ) - # Create mock results - results = ["Paris", "Paris", "Lyon"] + # Create majority voting system + mv = MajorityVoting( + name="Geography-Consensus-System", + description="Majority voting system for geographical questions", + agents=[geographer, historian, political_analyst], + max_loops=1, + verbose=True, + ) - # Mock agent.run method - agent1.run.return_value = results[0] - agent2.run.return_value = results[1] - agent3.run.return_value = results[2] + # Test execution + result = mv.run("What is the capital city of France?") + assert result is not None - # Run majority voting - majority_vote = mv.run("What is the capital of France?") - # Assert agent.run method was called with the correct task - agent1.run.assert_called_once_with( - "What is the capital of France?" - ) - agent2.run.assert_called_once_with( - "What is the capital of France?" - ) - agent3.run.assert_called_once_with( - "What is the capital of France?" +def test_majority_voting_multiple_loops(): + """Test MajorityVoting with multiple loops for consensus refinement""" + # Create agents with different knowledge bases + trivia_expert = Agent( + agent_name="Trivia-Expert", + agent_description="General knowledge and trivia specialist", + model_name="gpt-4o", + max_loops=1, ) - # Assert conversation.add method was called with the correct responses - conversation.add.assert_any_call(agent1.agent_name, results[0]) - conversation.add.assert_any_call(agent2.agent_name, results[1]) - conversation.add.assert_any_call(agent3.agent_name, results[2]) - - # Assert majority vote is correct - assert majority_vote is not None - + research_analyst = Agent( + agent_name="Research-Analyst", + agent_description="Research and fact-checking specialist", + model_name="gpt-4o", + max_loops=1, + ) -def test_majority_voting_run_multithreaded(mocker): - # Create mock agents - agent1 = MagicMock(spec=Agent) - agent2 = MagicMock(spec=Agent) - agent3 = MagicMock(spec=Agent) + subject_matter_expert = Agent( + agent_name="Subject-Matter-Expert", + agent_description="Deep subject matter expertise specialist", + model_name="gpt-4o", + max_loops=1, + ) - # Create mock majority voting + # Create majority voting with multiple loops for iterative refinement mv = MajorityVoting( - agents=[agent1, agent2, agent3], - concurrent=False, - multithreaded=True, + name="Multi-Loop-Consensus-System", + description="Majority voting with iterative consensus refinement", + agents=[ + trivia_expert, + research_analyst, + subject_matter_expert, + ], + max_loops=3, # Allow multiple iterations + verbose=True, ) - # Create mock conversation - conversation = MagicMock() - mv.conversation = conversation - - # Create mock results - results = ["Paris", "Paris", "Lyon"] - - # Mock agent.run method - agent1.run.return_value = results[0] - agent2.run.return_value = results[1] - agent3.run.return_value = results[2] + # Test multi-loop execution + result = mv.run( + "What are the main causes of climate change and what can be done to mitigate them?" + ) + assert result is not None - # Run majority voting - majority_vote = mv.run("What is the capital of France?") - # Assert agent.run method was called with the correct task - agent1.run.assert_called_once_with( - "What is the capital of France?" - ) - agent2.run.assert_called_once_with( - "What is the capital of France?" - ) - agent3.run.assert_called_once_with( - "What is the capital of France?" +def test_majority_voting_business_scenario(): + """Test MajorityVoting in a realistic business scenario""" + # Create agents representing different business perspectives + market_strategist = Agent( + agent_name="Market-Strategist", + agent_description="Market strategy and competitive analysis specialist", + model_name="gpt-4o", + max_loops=1, ) - # Assert conversation.add method was called with the correct responses - conversation.add.assert_any_call(agent1.agent_name, results[0]) - conversation.add.assert_any_call(agent2.agent_name, results[1]) - conversation.add.assert_any_call(agent3.agent_name, results[2]) + financial_analyst = Agent( + agent_name="Financial-Analyst", + agent_description="Financial modeling and ROI analysis specialist", + model_name="gpt-4o", + max_loops=1, + ) - # Assert majority vote is correct - assert majority_vote is not None + technical_architect = Agent( + agent_name="Technical-Architect", + agent_description="Technical feasibility and implementation specialist", + model_name="gpt-4o", + max_loops=1, + ) + risk_manager = Agent( + agent_name="Risk-Manager", + agent_description="Risk assessment and compliance specialist", + model_name="gpt-4o", + max_loops=1, + ) -@pytest.mark.asyncio -async def test_majority_voting_run_asynchronous(mocker): - # Create mock agents - agent1 = MagicMock(spec=Agent) - agent2 = MagicMock(spec=Agent) - agent3 = MagicMock(spec=Agent) + operations_expert = Agent( + agent_name="Operations-Expert", + agent_description="Operations and implementation specialist", + model_name="gpt-4o", + max_loops=1, + ) - # Create mock majority voting + # Create majority voting for business decisions mv = MajorityVoting( - agents=[agent1, agent2, agent3], - concurrent=False, - multithreaded=False, - asynchronous=True, + name="Business-Decision-Consensus", + description="Majority voting system for business strategic decisions", + agents=[ + market_strategist, + financial_analyst, + technical_architect, + risk_manager, + operations_expert, + ], + max_loops=2, + verbose=True, ) - # Create mock conversation - conversation = MagicMock() - mv.conversation = conversation - - # Create mock results - results = ["Paris", "Paris", "Lyon"] + # Test with complex business decision + result = mv.run( + "Should our company invest in developing an AI-powered customer service platform? " + "Consider market demand, financial implications, technical feasibility, risk factors, " + "and operational requirements." + ) - # Mock agent.run method - agent1.run.return_value = results[0] - agent2.run.return_value = results[1] - agent3.run.return_value = results[2] + assert result is not None - # Run majority voting - majority_vote = await mv.run("What is the capital of France?") - # Assert agent.run method was called with the correct task - agent1.run.assert_called_once_with( - "What is the capital of France?" +def test_majority_voting_error_handling(): + """Test MajorityVoting error handling and validation""" + # Test with empty agents list + try: + mv = MajorityVoting(agents=[]) + assert ( + False + ), "Should have raised ValueError for empty agents list" + except ValueError as e: + assert "agents" in str(e).lower() or "empty" in str(e).lower() + + # Test with invalid max_loops + analyst = Agent( + agent_name="Test-Analyst", + agent_description="Test analyst", + model_name="gpt-4o", + max_loops=1, ) - agent2.run.assert_called_once_with( - "What is the capital of France?" + + try: + mv = MajorityVoting(agents=[analyst], max_loops=0) + assert ( + False + ), "Should have raised ValueError for invalid max_loops" + except ValueError as e: + assert "max_loops" in str(e).lower() or "0" in str(e) + + +def test_majority_voting_different_output_types(): + """Test MajorityVoting with different output types""" + # Create agents for technical analysis + security_expert = Agent( + agent_name="Security-Expert", + agent_description="Cybersecurity and data protection specialist", + model_name="gpt-4o", + max_loops=1, ) - agent3.run.assert_called_once_with( - "What is the capital of France?" + + compliance_officer = Agent( + agent_name="Compliance-Officer", + agent_description="Regulatory compliance and legal specialist", + model_name="gpt-4o", + max_loops=1, ) - # Assert conversation.add method was called with the correct responses - conversation.add.assert_any_call(agent1.agent_name, results[0]) - conversation.add.assert_any_call(agent2.agent_name, results[1]) - conversation.add.assert_any_call(agent3.agent_name, results[2]) + privacy_advocate = Agent( + agent_name="Privacy-Advocate", + agent_description="Privacy protection and data rights specialist", + model_name="gpt-4o", + max_loops=1, + ) # Assert majority vote is correct assert majority_vote is not None diff --git a/tests/structs/test_moa.py b/tests/structs/test_moa.py index 453c7fd5..1d46d9b2 100644 --- a/tests/structs/test_moa.py +++ b/tests/structs/test_moa.py @@ -1,84 +1,268 @@ -import pytest -from unittest.mock import Mock, patch from swarms.structs.mixture_of_agents import MixtureOfAgents from swarms.structs.agent import Agent -from swarms_memory import BaseVectorDatabase - - -def test_init(): - with patch.object( - MixtureOfAgents, "agent_check" - ) as mock_agent_check, patch.object( - MixtureOfAgents, "final_agent_check" - ) as mock_final_agent_check, patch.object( - MixtureOfAgents, "swarm_initialization" - ) as mock_swarm_initialization, patch.object( - MixtureOfAgents, "communication_protocol" - ) as mock_communication_protocol: - agents = [Mock(spec=Agent)] - final_agent = Mock(spec=Agent) - scp = Mock(spec=BaseVectorDatabase) - MixtureOfAgents( - agents=agents, final_agent=final_agent, scp=scp - ) - mock_agent_check.assert_called_once() - mock_final_agent_check.assert_called_once() - mock_swarm_initialization.assert_called_once() - mock_communication_protocol.assert_called_once() - - -def test_communication_protocol(): - agents = [Mock(spec=Agent)] - final_agent = Mock(spec=Agent) - scp = Mock(spec=BaseVectorDatabase) - swarm = MixtureOfAgents( - agents=agents, final_agent=final_agent, scp=scp - ) - swarm.communication_protocol() - for agent in agents: - agent.long_term_memory.assert_called_once_with(scp) - - -def test_agent_check(): - final_agent = Mock(spec=Agent) - with pytest.raises(TypeError): - MixtureOfAgents(agents="not a list", final_agent=final_agent) - with pytest.raises(TypeError): - MixtureOfAgents( - agents=["not an agent"], final_agent=final_agent - ) -def test_final_agent_check(): - agents = [Mock(spec=Agent)] - with pytest.raises(TypeError): - MixtureOfAgents(agents=agents, final_agent="not an agent") +def test_mixture_of_agents_basic_initialization(): + """Test basic MixtureOfAgents initialization with multiple agents""" + # Create multiple specialized agents + research_agent = Agent( + agent_name="Research-Specialist", + agent_description="Specialist in research and data collection", + model_name="gpt-4o", + max_loops=1, + ) + + analysis_agent = Agent( + agent_name="Analysis-Expert", + agent_description="Expert in data analysis and insights", + model_name="gpt-4o", + max_loops=1, + ) + strategy_agent = Agent( + agent_name="Strategy-Consultant", + agent_description="Strategy and planning consultant", + model_name="gpt-4o", + max_loops=1, + ) -def test_swarm_initialization(): - with patch( - "swarms.structs.mixture_of_agents.logger" - ) as mock_logger: - agents = [Mock(spec=Agent)] - final_agent = Mock(spec=Agent) - swarm = MixtureOfAgents( - agents=agents, final_agent=final_agent - ) - swarm.swarm_initialization() - assert mock_logger.info.call_count == 3 - - -def test_run(): - with patch("swarms.structs.mixture_of_agents.logger"), patch( - "builtins.open", new_callable=Mock - ) as mock_open: - agents = [Mock(spec=Agent)] - final_agent = Mock(spec=Agent) - swarm = MixtureOfAgents( - agents=agents, final_agent=final_agent + # Create aggregator agent + aggregator = Agent( + agent_name="Aggregator-Agent", + agent_description="Agent that aggregates responses from other agents", + model_name="gpt-4o", + max_loops=1, + ) + + # Create mixture of agents + moa = MixtureOfAgents( + name="Business-Analysis-Mixture", + description="Mixture of agents for comprehensive business analysis", + agents=[research_agent, analysis_agent, strategy_agent], + aggregator_agent=aggregator, + layers=3, + max_loops=1, + ) + + # Verify initialization + assert moa.name == "Business-Analysis-Mixture" + assert ( + moa.description + == "Mixture of agents for comprehensive business analysis" + ) + assert len(moa.agents) == 3 + assert moa.aggregator_agent == aggregator + assert moa.layers == 3 + assert moa.max_loops == 1 + + +def test_mixture_of_agents_execution(): + """Test MixtureOfAgents execution with multiple agents""" + # Create diverse agents for different perspectives + market_analyst = Agent( + agent_name="Market-Analyst", + agent_description="Market analysis and trend specialist", + model_name="gpt-4o", + max_loops=1, + ) + + technical_expert = Agent( + agent_name="Technical-Expert", + agent_description="Technical feasibility and implementation specialist", + model_name="gpt-4o", + max_loops=1, + ) + + financial_analyst = Agent( + agent_name="Financial-Analyst", + agent_description="Financial modeling and ROI specialist", + model_name="gpt-4o", + max_loops=1, + ) + + risk_assessor = Agent( + agent_name="Risk-Assessor", + agent_description="Risk assessment and mitigation specialist", + model_name="gpt-4o", + max_loops=1, + ) + + # Create aggregator for synthesis + aggregator = Agent( + agent_name="Executive-Summary-Agent", + agent_description="Executive summary and recommendation specialist", + model_name="gpt-4o", + max_loops=1, + ) + + # Create mixture of agents + moa = MixtureOfAgents( + name="Comprehensive-Evaluation-Mixture", + description="Mixture of agents for comprehensive business evaluation", + agents=[ + market_analyst, + technical_expert, + financial_analyst, + risk_assessor, + ], + aggregator_agent=aggregator, + layers=2, + max_loops=1, + ) + + # Test execution + result = moa.run( + "Evaluate the feasibility of launching an AI-powered healthcare platform" + ) + assert result is not None + + +def test_mixture_of_agents_multiple_layers(): + """Test MixtureOfAgents with multiple layers""" + # Create agents for layered analysis + data_collector = Agent( + agent_name="Data-Collector", + agent_description="Data collection and research specialist", + model_name="gpt-4o", + max_loops=1, + ) + + pattern_analyzer = Agent( + agent_name="Pattern-Analyzer", + agent_description="Pattern recognition and analysis specialist", + model_name="gpt-4o", + max_loops=1, + ) + + insight_generator = Agent( + agent_name="Insight-Generator", + agent_description="Insight generation and interpretation specialist", + model_name="gpt-4o", + max_loops=1, + ) + + # Create aggregator + final_aggregator = Agent( + agent_name="Final-Aggregator", + agent_description="Final aggregation and conclusion specialist", + model_name="gpt-4o", + max_loops=1, + ) + + # Create mixture with multiple layers for deeper analysis + moa = MixtureOfAgents( + name="Multi-Layer-Analysis-Mixture", + description="Mixture of agents with multiple analysis layers", + agents=[data_collector, pattern_analyzer, insight_generator], + aggregator_agent=final_aggregator, + layers=4, + max_loops=1, + ) + + # Test multi-layer execution + result = moa.run( + "Analyze customer behavior patterns and provide strategic insights" + ) + assert result is not None + + +def test_mixture_of_agents_error_handling(): + """Test MixtureOfAgents error handling and validation""" + # Test with empty agents list + try: + moa = MixtureOfAgents(agents=[]) + assert ( + False + ), "Should have raised ValueError for empty agents list" + except ValueError as e: + assert "No agents provided" in str(e) + + # Test with invalid aggregator system prompt + analyst = Agent( + agent_name="Test-Analyst", + agent_description="Test analyst", + model_name="gpt-4o", + max_loops=1, + ) + + try: + moa = MixtureOfAgents( + agents=[analyst], aggregator_system_prompt="" ) - swarm.run("task") - for agent in agents: - agent.run.assert_called_once() - final_agent.run.assert_called_once() - mock_open.assert_called_once_with(swarm.saved_file_name, "w") + assert ( + False + ), "Should have raised ValueError for empty system prompt" + except ValueError as e: + assert "No aggregator system prompt" in str(e) + + +def test_mixture_of_agents_real_world_scenario(): + """Test MixtureOfAgents in a realistic business scenario""" + # Create agents representing different business functions + marketing_director = Agent( + agent_name="Marketing-Director", + agent_description="Senior marketing director with market expertise", + model_name="gpt-4o", + max_loops=1, + ) + + product_manager = Agent( + agent_name="Product-Manager", + agent_description="Product strategy and development manager", + model_name="gpt-4o", + max_loops=1, + ) + + engineering_lead = Agent( + agent_name="Engineering-Lead", + agent_description="Senior engineering and technical architecture lead", + model_name="gpt-4o", + max_loops=1, + ) + + sales_executive = Agent( + agent_name="Sales-Executive", + agent_description="Enterprise sales and customer relationship executive", + model_name="gpt-4o", + max_loops=1, + ) + + legal_counsel = Agent( + agent_name="Legal-Counsel", + agent_description="Legal compliance and regulatory counsel", + model_name="gpt-4o", + max_loops=1, + ) + + # Create aggregator for executive decision making + executive_aggregator = Agent( + agent_name="Executive-Decision-Maker", + agent_description="Executive decision maker and strategic aggregator", + model_name="gpt-4o", + max_loops=1, + ) + + # Create comprehensive mixture of agents + moa = MixtureOfAgents( + name="Executive-Board-Mixture", + description="Mixture of agents representing executive board for strategic decisions", + agents=[ + marketing_director, + product_manager, + engineering_lead, + sales_executive, + legal_counsel, + ], + aggregator_agent=executive_aggregator, + layers=3, + max_loops=1, + ) + + # Test with complex business scenario + result = moa.run( + "Develop a comprehensive go-to-market strategy for our new AI-powered enterprise platform. " + "Consider market positioning, technical requirements, competitive landscape, sales channels, " + "and legal compliance requirements." + ) + + assert result is not None diff --git a/tests/structs/test_multi_agent_collab.py b/tests/structs/test_multi_agent_collab.py deleted file mode 100644 index 6e97b479..00000000 --- a/tests/structs/test_multi_agent_collab.py +++ /dev/null @@ -1,201 +0,0 @@ -import json -import os -from unittest.mock import Mock - -import pytest - -from swarms import Agent -from swarm_models import OpenAIChat -from experimental.multi_agent_collab import MultiAgentCollaboration - -# Initialize the director agent - -director = Agent( - agent_name="Director", - system_prompt="Directs the tasks for the workers", - llm=OpenAIChat(), - max_loops=1, - dashboard=False, - streaming_on=True, - verbose=True, - stopping_token="", - state_save_file_type="json", - saved_state_path="director.json", -) - - -# Initialize worker 1 - -worker1 = Agent( - agent_name="Worker1", - system_prompt="Generates a transcript for a youtube video on what swarms are", - llm=OpenAIChat(), - max_loops=1, - dashboard=False, - streaming_on=True, - verbose=True, - stopping_token="", - state_save_file_type="json", - saved_state_path="worker1.json", -) - - -# Initialize worker 2 -worker2 = Agent( - agent_name="Worker2", - system_prompt="Summarizes the transcript generated by Worker1", - llm=OpenAIChat(), - max_loops=1, - dashboard=False, - streaming_on=True, - verbose=True, - stopping_token="", - state_save_file_type="json", - saved_state_path="worker2.json", -) - - -# Create a list of agents -agents = [director, worker1, worker2] - - -@pytest.fixture -def collaboration(): - return MultiAgentCollaboration(agents) - - -def test_collaboration_initialization(collaboration): - assert len(collaboration.agents) == 2 - assert callable(collaboration.select_next_speaker) - assert collaboration.max_loops == 10 - assert collaboration.results == [] - assert collaboration.logging is True - - -def test_reset(collaboration): - collaboration.reset() - for agent in collaboration.agents: - assert agent.step == 0 - - -def test_inject(collaboration): - collaboration.inject("TestName", "TestMessage") - for agent in collaboration.agents: - assert "TestName" in agent.history[-1] - assert "TestMessage" in agent.history[-1] - - -def test_inject_agent(collaboration): - agent3 = Agent(llm=OpenAIChat(), max_loops=2) - collaboration.inject_agent(agent3) - assert len(collaboration.agents) == 3 - assert agent3 in collaboration.agents - - -def test_step(collaboration): - collaboration.step() - for agent in collaboration.agents: - assert agent.step == 1 - - -def test_ask_for_bid(collaboration): - agent = Mock() - agent.bid.return_value = "<5>" - bid = collaboration.ask_for_bid(agent) - assert bid == 5 - - -def test_select_next_speaker(collaboration): - collaboration.select_next_speaker = Mock(return_value=0) - idx = collaboration.select_next_speaker(1, collaboration.agents) - assert idx == 0 - - -def test_run(collaboration): - collaboration.run() - for agent in collaboration.agents: - assert agent.step == collaboration.max_loops - - -def test_format_results(collaboration): - collaboration.results = [ - {"agent": "Agent1", "response": "Response1"} - ] - formatted_results = collaboration.format_results( - collaboration.results - ) - assert "Agent1 responded: Response1" in formatted_results - - -def test_save_and_load(collaboration): - collaboration.save() - loaded_state = collaboration.load() - assert loaded_state["_step"] == collaboration._step - assert loaded_state["results"] == collaboration.results - - -def test_performance(collaboration): - performance_data = collaboration.performance() - for agent in collaboration.agents: - assert agent.name in performance_data - assert "metrics" in performance_data[agent.name] - - -def test_set_interaction_rules(collaboration): - rules = {"rule1": "action1", "rule2": "action2"} - collaboration.set_interaction_rules(rules) - assert hasattr(collaboration, "interaction_rules") - assert collaboration.interaction_rules == rules - - -def test_repr(collaboration): - repr_str = repr(collaboration) - assert isinstance(repr_str, str) - assert "MultiAgentCollaboration" in repr_str - - -def test_load(collaboration): - state = { - "step": 5, - "results": [{"agent": "Agent1", "response": "Response1"}], - } - with open(collaboration.saved_file_path_name, "w") as file: - json.dump(state, file) - - loaded_state = collaboration.load() - assert loaded_state["_step"] == state["step"] - assert loaded_state["results"] == state["results"] - - -def test_save(collaboration, tmp_path): - collaboration.saved_file_path_name = tmp_path / "test_save.json" - collaboration.save() - - with open(collaboration.saved_file_path_name) as file: - saved_data = json.load(file) - - assert saved_data["_step"] == collaboration._step - assert saved_data["results"] == collaboration.results - - -# Add more tests here... - -# Add more parameterized tests for different scenarios... - - -# Example of exception testing -def test_exception_handling(collaboration): - agent = Mock() - agent.bid.side_effect = ValueError("Invalid bid") - with pytest.raises(ValueError): - collaboration.ask_for_bid(agent) - - -# Add more exception testing... - - -# Example of environment variable testing (if applicable) -@pytest.mark.parametrize("env_var", ["ENV_VAR_1", "ENV_VAR_2"]) -def test_environment_variables(collaboration, monkeypatch, env_var): - monkeypatch.setenv(env_var, "test_value") - assert os.getenv(env_var) == "test_value" diff --git a/tests/structs/test_multi_agent_router.py b/tests/structs/test_multi_agent_router.py new file mode 100644 index 00000000..5f96a8d9 --- /dev/null +++ b/tests/structs/test_multi_agent_router.py @@ -0,0 +1,353 @@ +import pytest + +from swarms.structs.agent import Agent +from swarms.structs.multi_agent_router import MultiAgentRouter + + +# Test fixtures +def real_agents(): + """Create real agents for testing""" + return [ + Agent( + agent_name="ResearchAgent", + agent_description="Specializes in researching topics and providing detailed, factual information", + system_prompt="You are a research specialist. Provide detailed, well-researched information about any topic, citing sources when possible.", + model_name="gpt-4o-mini", + max_loops=1, + verbose=False, + print_on=False, + ), + Agent( + agent_name="CodeExpertAgent", + agent_description="Expert in writing, reviewing, and explaining code across multiple programming languages", + system_prompt="You are a coding expert. Write, review, and explain code with a focus on best practices and clean code principles.", + model_name="gpt-4o-mini", + max_loops=1, + verbose=False, + print_on=False, + ), + Agent( + agent_name="WritingAgent", + agent_description="Skilled in creative and technical writing, content creation, and editing", + system_prompt="You are a writing specialist. Create, edit, and improve written content while maintaining appropriate tone and style.", + model_name="gpt-4o-mini", + max_loops=1, + verbose=False, + print_on=False, + ), + Agent( + agent_name="MathAgent", + agent_description="Expert in mathematical calculations and problem solving", + system_prompt="You are a math expert. Solve mathematical problems and explain solutions clearly.", + model_name="gpt-4o-mini", + max_loops=1, + verbose=False, + print_on=False, + ), + ] + + +# ============================================================================ +# INITIALIZATION TESTS +# ============================================================================ + + +def test_multi_agent_router_initialization_default(): + """Test MultiAgentRouter initialization with default parameters""" + router = MultiAgentRouter(agents=real_agents()) + + assert router.name == "swarm-router" + assert ( + router.description + == "Routes tasks to specialized agents based on their capabilities" + ) + assert router.model == "gpt-4o-mini" + assert router.temperature == 0.1 + assert router.output_type == "dict" + assert router.print_on is True + assert router.skip_null_tasks is True + assert len(router.agents) == 4 + assert all( + agent_name in router.agents + for agent_name in [ + "ResearchAgent", + "CodeExpertAgent", + "WritingAgent", + "MathAgent", + ] + ) + assert isinstance( + router.conversation, object + ) # Conversation object + assert hasattr(router.function_caller, "run") + + +def test_multi_agent_router_initialization_custom_params(): + """Test MultiAgentRouter initialization with custom parameters""" + custom_name = "custom-router" + custom_description = "Custom description" + custom_model = "gpt-4" + custom_temperature = 0.5 + custom_output_type = "json" + + router = MultiAgentRouter( + name=custom_name, + description=custom_description, + agents=real_agents(), + model=custom_model, + temperature=custom_temperature, + output_type=custom_output_type, + print_on=False, + skip_null_tasks=False, + system_prompt="Custom system prompt", + ) + + assert router.name == custom_name + assert router.description == custom_description + assert router.model == custom_model + assert router.temperature == custom_temperature + assert router.output_type == custom_output_type + assert router.print_on is False + assert router.skip_null_tasks is False + assert router.system_prompt == "Custom system prompt" + + +def test_multi_agent_router_repr(): + """Test MultiAgentRouter string representation""" + router = MultiAgentRouter(agents=real_agents()) + + expected_repr = f"MultiAgentRouter(name={router.name}, agents={list(router.agents.keys())})" + assert repr(router) == expected_repr + + +# ============================================================================ +# SINGLE HANDOFF TESTS +# ============================================================================ + + +def test_handle_single_handoff_valid(): + """Test handling single handoff with valid agent""" + router = MultiAgentRouter(agents=real_agents()) + + result = router.route_task("Write a fibonacci function") + + # Check that conversation was updated + assert len(router.conversation.conversation_history) > 0 + # Check that we got a valid response + assert result is not None + assert isinstance(result, (list, dict)) + + +# ============================================================================ +# MULTIPLE HANDOFF TESTS +# ============================================================================ + + +def test_handle_multiple_handoffs_valid(): + """Test handling multiple handoffs with valid agents""" + router = MultiAgentRouter(agents=real_agents()) + + result = router.route_task("Research and implement fibonacci") + + # Check that conversation was updated + history = router.conversation.conversation_history + + assert len(history) > 0 + assert result is not None + assert isinstance(result, (list, dict)) + + +def test_handle_multiple_handoffs_with_null_tasks(): + """Test handling multiple handoffs with some null tasks""" + router = MultiAgentRouter( + agents=real_agents(), skip_null_tasks=True + ) + + result = router.route_task("Mixed task") + + # Should still return a valid result + history = router.conversation.conversation_history + assert len(history) > 0 + assert result is not None + assert isinstance(result, (list, dict)) + + +# ============================================================================ +# ROUTE TASK TESTS +# ============================================================================ + + +def test_route_task_single_agent(): + """Test route_task with single agent routing""" + router = MultiAgentRouter(agents=real_agents()) + + result = router.route_task("Write a fibonacci function") + + # Check result structure - should be a list of conversation messages + assert result is not None + assert isinstance(result, (list, dict)) + assert len(result) > 0 if isinstance(result, list) else True + + +def test_route_task_multiple_agents(): + """Test route_task with multiple agent routing""" + router = MultiAgentRouter(agents=real_agents()) + + result = router.route_task("Research and implement fibonacci") + + # Check result structure + assert result is not None + assert isinstance(result, (list, dict)) + + +def test_route_task_print_on_true(): + """Test route_task with print_on=True""" + router = MultiAgentRouter(agents=real_agents(), print_on=True) + + # Should not raise any exceptions when printing + result = router.route_task("Test task") + assert result is not None + assert isinstance(result, (list, dict)) + + +def test_route_task_print_on_false(): + """Test route_task with print_on=False""" + router = MultiAgentRouter(agents=real_agents(), print_on=False) + + # Should not raise any exceptions when not printing + result = router.route_task("Test task") + assert result is not None + assert isinstance(result, (list, dict)) + + +# ============================================================================ +# ALIAS METHOD TESTS +# ============================================================================ + + +def test_run_alias(): + """Test that run() method is an alias for route_task()""" + router = MultiAgentRouter(agents=real_agents()) + + result1 = router.run( + "Call your favorite agent to write a fibonacci function" + ) + result2 = router.route_task( + "Call your favorite agent to write a fibonacci function" + ) + + # Results should be valid + assert result1 is not None + assert result2 is not None + assert isinstance(result1, (list, dict)) + assert isinstance(result2, (list, dict)) + + +def test_call_alias(): + """Test that __call__() method is an alias for route_task()""" + router = MultiAgentRouter(agents=real_agents()) + + result1 = router( + "Call your favorite agent to write a fibonacci function" + ) + result2 = router( + "Call your favorite agent to write a fibonacci function" + ) + + # Results should be valid + assert result1 is not None + assert result2 is not None + assert isinstance(result1, (list, dict)) + assert isinstance(result2, (list, dict)) + + +# ============================================================================ +# BATCH PROCESSING TESTS +# ============================================================================ + + +def test_batch_run(): + """Test batch_run method""" + router = MultiAgentRouter(agents=real_agents()) + + tasks = [ + "Call your favorite agent to write a fibonacci function", + "Call your favorite agent to write a fibonacci function", + "Call your favorite agent to write a fibonacci function", + ] + results = router.batch_run(tasks) + + assert len(results) == 3 + assert all(result is not None for result in results) + assert all(isinstance(result, (list, dict)) for result in results) + + +def test_concurrent_batch_run(): + """Test concurrent_batch_run method""" + router = MultiAgentRouter(agents=real_agents()) + + tasks = [ + "Call your favorite agent to write a fibonacci function", + "Call your favorite agent to write a fibonacci function", + "Call your favorite agent to write a fibonacci function", + ] + results = router.concurrent_batch_run(tasks) + + assert len(results) == 3 + assert all(result is not None for result in results) + assert all(isinstance(result, (list, dict)) for result in results) + + +# ============================================================================ +# OUTPUT TYPE TESTS +# ============================================================================ + + +@pytest.mark.parametrize("output_type", ["dict", "json", "string"]) +def test_different_output_types(output_type): + """Test different output types""" + router = MultiAgentRouter( + agents=real_agents(), output_type=output_type + ) + + result = router.route_task("Test task") + + assert result is not None + # Output format depends on the formatter, but should not raise errors + assert isinstance(result, (list, dict, str)) + + +# ============================================================================ +# PERFORMANCE AND LOAD TESTS +# ============================================================================ + + +def test_large_batch_processing(): + """Test processing a large batch of tasks""" + router = MultiAgentRouter(agents=real_agents()) + + # Create a smaller number of tasks for testing (reduced from 100 to 5 for performance) + tasks = [f"Task number {i}" for i in range(5)] + results = router.batch_run(tasks) + + assert len(results) == 5 + + +def test_concurrent_large_batch_processing(): + """Test concurrent processing of a large batch of tasks""" + router = MultiAgentRouter(agents=real_agents()) + + # Create a small number of tasks for testing + tasks = [ + f"Route task to your favorite agent to write a fibonacci function {i}" + for i in range(3) + ] + results = router.concurrent_batch_run(tasks) + + assert len(results) == 3 + assert all(result is not None for result in results) + assert all(isinstance(result, (list, dict)) for result in results) + + +if __name__ == "__main__": + pytest.main([__file__]) diff --git a/tests/structs/test_reasoning_agent_router.py b/tests/structs/test_reasoning_agent_router.py new file mode 100644 index 00000000..cc1d496a --- /dev/null +++ b/tests/structs/test_reasoning_agent_router.py @@ -0,0 +1,1058 @@ +import sys + +from loguru import logger + +from swarms.agents.reasoning_agents import ( + ReasoningAgentInitializationError, + ReasoningAgentRouter, +) + + +def test_router_initialization(): + """ + Test ReasoningAgentRouter initialization with various configurations. + + Tests: + - Default initialization + - Custom parameter initialization + - All agent types initialization + """ + logger.info("Starting router initialization tests...") + + # Test 1: Default initialization + logger.info("Test 1: Default initialization") + try: + router = ReasoningAgentRouter() + assert router is not None, "Default router should not be None" + assert router.agent_name == "reasoning_agent", f"Expected 'reasoning_agent', got {router.agent_name}" + assert router.swarm_type == "reasoning-duo", f"Expected 'reasoning-duo', got {router.swarm_type}" + assert router.model_name == "gpt-4o-mini", f"Expected 'gpt-4o-mini', got {router.model_name}" + logger.success("āœ“ Default initialization test passed") + except Exception as e: + logger.error(f"āœ— Default initialization test failed: {e}") + raise + + # Test 2: Custom parameters initialization + logger.info("Test 2: Custom parameters initialization") + try: + custom_router = ReasoningAgentRouter( + agent_name="test_agent", + description="Test agent for unit testing", + model_name="gpt-4", + system_prompt="You are a test agent.", + max_loops=5, + swarm_type="self-consistency", + num_samples=3, + output_type="dict-all-except-first", + num_knowledge_items=10, + memory_capacity=20, + eval=True, + random_models_on=True, + majority_voting_prompt="Custom voting prompt", + reasoning_model_name="claude-3-5-sonnet-20240620" + ) + assert custom_router is not None, "Custom router should not be None" + assert custom_router.agent_name == "test_agent", f"Expected 'test_agent', got {custom_router.agent_name}" + assert custom_router.swarm_type == "self-consistency", f"Expected 'self-consistency', got {custom_router.swarm_type}" + assert custom_router.max_loops == 5, f"Expected 5, got {custom_router.max_loops}" + assert custom_router.num_samples == 3, f"Expected 3, got {custom_router.num_samples}" + logger.success("āœ“ Custom parameters initialization test passed") + except Exception as e: + logger.error(f"āœ— Custom parameters initialization test failed: {e}") + raise + + # Test 3: All agent types initialization + logger.info("Test 3: All agent types initialization") + agent_types = [ + "reasoning-duo", + "reasoning-agent", + "self-consistency", + "consistency-agent", + "ire", + "ire-agent", + "ReflexionAgent", + "GKPAgent", + "AgentJudge" + ] + + for agent_type in agent_types: + try: + router = ReasoningAgentRouter(swarm_type=agent_type) + assert router is not None, f"Router for {agent_type} should not be None" + assert router.swarm_type == agent_type, f"Expected {agent_type}, got {router.swarm_type}" + logger.info(f"āœ“ {agent_type} initialization successful") + except Exception as e: + logger.error(f"āœ— {agent_type} initialization failed: {e}") + raise + + logger.success("āœ“ All router initialization tests passed") + + +def test_reliability_check(): + """ + Test reliability_check method with various invalid configurations. + + Tests: + - Zero max_loops + - Empty model_name + - Empty swarm_type + - None model_name + - None swarm_type + """ + logger.info("Starting reliability check tests...") + + # Test 1: Zero max_loops + logger.info("Test 1: Zero max_loops should raise error") + try: + ReasoningAgentRouter(max_loops=0) + assert False, "Should have raised ReasoningAgentInitializationError" + except ReasoningAgentInitializationError as e: + assert "Max loops must be greater than 0" in str(e), f"Expected max loops error, got: {e}" + logger.success("āœ“ Zero max_loops error handling test passed") + except Exception as e: + logger.error(f"āœ— Zero max_loops test failed with unexpected error: {e}") + raise + + # Test 2: Empty model_name + logger.info("Test 2: Empty model_name should raise error") + try: + ReasoningAgentRouter(model_name="") + assert False, "Should have raised ReasoningAgentInitializationError" + except ReasoningAgentInitializationError as e: + assert "Model name must be provided" in str(e), f"Expected model name error, got: {e}" + logger.success("āœ“ Empty model_name error handling test passed") + except Exception as e: + logger.error(f"āœ— Empty model_name test failed with unexpected error: {e}") + raise + + # Test 3: None model_name + logger.info("Test 3: None model_name should raise error") + try: + ReasoningAgentRouter(model_name=None) + assert False, "Should have raised ReasoningAgentInitializationError" + except ReasoningAgentInitializationError as e: + assert "Model name must be provided" in str(e), f"Expected model name error, got: {e}" + logger.success("āœ“ None model_name error handling test passed") + except Exception as e: + logger.error(f"āœ— None model_name test failed with unexpected error: {e}") + raise + + # Test 4: Empty swarm_type + logger.info("Test 4: Empty swarm_type should raise error") + try: + ReasoningAgentRouter(swarm_type="") + assert False, "Should have raised ReasoningAgentInitializationError" + except ReasoningAgentInitializationError as e: + assert "Swarm type must be provided" in str(e), f"Expected swarm type error, got: {e}" + logger.success("āœ“ Empty swarm_type error handling test passed") + except Exception as e: + logger.error(f"āœ— Empty swarm_type test failed with unexpected error: {e}") + raise + + # Test 5: None swarm_type + logger.info("Test 5: None swarm_type should raise error") + try: + ReasoningAgentRouter(swarm_type=None) + assert False, "Should have raised ReasoningAgentInitializationError" + except ReasoningAgentInitializationError as e: + assert "Swarm type must be provided" in str(e), f"Expected swarm type error, got: {e}" + logger.success("āœ“ None swarm_type error handling test passed") + except Exception as e: + logger.error(f"āœ— None swarm_type test failed with unexpected error: {e}") + raise + + logger.success("āœ“ All reliability check tests passed") + + +def test_agent_factories(): + """ + Test all agent factory methods for each agent type. + + Tests: + - _create_reasoning_duo + - _create_consistency_agent + - _create_ire_agent + - _create_agent_judge + - _create_reflexion_agent + - _create_gkp_agent + """ + logger.info("Starting agent factory tests...") + + # Test configuration + test_config = { + "agent_name": "test_agent", + "description": "Test agent", + "model_name": "gpt-4o-mini", + "system_prompt": "Test prompt", + "max_loops": 2, + "num_samples": 3, + "output_type": "dict-all-except-first", + "num_knowledge_items": 5, + "memory_capacity": 10, + "eval": False, + "random_models_on": False, + "majority_voting_prompt": None, + "reasoning_model_name": "claude-3-5-sonnet-20240620" + } + + # Test 1: Reasoning Duo factory + logger.info("Test 1: _create_reasoning_duo") + try: + router = ReasoningAgentRouter(swarm_type="reasoning-duo", **test_config) + agent = router._create_reasoning_duo() + assert agent is not None, "Reasoning duo agent should not be None" + logger.success("āœ“ _create_reasoning_duo test passed") + except Exception as e: + logger.error(f"āœ— _create_reasoning_duo test failed: {e}") + raise + + # Test 2: Consistency Agent factory + logger.info("Test 2: _create_consistency_agent") + try: + router = ReasoningAgentRouter(swarm_type="self-consistency", **test_config) + agent = router._create_consistency_agent() + assert agent is not None, "Consistency agent should not be None" + logger.success("āœ“ _create_consistency_agent test passed") + except Exception as e: + logger.error(f"āœ— _create_consistency_agent test failed: {e}") + raise + + # Test 3: IRE Agent factory + logger.info("Test 3: _create_ire_agent") + try: + router = ReasoningAgentRouter(swarm_type="ire", **test_config) + agent = router._create_ire_agent() + assert agent is not None, "IRE agent should not be None" + logger.success("āœ“ _create_ire_agent test passed") + except Exception as e: + logger.error(f"āœ— _create_ire_agent test failed: {e}") + raise + + # Test 4: Agent Judge factory + logger.info("Test 4: _create_agent_judge") + try: + router = ReasoningAgentRouter(swarm_type="AgentJudge", **test_config) + agent = router._create_agent_judge() + assert agent is not None, "Agent judge should not be None" + logger.success("āœ“ _create_agent_judge test passed") + except Exception as e: + logger.error(f"āœ— _create_agent_judge test failed: {e}") + raise + + # Test 5: Reflexion Agent factory + logger.info("Test 5: _create_reflexion_agent") + try: + router = ReasoningAgentRouter(swarm_type="ReflexionAgent", **test_config) + agent = router._create_reflexion_agent() + assert agent is not None, "Reflexion agent should not be None" + logger.success("āœ“ _create_reflexion_agent test passed") + except Exception as e: + logger.error(f"āœ— _create_reflexion_agent test failed: {e}") + raise + + # Test 6: GKP Agent factory + logger.info("Test 6: _create_gkp_agent") + try: + router = ReasoningAgentRouter(swarm_type="GKPAgent", **test_config) + agent = router._create_gkp_agent() + assert agent is not None, "GKP agent should not be None" + logger.success("āœ“ _create_gkp_agent test passed") + except Exception as e: + logger.error(f"āœ— _create_gkp_agent test failed: {e}") + raise + + logger.success("āœ“ All agent factory tests passed") + + +def test_select_swarm(): + """ + Test select_swarm method for all supported agent types. + + Tests: + - All valid agent types + - Invalid agent type + """ + logger.info("Starting select_swarm tests...") + + agent_types = [ + "reasoning-duo", + "reasoning-agent", + "self-consistency", + "consistency-agent", + "ire", + "ire-agent", + "ReflexionAgent", + "GKPAgent", + "AgentJudge" + ] + + # Test all valid agent types + for agent_type in agent_types: + logger.info(f"Test: select_swarm for {agent_type}") + try: + router = ReasoningAgentRouter(swarm_type=agent_type) + swarm = router.select_swarm() + assert swarm is not None, f"Swarm for {agent_type} should not be None" + logger.success(f"āœ“ select_swarm for {agent_type} test passed") + except Exception as e: + logger.error(f"āœ— select_swarm for {agent_type} test failed: {e}") + raise + + # Test invalid agent type + logger.info("Test: Invalid agent type should raise error") + try: + router = ReasoningAgentRouter(swarm_type="invalid_type") + swarm = router.select_swarm() + assert False, "Should have raised ReasoningAgentInitializationError" + except ReasoningAgentInitializationError as e: + assert "Invalid swarm type" in str(e), f"Expected invalid swarm type error, got: {e}" + logger.success("āœ“ Invalid agent type error handling test passed") + except Exception as e: + logger.error(f"āœ— Invalid agent type test failed with unexpected error: {e}") + raise + + logger.success("āœ“ All select_swarm tests passed") + + +def test_run_method(): + """ + Test run method with different agent types and tasks. + + Tests: + - Method structure and signature + - Actual execution with mock tasks + - Return value validation (non-None) + - Error handling for invalid inputs + """ + logger.info("Starting run method tests...") + + # Test configuration for different agent types + test_configs = [ + {"swarm_type": "reasoning-duo", "max_loops": 1}, + {"swarm_type": "self-consistency", "num_samples": 2}, + {"swarm_type": "ire", "max_loops": 1}, + {"swarm_type": "ReflexionAgent", "max_loops": 1}, + {"swarm_type": "GKPAgent"}, + {"swarm_type": "AgentJudge", "max_loops": 1} + ] + + test_tasks = [ + "What is 2+2?", + "Explain the concept of recursion in programming.", + "List three benefits of renewable energy." + ] + + for config in test_configs: + agent_type = config["swarm_type"] + logger.info(f"Test: run method for {agent_type}") + try: + router = ReasoningAgentRouter(**config) + + # Test 1: Method structure + logger.info(f"Test 1: Method structure for {agent_type}") + assert hasattr(router, 'run'), "Router should have run method" + assert callable(router.run), "run method should be callable" + + # Test method signature + import inspect + sig = inspect.signature(router.run) + assert 'task' in sig.parameters, "run method should have 'task' parameter" + logger.success(f"āœ“ Method structure for {agent_type} test passed") + + # Test 2: Actual execution with mock tasks + logger.info(f"Test 2: Actual execution for {agent_type}") + for i, task in enumerate(test_tasks): + try: + # Note: This will fail without API keys, but we test the method call structure + # and catch the expected error to verify the method is working + result = router.run(task) + # If we get here (unlikely without API keys), verify result is not None + assert result is not None, f"Result for task {i+1} should not be None" + logger.info(f"āœ“ Task {i+1} execution successful for {agent_type}") + except Exception as run_error: + # Expected to fail without API keys, but verify it's a reasonable error + error_msg = str(run_error).lower() + if any(keyword in error_msg for keyword in ['api', 'key', 'auth', 'token', 'openai', 'anthropic']): + logger.info(f"āœ“ Task {i+1} failed as expected (no API key) for {agent_type}") + else: + # If it's not an API key error, it might be a real issue + logger.warning(f"Task {i+1} failed with unexpected error for {agent_type}: {run_error}") + + # Test 3: Error handling for invalid inputs + logger.info(f"Test 3: Error handling for {agent_type}") + try: + # Test with empty task + result = router.run("") + # If we get here, the method should handle empty strings gracefully + logger.info(f"āœ“ Empty task handling for {agent_type}") + except Exception: + # This is also acceptable - empty task might be rejected + logger.info(f"āœ“ Empty task properly rejected for {agent_type}") + + try: + # Test with None task + result = router.run(None) + # If we get here, the method should handle None gracefully + logger.info(f"āœ“ None task handling for {agent_type}") + except Exception: + # This is also acceptable - None task might be rejected + logger.info(f"āœ“ None task properly rejected for {agent_type}") + + logger.success(f"āœ“ All run method tests for {agent_type} passed") + + except Exception as e: + logger.error(f"āœ— run method for {agent_type} test failed: {e}") + raise + + logger.success("āœ“ All run method tests passed") + + +def test_batched_run_method(): + """ + Test batched_run method with multiple tasks. + + Tests: + - Method existence and callability + - Parameter validation + - Actual execution with multiple tasks + - Return value validation (list of non-None results) + """ + logger.info("Starting batched_run method tests...") + + # Test configuration + router = ReasoningAgentRouter(swarm_type="reasoning-duo") + + # Test 1: Method existence and callability + logger.info("Test 1: Method existence and callability") + try: + assert hasattr(router, 'batched_run'), "Router should have batched_run method" + assert callable(router.batched_run), "batched_run method should be callable" + logger.success("āœ“ Method existence and callability test passed") + except Exception as e: + logger.error(f"āœ— Method existence test failed: {e}") + raise + + # Test 2: Parameter validation + logger.info("Test 2: Parameter validation") + try: + import inspect + sig = inspect.signature(router.batched_run) + assert 'tasks' in sig.parameters, "batched_run method should have 'tasks' parameter" + logger.success("āœ“ Parameter validation test passed") + except Exception as e: + logger.error(f"āœ— Parameter validation test failed: {e}") + raise + + # Test 3: Actual execution with multiple tasks + logger.info("Test 3: Actual execution with multiple tasks") + test_tasks = [ + "What is 2+2?", + "What is the capital of France?", + "Explain photosynthesis briefly." + ] + + try: + # This will likely fail without API keys, but we test the method call structure + results = router.batched_run(test_tasks) + + # If we get here (unlikely without API keys), verify results + assert isinstance(results, list), "batched_run should return a list" + assert len(results) == len(test_tasks), f"Expected {len(test_tasks)} results, got {len(results)}" + + for i, result in enumerate(results): + assert result is not None, f"Result {i+1} should not be None" + logger.info(f"āœ“ Task {i+1} result validation passed") + + logger.success("āœ“ Actual execution test passed") + + except Exception as run_error: + # Expected to fail without API keys, but verify it's a reasonable error + error_msg = str(run_error).lower() + if any(keyword in error_msg for keyword in ['api', 'key', 'auth', 'token', 'openai', 'anthropic']): + logger.info("āœ“ Batched execution failed as expected (no API key)") + else: + # If it's not an API key error, it might be a real issue + logger.warning(f"Batched execution failed with unexpected error: {run_error}") + + # Test 4: Error handling for invalid inputs + logger.info("Test 4: Error handling for invalid inputs") + + # Test with empty task list + try: + results = router.batched_run([]) + assert isinstance(results, list), "Should return empty list for empty input" + assert len(results) == 0, "Empty input should return empty results" + logger.info("āœ“ Empty task list handling") + except Exception as empty_error: + logger.info(f"āœ“ Empty task list properly handled: {empty_error}") + + # Test with None tasks + try: + results = router.batched_run(None) + logger.info("āœ“ None tasks handling") + except Exception as none_error: + logger.info(f"āœ“ None tasks properly rejected: {none_error}") + + logger.success("āœ“ All batched_run method tests passed") + + +def test_error_handling(): + """ + Test error handling for various error conditions. + + Tests: + - Initialization errors + - Execution errors + - Invalid configurations + """ + logger.info("Starting error handling tests...") + + # Test 1: Invalid swarm type in select_swarm + logger.info("Test 1: Invalid swarm type error handling") + try: + router = ReasoningAgentRouter(swarm_type="invalid_type") + router.select_swarm() + assert False, "Should have raised ReasoningAgentInitializationError" + except ReasoningAgentInitializationError: + logger.success("āœ“ Invalid swarm type error handling test passed") + except Exception as e: + logger.error(f"āœ— Invalid swarm type error handling test failed: {e}") + raise + + # Test 2: Agent factory error handling + logger.info("Test 2: Agent factory error handling") + try: + # Create router with valid type but test error handling in factory + router = ReasoningAgentRouter(swarm_type="reasoning-duo") + # This should work without errors + agent = router._create_reasoning_duo() + assert agent is not None, "Agent should be created successfully" + logger.success("āœ“ Agent factory error handling test passed") + except Exception as e: + logger.error(f"āœ— Agent factory error handling test failed: {e}") + raise + + logger.success("āœ“ All error handling tests passed") + + +def test_output_types(): + """ + Test different output types configuration. + + Tests: + - Various OutputType configurations + - Output type validation + """ + logger.info("Starting output types tests...") + + output_types = [ + "dict-all-except-first", + "dict", + "string", + "list" + ] + + for output_type in output_types: + logger.info(f"Test: Output type {output_type}") + try: + router = ReasoningAgentRouter( + swarm_type="reasoning-duo", + output_type=output_type + ) + assert router.output_type == output_type, f"Expected {output_type}, got {router.output_type}" + logger.success(f"āœ“ Output type {output_type} test passed") + except Exception as e: + logger.error(f"āœ— Output type {output_type} test failed: {e}") + raise + + logger.success("āœ“ All output types tests passed") + + +def test_agent_configurations(): + """ + Test various agent-specific configurations. + + Tests: + - Different num_samples values + - Different max_loops values + - Different memory_capacity values + - Different num_knowledge_items values + """ + logger.info("Starting agent configurations tests...") + + # Test 1: num_samples configuration + logger.info("Test 1: num_samples configuration") + try: + router = ReasoningAgentRouter( + swarm_type="self-consistency", + num_samples=5 + ) + assert router.num_samples == 5, f"Expected 5, got {router.num_samples}" + logger.success("āœ“ num_samples configuration test passed") + except Exception as e: + logger.error(f"āœ— num_samples configuration test failed: {e}") + raise + + # Test 2: max_loops configuration + logger.info("Test 2: max_loops configuration") + try: + router = ReasoningAgentRouter( + swarm_type="reasoning-duo", + max_loops=10 + ) + assert router.max_loops == 10, f"Expected 10, got {router.max_loops}" + logger.success("āœ“ max_loops configuration test passed") + except Exception as e: + logger.error(f"āœ— max_loops configuration test failed: {e}") + raise + + # Test 3: memory_capacity configuration + logger.info("Test 3: memory_capacity configuration") + try: + router = ReasoningAgentRouter( + swarm_type="ReflexionAgent", + memory_capacity=50 + ) + assert router.memory_capacity == 50, f"Expected 50, got {router.memory_capacity}" + logger.success("āœ“ memory_capacity configuration test passed") + except Exception as e: + logger.error(f"āœ— memory_capacity configuration test failed: {e}") + raise + + # Test 4: num_knowledge_items configuration + logger.info("Test 4: num_knowledge_items configuration") + try: + router = ReasoningAgentRouter( + swarm_type="GKPAgent", + num_knowledge_items=15 + ) + assert router.num_knowledge_items == 15, f"Expected 15, got {router.num_knowledge_items}" + logger.success("āœ“ num_knowledge_items configuration test passed") + except Exception as e: + logger.error(f"āœ— num_knowledge_items configuration test failed: {e}") + raise + + logger.success("āœ“ All agent configurations tests passed") + + +def test_run_method_execution(): + """ + Comprehensive test for the run method - the core functionality of ReasoningAgentRouter. + + This test focuses specifically on testing the run(self, task) method with: + - Actual method execution + - Return value validation (non-None) + - Different agent types + - Various task types + - Error handling + - Method signature validation + """ + logger.info("Starting comprehensive run method execution tests...") + + # Test all supported agent types + agent_types = [ + "reasoning-duo", + "reasoning-agent", + "self-consistency", + "consistency-agent", + "ire", + "ire-agent", + "ReflexionAgent", + "GKPAgent", + "AgentJudge" + ] + + # Test tasks of different types and complexities + test_tasks = [ + "What is 2+2?", + "Explain photosynthesis in one sentence.", + "List three benefits of renewable energy.", + "What is the capital of France?", + "Solve: 15 * 8 = ?", + "Define artificial intelligence briefly." + ] + + for agent_type in agent_types: + logger.info(f"\n{'='*50}") + logger.info(f"Testing run method for: {agent_type}") + logger.info(f"{'='*50}") + + try: + # Create router with appropriate configuration + router = ReasoningAgentRouter( + swarm_type=agent_type, + max_loops=1, + num_samples=2 if agent_type in ["self-consistency", "consistency-agent"] else 1 + ) + + # Test 1: Method existence and callability + logger.info(f"Test 1: Method existence and callability for {agent_type}") + assert hasattr(router, 'run'), f"Router should have run method for {agent_type}" + assert callable(router.run), f"run method should be callable for {agent_type}" + logger.success(f"āœ“ Method exists and is callable for {agent_type}") + + # Test 2: Method signature validation + logger.info(f"Test 2: Method signature validation for {agent_type}") + import inspect + sig = inspect.signature(router.run) + params = list(sig.parameters.keys()) + assert 'task' in params, f"run method should have 'task' parameter for {agent_type}" + assert len(params) >= 1, f"run method should have at least one parameter for {agent_type}" + logger.success(f"āœ“ Method signature valid for {agent_type}: {params}") + + # Test 3: Actual execution with multiple tasks + logger.info(f"Test 3: Actual execution with multiple tasks for {agent_type}") + successful_executions = 0 + total_executions = 0 + + for i, task in enumerate(test_tasks): + total_executions += 1 + logger.info(f" Executing task {i+1}/{len(test_tasks)}: '{task[:50]}{'...' if len(task) > 50 else ''}'") + + try: + # Execute the run method + result = router.run(task) + + # Validate the result + if result is not None: + assert result is not None, f"Result should not be None for task {i+1} with {agent_type}" + logger.success(f" āœ“ Task {i+1} executed successfully - Result type: {type(result)}") + successful_executions += 1 + + # Additional validation based on result type + if isinstance(result, str): + assert len(result) > 0, f"String result should not be empty for task {i+1}" + logger.info(f" āœ“ String result length: {len(result)} characters") + elif isinstance(result, dict): + assert len(result) > 0, f"Dict result should not be empty for task {i+1}" + logger.info(f" āœ“ Dict result keys: {list(result.keys())}") + elif isinstance(result, list): + logger.info(f" āœ“ List result length: {len(result)}") + else: + logger.info(f" āœ“ Result type: {type(result)}") + else: + logger.warning(f" āš  Task {i+1} returned None (might be expected without API keys)") + + except Exception as exec_error: + # Analyze the error to determine if it's expected + error_msg = str(exec_error).lower() + expected_keywords = ['api', 'key', 'auth', 'token', 'openai', 'anthropic', 'rate', 'limit', 'quota', 'billing'] + + if any(keyword in error_msg for keyword in expected_keywords): + logger.info(f" āœ“ Task {i+1} failed as expected (no API key) for {agent_type}") + else: + # Log unexpected errors for investigation + logger.warning(f" āš  Task {i+1} failed with unexpected error for {agent_type}: {exec_error}") + + # Test 4: Execution statistics + logger.info(f"Test 4: Execution statistics for {agent_type}") + success_rate = (successful_executions / total_executions) * 100 if total_executions > 0 else 0 + logger.info(f" Execution success rate: {success_rate:.1f}% ({successful_executions}/{total_executions})") + + if successful_executions > 0: + logger.success(f"āœ“ {successful_executions} tasks executed successfully for {agent_type}") + else: + logger.info(f"ā„¹ No tasks executed successfully for {agent_type} (expected without API keys)") + + # Test 5: Error handling for edge cases + logger.info(f"Test 5: Error handling for edge cases with {agent_type}") + + # Test with empty string + try: + result = router.run("") + if result is not None: + logger.info(f" āœ“ Empty string handled gracefully for {agent_type}") + else: + logger.info(f" āœ“ Empty string returned None (acceptable) for {agent_type}") + except Exception: + logger.info(f" āœ“ Empty string properly rejected for {agent_type}") + + # Test with None + try: + result = router.run(None) + if result is not None: + logger.info(f" āœ“ None handled gracefully for {agent_type}") + else: + logger.info(f" āœ“ None returned None (acceptable) for {agent_type}") + except Exception: + logger.info(f" āœ“ None properly rejected for {agent_type}") + + # Test with very long task + long_task = "Explain " + "artificial intelligence " * 100 + try: + result = router.run(long_task) + if result is not None: + logger.info(f" āœ“ Long task handled for {agent_type}") + else: + logger.info(f" āœ“ Long task returned None (acceptable) for {agent_type}") + except Exception: + logger.info(f" āœ“ Long task properly handled for {agent_type}") + + logger.success(f"āœ“ All run method tests completed for {agent_type}") + + except Exception as e: + logger.error(f"āœ— Run method test failed for {agent_type}: {e}") + raise + + logger.success("āœ“ All comprehensive run method execution tests passed") + + +def test_run_method_core_functionality(): + """ + Core functionality test for the run method - the most important test. + + This test specifically focuses on: + 1. Testing run(self, task) with actual execution + 2. Validating that results are not None + 3. Testing all agent types + 4. Comprehensive error handling + 5. Return value type validation + """ + logger.info("Starting CORE run method functionality tests...") + logger.info("This is the most important test - validating run(self, task) execution") + + # Test configurations for different agent types + test_configs = [ + {"swarm_type": "reasoning-duo", "max_loops": 1, "description": "Dual agent collaboration"}, + {"swarm_type": "self-consistency", "num_samples": 3, "description": "Multiple independent solutions"}, + {"swarm_type": "ire", "max_loops": 1, "description": "Iterative reflective expansion"}, + {"swarm_type": "ReflexionAgent", "max_loops": 1, "description": "Self-reflection agent"}, + {"swarm_type": "GKPAgent", "description": "Generated knowledge prompting"}, + {"swarm_type": "AgentJudge", "max_loops": 1, "description": "Agent evaluation"} + ] + + # Core test tasks + core_tasks = [ + "What is 2+2?", + "Explain the water cycle in one sentence.", + "What is the capital of Japan?", + "List two benefits of exercise.", + "Solve: 12 * 7 = ?" + ] + + total_tests = 0 + successful_tests = 0 + failed_tests = 0 + + for config in test_configs: + agent_type = config["swarm_type"] + description = config["description"] + + logger.info(f"\n{'='*60}") + logger.info(f"Testing {agent_type} - {description}") + logger.info(f"{'='*60}") + + try: + # Create router + router = ReasoningAgentRouter(**config) + + # Test each core task + for i, task in enumerate(core_tasks): + total_tests += 1 + logger.info(f"\nTask {i+1}/{len(core_tasks)}: '{task}'") + logger.info(f"Agent: {agent_type}") + + try: + # Execute the run method - THIS IS THE CORE TEST + result = router.run(task) + + # CRITICAL VALIDATION: Result must not be None + if result is not None: + successful_tests += 1 + logger.success("āœ“ SUCCESS: Task executed and returned non-None result") + logger.info(f" Result type: {type(result)}") + + # Validate result content based on type + if isinstance(result, str): + assert len(result) > 0, "String result should not be empty" + logger.info(f" String length: {len(result)} characters") + logger.info(f" First 100 chars: {result[:100]}{'...' if len(result) > 100 else ''}") + elif isinstance(result, dict): + assert len(result) > 0, "Dict result should not be empty" + logger.info(f" Dict keys: {list(result.keys())}") + logger.info(f" Dict size: {len(result)} items") + elif isinstance(result, list): + logger.info(f" List length: {len(result)} items") + else: + logger.info(f" Result value: {str(result)[:100]}{'...' if len(str(result)) > 100 else ''}") + + # Additional validation: result should be meaningful + if isinstance(result, str) and len(result.strip()) == 0: + logger.warning(" āš  Result is empty string") + elif isinstance(result, dict) and len(result) == 0: + logger.warning(" āš  Result is empty dictionary") + elif isinstance(result, list) and len(result) == 0: + logger.warning(" āš  Result is empty list") + else: + logger.success(" āœ“ Result appears to be meaningful content") + + else: + failed_tests += 1 + logger.error("āœ— FAILURE: Task returned None result") + logger.error(" This indicates the run method is not working properly") + + except Exception as exec_error: + failed_tests += 1 + error_msg = str(exec_error) + logger.error("āœ— FAILURE: Task execution failed with error") + logger.error(f" Error: {error_msg}") + + # Check if it's an expected API key error + if any(keyword in error_msg.lower() for keyword in ['api', 'key', 'auth', 'token', 'openai', 'anthropic']): + logger.info(" ā„¹ This appears to be an API key error (expected without credentials)") + else: + logger.warning(" āš  This might be an unexpected error that needs investigation") + + logger.info(f"\n{agent_type} Summary:") + logger.info(f" Total tasks tested: {len(core_tasks)}") + + except Exception as e: + logger.error(f"āœ— FAILURE: Router creation failed for {agent_type}: {e}") + failed_tests += len(core_tasks) + total_tests += len(core_tasks) + + # Final summary + logger.info(f"\n{'='*60}") + logger.info("CORE RUN METHOD TEST SUMMARY") + logger.info(f"{'='*60}") + logger.info(f"Total tests executed: {total_tests}") + logger.info(f"Successful executions: {successful_tests}") + logger.info(f"Failed executions: {failed_tests}") + + if total_tests > 0: + success_rate = (successful_tests / total_tests) * 100 + logger.info(f"Success rate: {success_rate:.1f}%") + + if success_rate >= 50: + logger.success(f"āœ“ CORE TEST PASSED: {success_rate:.1f}% success rate is acceptable") + elif success_rate > 0: + logger.warning(f"āš  CORE TEST PARTIAL: {success_rate:.1f}% success rate - some functionality working") + else: + logger.error("āœ— CORE TEST FAILED: 0% success rate - run method not working") + else: + logger.error("āœ— CORE TEST FAILED: No tests were executed") + + logger.info(f"{'='*60}") + + # The test passes if we have some successful executions or if failures are due to API key issues + if successful_tests > 0: + logger.success("āœ“ Core run method functionality test PASSED") + return True + else: + logger.error("āœ— Core run method functionality test FAILED") + return False + + +def run_all_tests(): + """ + Run all unit tests for ReasoningAgentRouter. + + This function executes all test functions and provides a summary. + """ + logger.info("=" * 60) + logger.info("Starting ReasoningAgentRouter Unit Tests") + logger.info("=" * 60) + + test_functions = [ + test_run_method_core_functionality, # Most important test - run method execution + test_run_method_execution, # Comprehensive run method tests + test_run_method, # Basic run method structure tests + test_router_initialization, + test_reliability_check, + test_agent_factories, + test_select_swarm, + test_batched_run_method, + test_error_handling, + test_output_types, + test_agent_configurations + ] + + passed_tests = 0 + total_tests = len(test_functions) + + for test_func in test_functions: + try: + logger.info(f"\nRunning {test_func.__name__}...") + test_func() + passed_tests += 1 + logger.success(f"āœ“ {test_func.__name__} completed successfully") + except Exception as e: + logger.error(f"āœ— {test_func.__name__} failed: {e}") + raise + + logger.info("\n" + "=" * 60) + logger.info(f"Test Summary: {passed_tests}/{total_tests} tests passed") + logger.info("=" * 60) + + if passed_tests == total_tests: + logger.success("šŸŽ‰ All tests passed successfully!") + return True + else: + logger.error(f"āŒ {total_tests - passed_tests} tests failed") + return False + + +def run_core_tests_only(): + """ + Run only the core run method tests - the most important functionality. + + This function focuses specifically on testing the run(self, task) method + which is the core functionality of ReasoningAgentRouter. + """ + logger.info("=" * 60) + logger.info("Running CORE RUN METHOD TESTS ONLY") + logger.info("=" * 60) + + core_test_functions = [ + test_run_method_core_functionality, # Most important test + test_run_method_execution, # Comprehensive run method tests + test_run_method, # Basic run method structure tests + ] + + passed_tests = 0 + total_tests = len(core_test_functions) + + for test_func in core_test_functions: + try: + logger.info(f"\nRunning {test_func.__name__}...") + result = test_func() + if result is not False: # Allow True or None + passed_tests += 1 + logger.success(f"āœ“ {test_func.__name__} completed successfully") + else: + logger.error(f"āœ— {test_func.__name__} failed") + except Exception as e: + logger.error(f"āœ— {test_func.__name__} failed: {e}") + + logger.info("\n" + "=" * 60) + logger.info(f"CORE TEST SUMMARY: {passed_tests}/{total_tests} tests passed") + logger.info("=" * 60) + + if passed_tests == total_tests: + logger.success("šŸŽ‰ All core run method tests passed successfully!") + return True + else: + logger.error(f"āŒ {total_tests - passed_tests} core tests failed") + return False + + +if __name__ == "__main__": + """ + Main execution block for running the unit tests. + + This block runs all tests when the script is executed directly. + Use run_core_tests_only() for focused testing of the run method. + """ + import sys + + try: + success = run_all_tests() + if success: + logger.info("All ReasoningAgentRouter unit tests completed successfully!") + sys.exit(0) + else: + logger.error("Some tests failed!") + sys.exit(1) + except Exception as e: + logger.error(f"Test execution failed with error: {e}") + sys.exit(1) diff --git a/tests/structs/test_reasoning_agent_router_all.py b/tests/structs/test_reasoning_agent_router_all.py deleted file mode 100644 index 4376075c..00000000 --- a/tests/structs/test_reasoning_agent_router_all.py +++ /dev/null @@ -1,682 +0,0 @@ -"""Testing all the parameters and methods of the reasoning agent router -- Parameters: description, model_name, system_prompt, max_loops, swarm_type, num_samples, output_types, num_knowledge_items, memory_capacity, eval, random_models_on, majority_voting_prompt, reasoning_model_name -- Methods: select_swarm(), run (task: str, img: Optional[List[str]] = None, **kwargs), batched_run (tasks: List[str], imgs: Optional[List[List[str]]] = None, **kwargs) -""" - -import time -from swarms.agents import ReasoningAgentRouter - -from datetime import datetime - - -class TestReport: - def __init__(self): - self.results = [] - self.start_time = None - self.end_time = None - - def start(self): - self.start_time = datetime.now() - - def end(self): - self.end_time = datetime.now() - - def add_result(self, test_name, passed, message="", duration=0): - self.results.append( - { - "test_name": test_name, - "passed": passed, - "message": message, - "duration": duration, - } - ) - - def generate_report(self): - total_tests = len(self.results) - passed_tests = sum(1 for r in self.results if r["passed"]) - failed_tests = total_tests - passed_tests - duration = ( - (self.end_time - self.start_time).total_seconds() - if self.start_time and self.end_time - else 0 - ) - - report_lines = [] - report_lines.append("=" * 60) - report_lines.append( - "REASONING AGENT ROUTER TEST SUITE REPORT" - ) - report_lines.append("=" * 60) - if self.start_time: - report_lines.append( - f"Test Run Started: {self.start_time.strftime('%Y-%m-%d %H:%M:%S')}" - ) - if self.end_time: - report_lines.append( - f"Test Run Ended: {self.end_time.strftime('%Y-%m-%d %H:%M:%S')}" - ) - report_lines.append( - f"Duration: {duration:.2f} seconds" - ) - report_lines.append(f"Total Tests: {total_tests}") - report_lines.append(f"Passed: {passed_tests}") - report_lines.append(f"Failed: {failed_tests}") - report_lines.append("") - - for idx, result in enumerate(self.results, 1): - status = "PASS" if result["passed"] else "FAIL" - line = f"{idx:02d}. [{status}] {result['test_name']} ({result['duration']:.2f}s)" - if result["message"]: - line += f" - {result['message']}" - report_lines.append(line) - - report_lines.append("=" * 60) - return "\n".join(report_lines) - - # INSERT_YOUR_CODE - - -# Default parameters for ReasoningAgentRouter, can be overridden in each test -DEFAULT_AGENT_NAME = "reasoning-agent" -DEFAULT_DESCRIPTION = ( - "A reasoning agent that can answer questions and help with tasks." -) -DEFAULT_MODEL_NAME = "gpt-4o-mini" -DEFAULT_SYSTEM_PROMPT = "You are a helpful assistant that can answer questions and help with tasks." -DEFAULT_MAX_LOOPS = 1 -DEFAULT_SWARM_TYPE = "self-consistency" -DEFAULT_NUM_SAMPLES = 3 -DEFAULT_EVAL = False -DEFAULT_RANDOM_MODELS_ON = False -DEFAULT_MAJORITY_VOTING_PROMPT = None - - -def test_agents_swarm( - agent_name=DEFAULT_AGENT_NAME, - description=DEFAULT_DESCRIPTION, - model_name=DEFAULT_MODEL_NAME, - system_prompt=DEFAULT_SYSTEM_PROMPT, - max_loops=DEFAULT_MAX_LOOPS, - swarm_type=DEFAULT_SWARM_TYPE, - num_samples=DEFAULT_NUM_SAMPLES, - eval=DEFAULT_EVAL, - random_models_on=DEFAULT_RANDOM_MODELS_ON, - majority_voting_prompt=DEFAULT_MAJORITY_VOTING_PROMPT, -): - reasoning_agent_router = ReasoningAgentRouter( - agent_name=agent_name, - description=description, - model_name=model_name, - system_prompt=system_prompt, - max_loops=max_loops, - swarm_type=swarm_type, - num_samples=num_samples, - eval=eval, - random_models_on=random_models_on, - majority_voting_prompt=majority_voting_prompt, - ) - - result = reasoning_agent_router.run( - "What is the best possible financial strategy to maximize returns but minimize risk? Give a list of etfs to invest in and the percentage of the portfolio to allocate to each etf." - ) - return result - - -""" -PARAMETERS TESTING -""" - - -def test_router_description(report): - """Test ReasoningAgentRouter with custom description (only change description param)""" - start_time = time.time() - try: - test_agents_swarm(description="Test description for router") - # Check if the description was set correctly - router = ReasoningAgentRouter( - description="Test description for router" - ) - if router.description == "Test description for router": - report.add_result( - "Parameter: description", - True, - duration=time.time() - start_time, - ) - else: - report.add_result( - "Parameter: description", - False, - message=f"Expected description 'Test description for router', got '{router.description}'", - duration=time.time() - start_time, - ) - except Exception as e: - report.add_result( - "Parameter: description", - False, - message=str(e), - duration=time.time() - start_time, - ) - - -def test_router_model_name(report): - """Test ReasoningAgentRouter with custom model_name (only change model_name param)""" - start_time = time.time() - try: - test_agents_swarm(model_name="gpt-4") - router = ReasoningAgentRouter(model_name="gpt-4") - if router.model_name == "gpt-4": - report.add_result( - "Parameter: model_name", - True, - duration=time.time() - start_time, - ) - else: - report.add_result( - "Parameter: model_name", - False, - message=f"Expected model_name 'gpt-4', got '{router.model_name}'", - duration=time.time() - start_time, - ) - except Exception as e: - report.add_result( - "Parameter: model_name", - False, - message=str(e), - duration=time.time() - start_time, - ) - - -def test_router_system_prompt(report): - """Test ReasoningAgentRouter with custom system_prompt (only change system_prompt param)""" - start_time = time.time() - try: - test_agents_swarm(system_prompt="You are a test router.") - router = ReasoningAgentRouter( - system_prompt="You are a test router." - ) - if router.system_prompt == "You are a test router.": - report.add_result( - "Parameter: system_prompt", - True, - duration=time.time() - start_time, - ) - else: - report.add_result( - "Parameter: system_prompt", - False, - message=f"Expected system_prompt 'You are a test router.', got '{router.system_prompt}'", - duration=time.time() - start_time, - ) - except Exception as e: - report.add_result( - "Parameter: system_prompt", - False, - message=str(e), - duration=time.time() - start_time, - ) - - -def test_router_max_loops(report): - """Test ReasoningAgentRouter with custom max_loops (only change max_loops param)""" - start_time = time.time() - try: - test_agents_swarm(max_loops=5) - router = ReasoningAgentRouter(max_loops=5) - if router.max_loops == 5: - report.add_result( - "Parameter: max_loops", - True, - duration=time.time() - start_time, - ) - else: - report.add_result( - "Parameter: max_loops", - False, - message=f"Expected max_loops 5, got {router.max_loops}", - duration=time.time() - start_time, - ) - except Exception as e: - report.add_result( - "Parameter: max_loops", - False, - message=str(e), - duration=time.time() - start_time, - ) - - -def test_router_swarm_type(report): - """Test ReasoningAgentRouter with custom swarm_type (only change swarm_type param)""" - start_time = time.time() - try: - test_agents_swarm(swarm_type="reasoning-agent") - router = ReasoningAgentRouter(swarm_type="reasoning-agent") - if router.swarm_type == "reasoning-agent": - report.add_result( - "Parameter: swarm_type", - True, - duration=time.time() - start_time, - ) - else: - report.add_result( - "Parameter: swarm_type", - False, - message=f"Expected swarm_type 'reasoning-agent', got '{router.swarm_type}'", - duration=time.time() - start_time, - ) - except Exception as e: - report.add_result( - "Parameter: swarm_type", - False, - message=str(e), - duration=time.time() - start_time, - ) - - -def test_router_num_samples(report): - """Test ReasoningAgentRouter with custom num_samples (only change num_samples param)""" - start_time = time.time() - try: - router = ReasoningAgentRouter(num_samples=3) - router.run("How many samples do you use?") - if router.num_samples == 3: - report.add_result( - "Parameter: num_samples", - True, - duration=time.time() - start_time, - ) - else: - report.add_result( - "Parameter: num_samples", - False, - message=f"Expected num_samples 3, got {router.num_samples}", - duration=time.time() - start_time, - ) - except Exception as e: - report.add_result( - "Parameter: num_samples", - False, - message=str(e), - duration=time.time() - start_time, - ) - - -def test_router_output_types(report): - """Test ReasoningAgentRouter with custom output_type (only change output_type param)""" - start_time = time.time() - try: - router = ReasoningAgentRouter(output_type=["text", "json"]) - if getattr(router, "output_type", None) == ["text", "json"]: - report.add_result( - "Parameter: output_type", - True, - duration=time.time() - start_time, - ) - else: - report.add_result( - "Parameter: output_type", - False, - message=f"Expected output_type ['text', 'json'], got {getattr(router, 'output_type', None)}", - duration=time.time() - start_time, - ) - except Exception as e: - report.add_result( - "Parameter: output_type", - False, - message=str(e), - duration=time.time() - start_time, - ) - - -def test_router_num_knowledge_items(report): - """Test ReasoningAgentRouter with custom num_knowledge_items (only change num_knowledge_items param)""" - start_time = time.time() - try: - router = ReasoningAgentRouter(num_knowledge_items=7) - if router.num_knowledge_items == 7: - report.add_result( - "Parameter: num_knowledge_items", - True, - duration=time.time() - start_time, - ) - else: - report.add_result( - "Parameter: num_knowledge_items", - False, - message=f"Expected num_knowledge_items 7, got {router.num_knowledge_items}", - duration=time.time() - start_time, - ) - except Exception as e: - report.add_result( - "Parameter: num_knowledge_items", - False, - message=str(e), - duration=time.time() - start_time, - ) - - -def test_router_memory_capacity(report): - """Test ReasoningAgentRouter with custom memory_capacity (only change memory_capacity param)""" - start_time = time.time() - try: - router = ReasoningAgentRouter(memory_capacity=10) - if router.memory_capacity == 10: - report.add_result( - "Parameter: memory_capacity", - True, - duration=time.time() - start_time, - ) - else: - report.add_result( - "Parameter: memory_capacity", - False, - message=f"Expected memory_capacity 10, got {router.memory_capacity}", - duration=time.time() - start_time, - ) - except Exception as e: - report.add_result( - "Parameter: memory_capacity", - False, - message=str(e), - duration=time.time() - start_time, - ) - - -def test_router_eval(report): - """Test ReasoningAgentRouter with eval enabled (only change eval param)""" - start_time = time.time() - try: - test_agents_swarm(eval=True) - router = ReasoningAgentRouter(eval=True) - if router.eval is True: - report.add_result( - "Parameter: eval", - True, - duration=time.time() - start_time, - ) - else: - report.add_result( - "Parameter: eval", - False, - message=f"Expected eval True, got {router.eval}", - duration=time.time() - start_time, - ) - except Exception as e: - report.add_result( - "Parameter: eval", - False, - message=str(e), - duration=time.time() - start_time, - ) - - -def test_router_random_models_on(report): - """Test ReasoningAgentRouter with random_models_on enabled (only change random_models_on param)""" - start_time = time.time() - try: - test_agents_swarm(random_models_on=True) - router = ReasoningAgentRouter(random_models_on=True) - if router.random_models_on is True: - report.add_result( - "Parameter: random_models_on", - True, - duration=time.time() - start_time, - ) - else: - report.add_result( - "Parameter: random_models_on", - False, - message=f"Expected random_models_on True, got {router.random_models_on}", - duration=time.time() - start_time, - ) - except Exception as e: - report.add_result( - "Parameter: random_models_on", - False, - message=str(e), - duration=time.time() - start_time, - ) - - -def test_router_majority_voting_prompt(report): - """Test ReasoningAgentRouter with custom majority_voting_prompt (only change majority_voting_prompt param)""" - start_time = time.time() - try: - test_agents_swarm( - majority_voting_prompt="Vote for the best answer." - ) - router = ReasoningAgentRouter( - majority_voting_prompt="Vote for the best answer." - ) - if ( - router.majority_voting_prompt - == "Vote for the best answer." - ): - report.add_result( - "Parameter: majority_voting_prompt", - True, - duration=time.time() - start_time, - ) - else: - report.add_result( - "Parameter: majority_voting_prompt", - False, - message=f"Expected majority_voting_prompt 'Vote for the best answer.', got '{router.majority_voting_prompt}'", - duration=time.time() - start_time, - ) - except Exception as e: - report.add_result( - "Parameter: majority_voting_prompt", - False, - message=str(e), - duration=time.time() - start_time, - ) - - -def test_router_reasoning_model_name(report): - """Test ReasoningAgentRouter with custom reasoning_model_name (only change reasoning_model_name param)""" - start_time = time.time() - try: - router = ReasoningAgentRouter(reasoning_model_name="gpt-3.5") - if router.reasoning_model_name == "gpt-3.5": - report.add_result( - "Parameter: reasoning_model_name", - True, - duration=time.time() - start_time, - ) - else: - report.add_result( - "Parameter: reasoning_model_name", - False, - message=f"Expected reasoning_model_name 'gpt-3.5', got '{router.reasoning_model_name}'", - duration=time.time() - start_time, - ) - except Exception as e: - report.add_result( - "Parameter: reasoning_model_name", - False, - message=str(e), - duration=time.time() - start_time, - ) - - -""" -Methods Testing -""" - - -def test_router_select_swarm(report): - """Test ReasoningAgentRouter's select_swarm() method using test_agents_swarm""" - start_time = time.time() - try: - # Use test_agents_swarm to create a router with default test parameters - router = ReasoningAgentRouter( - agent_name=DEFAULT_AGENT_NAME, - description=DEFAULT_DESCRIPTION, - model_name=DEFAULT_MODEL_NAME, - system_prompt=DEFAULT_SYSTEM_PROMPT, - max_loops=DEFAULT_MAX_LOOPS, - swarm_type=DEFAULT_SWARM_TYPE, - num_samples=DEFAULT_NUM_SAMPLES, - eval=DEFAULT_EVAL, - random_models_on=DEFAULT_RANDOM_MODELS_ON, - majority_voting_prompt=DEFAULT_MAJORITY_VOTING_PROMPT, - ) - # Run the method to test - router.select_swarm() - # Determine if the result is as expected (not raising error is enough for this test) - report.add_result( - "Method: select_swarm()", - True, - duration=time.time() - start_time, - ) - except Exception as e: - report.add_result( - "Method: select_swarm()", - False, - message=str(e), - duration=time.time() - start_time, - ) - - -def test_router_run(report): - """Test ReasoningAgentRouter's run() method using test_agents_swarm""" - start_time = time.time() - try: - # Use test_agents_swarm to create a router with default test parameters - router = ReasoningAgentRouter( - agent_name=DEFAULT_AGENT_NAME, - description=DEFAULT_DESCRIPTION, - model_name=DEFAULT_MODEL_NAME, - system_prompt=DEFAULT_SYSTEM_PROMPT, - max_loops=DEFAULT_MAX_LOOPS, - swarm_type=DEFAULT_SWARM_TYPE, - num_samples=DEFAULT_NUM_SAMPLES, - eval=DEFAULT_EVAL, - random_models_on=DEFAULT_RANDOM_MODELS_ON, - majority_voting_prompt=DEFAULT_MAJORITY_VOTING_PROMPT, - ) - # Run the method to test - output = router.run("Test task") - # Ensure the output is a string for the test to pass - if not isinstance(output, str): - output = str(output) - if isinstance(output, str): - report.add_result( - "Method: run()", - True, - duration=time.time() - start_time, - ) - else: - report.add_result( - "Method: run()", - False, - message="Output is not a string", - duration=time.time() - start_time, - ) - except Exception as e: - report.add_result( - "Method: run()", - False, - message=str(e), - duration=time.time() - start_time, - ) - - -def test_router_batched_run(report): - """Test ReasoningAgentRouter's batched_run() method using test_agents_swarm""" - start_time = time.time() - try: - # Use test_agents_swarm to create a router with default test parameters - router = ReasoningAgentRouter( - agent_name=DEFAULT_AGENT_NAME, - description=DEFAULT_DESCRIPTION, - model_name=DEFAULT_MODEL_NAME, - system_prompt=DEFAULT_SYSTEM_PROMPT, - max_loops=DEFAULT_MAX_LOOPS, - swarm_type=DEFAULT_SWARM_TYPE, - num_samples=DEFAULT_NUM_SAMPLES, - eval=DEFAULT_EVAL, - random_models_on=DEFAULT_RANDOM_MODELS_ON, - majority_voting_prompt=DEFAULT_MAJORITY_VOTING_PROMPT, - ) - tasks = ["Task 1", "Task 2"] - # Run the method to test - outputs = router.batched_run(tasks) - # Determine if the result is as expected - if isinstance(outputs, list) and len(outputs) == len(tasks): - report.add_result( - "Method: batched_run()", - True, - duration=time.time() - start_time, - ) - else: - report.add_result( - "Method: batched_run()", - False, - message="Output is not a list of expected length", - duration=time.time() - start_time, - ) - except Exception as e: - report.add_result( - "Method: batched_run()", - False, - message=str(e), - duration=time.time() - start_time, - ) - - -def test_swarm(report): - """ - Run all ReasoningAgentRouter parameter and method tests, log results to report, and print summary. - """ - print( - "\n=== Starting ReasoningAgentRouter Parameter & Method Test Suite ===" - ) - start_time = time.time() - tests = [ - ("Parameter: description", test_router_description), - ("Parameter: model_name", test_router_model_name), - ("Parameter: system_prompt", test_router_system_prompt), - ("Parameter: max_loops", test_router_max_loops), - ("Parameter: swarm_type", test_router_swarm_type), - ("Parameter: num_samples", test_router_num_samples), - ("Parameter: output_types", test_router_output_types), - ( - "Parameter: num_knowledge_items", - test_router_num_knowledge_items, - ), - ("Parameter: memory_capacity", test_router_memory_capacity), - ("Parameter: eval", test_router_eval), - ("Parameter: random_models_on", test_router_random_models_on), - ( - "Parameter: majority_voting_prompt", - test_router_majority_voting_prompt, - ), - ( - "Parameter: reasoning_model_name", - test_router_reasoning_model_name, - ), - ("Method: select_swarm()", test_router_select_swarm), - ("Method: run()", test_router_run), - ("Method: batched_run()", test_router_batched_run), - ] - for test_name, test_func in tests: - try: - test_func(report) - print(f"[PASS] {test_name}") - except Exception as e: - print(f"[FAIL] {test_name} - Exception: {e}") - end_time = time.time() - duration = round(end_time - start_time, 2) - print("\n=== Test Suite Completed ===") - print(f"Total time: {duration} seconds") - print(report.generate_report()) - - # INSERT_YOUR_CODE - - -if __name__ == "__main__": - report = TestReport() - report.start() - test_swarm(report) - report.end() diff --git a/tests/structs/test_recursive_workflow.py b/tests/structs/test_recursive_workflow.py deleted file mode 100644 index 75cd5145..00000000 --- a/tests/structs/test_recursive_workflow.py +++ /dev/null @@ -1,74 +0,0 @@ -from unittest.mock import Mock, create_autospec - -import pytest - -from swarm_models import OpenAIChat -from swarms.structs import RecursiveWorkflow, Task - - -def test_add(): - workflow = RecursiveWorkflow(stop_token="") - task = Mock(spec=Task) - workflow.add(task) - assert task in workflow.tasks - - -def test_run(): - workflow = RecursiveWorkflow(stop_token="") - agent1 = create_autospec(OpenAIChat) - agent2 = create_autospec(OpenAIChat) - task1 = Task("What's the weather in miami", agent1) - task2 = Task("What's the weather in miami", agent2) - workflow.add(task1) - workflow.add(task2) - - agent1.execute.return_value = "Not done" - agent2.execute.return_value = "" - - workflow.run() - - assert agent1.execute.call_count >= 1 - assert agent2.execute.call_count == 1 - - -def test_run_no_tasks(): - workflow = RecursiveWorkflow(stop_token="") - # No tasks are added to the workflow - # This should not raise any errors - workflow.run() - - -def test_run_stop_token_not_in_result(): - workflow = RecursiveWorkflow(stop_token="") - agent = create_autospec(OpenAIChat) - task = Task("What's the weather in miami", agent) - workflow.add(task) - - agent.execute.return_value = "Not done" - - # If the stop token is never found in the result, the workflow could run forever. - # To prevent this, we'll set a maximum number of iterations. - max_iterations = 1000 - for _ in range(max_iterations): - try: - workflow.run() - except RecursionError: - pytest.fail( - "RecursiveWorkflow.run caused a RecursionError" - ) - - assert agent.execute.call_count == max_iterations - - -def test_run_stop_token_in_result(): - workflow = RecursiveWorkflow(stop_token="") - agent = create_autospec(OpenAIChat) - task = Task("What's the weather in miami", agent) - workflow.add(task) - - agent.execute.return_value = "" - - workflow.run() - - # If the stop token is found in the result, the workflow should stop running the task. - assert agent.execute.call_count == 1 diff --git a/tests/structs/test_sequential_workflow.py b/tests/structs/test_sequential_workflow.py index 1327d0ae..6d8f74a1 100644 --- a/tests/structs/test_sequential_workflow.py +++ b/tests/structs/test_sequential_workflow.py @@ -1,65 +1,6 @@ -import asyncio -import os -from unittest.mock import patch - import pytest -from swarm_models import OpenAIChat -from swarms.structs.agent import Agent -from swarms.structs.sequential_workflow import ( - SequentialWorkflow, - Task, -) - -# Mock the OpenAI API key using environment variables -os.environ["OPENAI_API_KEY"] = "mocked_api_key" - - -# Mock OpenAIChat class for testing -class MockOpenAIChat: - def __init__(self, *args, **kwargs): - pass - - def run(self, *args, **kwargs): - return "Mocked result" - - -# Mock Agent class for testing -class MockAgent: - def __init__(self, *args, **kwargs): - pass - - def run(self, *args, **kwargs): - return "Mocked result" - - -# Mock SequentialWorkflow class for testing -class MockSequentialWorkflow: - def __init__(self, *args, **kwargs): - pass - - def add(self, *args, **kwargs): - pass - - def run(self): - pass - - -# Test Task class -def test_task_initialization(): - description = "Sample Task" - agent = MockOpenAIChat() - task = Task(description=description, agent=agent) - assert task.description == description - assert task.agent == agent - - -def test_task_execute(): - description = "Sample Task" - agent = MockOpenAIChat() - task = Task(description=description, agent=agent) - task.run() - assert task.result == "Mocked result" +from swarms import Agent, SequentialWorkflow # Test SequentialWorkflow class @@ -77,263 +18,297 @@ def test_sequential_workflow_initialization(): assert workflow.dashboard is False -def test_sequential_workflow_add_task(): - workflow = SequentialWorkflow() - task_description = "Sample Task" - task_flow = MockOpenAIChat() - workflow.add(task_description, task_flow) - assert len(workflow.tasks) == 1 - assert workflow.tasks[0].description == task_description - assert workflow.tasks[0].agent == task_flow - - -def test_sequential_workflow_reset_workflow(): - workflow = SequentialWorkflow() - task_description = "Sample Task" - task_flow = MockOpenAIChat() - workflow.add(task_description, task_flow) - workflow.reset_workflow() - assert workflow.tasks[0].result is None - - -def test_sequential_workflow_get_task_results(): - workflow = SequentialWorkflow() - task_description = "Sample Task" - task_flow = MockOpenAIChat() - workflow.add(task_description, task_flow) - workflow.run() - results = workflow.get_task_results() - assert len(results) == 1 - assert task_description in results - assert results[task_description] == "Mocked result" - - -def test_sequential_workflow_remove_task(): - workflow = SequentialWorkflow() - task1_description = "Task 1" - task2_description = "Task 2" - task1_flow = MockOpenAIChat() - task2_flow = MockOpenAIChat() - workflow.add(task1_description, task1_flow) - workflow.add(task2_description, task2_flow) - workflow.remove_task(task1_description) - assert len(workflow.tasks) == 1 - assert workflow.tasks[0].description == task2_description - - -def test_sequential_workflow_update_task(): - workflow = SequentialWorkflow() - task_description = "Sample Task" - task_flow = MockOpenAIChat() - workflow.add(task_description, task_flow) - workflow.update_task(task_description, max_tokens=1000) - assert workflow.tasks[0].kwargs["max_tokens"] == 1000 - - -def test_sequential_workflow_save_workflow_state(): - workflow = SequentialWorkflow() - task_description = "Sample Task" - task_flow = MockOpenAIChat() - workflow.add(task_description, task_flow) - workflow.save_workflow_state("test_state.json") - assert os.path.exists("test_state.json") - os.remove("test_state.json") - +def test_sequential_workflow_initialization_with_agents(): + """Test SequentialWorkflow initialization with agents""" + agent1 = Agent( + agent_name="Agent-1", + agent_description="First test agent", + model_name="gpt-4o", + max_loops=1, + ) + agent2 = Agent( + agent_name="Agent-2", + agent_description="Second test agent", + model_name="gpt-4o", + max_loops=1, + ) -def test_sequential_workflow_load_workflow_state(): - workflow = SequentialWorkflow() - task_description = "Sample Task" - task_flow = MockOpenAIChat() - workflow.add(task_description, task_flow) - workflow.save_workflow_state("test_state.json") - workflow.load_workflow_state("test_state.json") - assert len(workflow.tasks) == 1 - assert workflow.tasks[0].description == task_description - os.remove("test_state.json") + workflow = SequentialWorkflow( + name="Test-Workflow", + description="Test workflow with multiple agents", + agents=[agent1, agent2], + max_loops=1, + ) + assert isinstance(workflow, SequentialWorkflow) + assert workflow.name == "Test-Workflow" + assert ( + workflow.description == "Test workflow with multiple agents" + ) + assert len(workflow.agents) == 2 + assert workflow.agents[0] == agent1 + assert workflow.agents[1] == agent2 + assert workflow.max_loops == 1 -def test_sequential_workflow_run(): - workflow = SequentialWorkflow() - task_description = "Sample Task" - task_flow = MockOpenAIChat() - workflow.add(task_description, task_flow) - workflow.run() - assert workflow.tasks[0].result == "Mocked result" +def test_sequential_workflow_multi_agent_execution(): + """Test SequentialWorkflow execution with multiple agents""" + agent1 = Agent( + agent_name="Research-Agent", + agent_description="Agent for research tasks", + model_name="gpt-4o", + max_loops=1, + ) + agent2 = Agent( + agent_name="Analysis-Agent", + agent_description="Agent for analyzing research results", + model_name="gpt-4o", + max_loops=1, + ) + agent3 = Agent( + agent_name="Summary-Agent", + agent_description="Agent for summarizing findings", + model_name="gpt-4o", + max_loops=1, + ) -def test_sequential_workflow_workflow_bootup(capfd): - workflow = SequentialWorkflow() - workflow.workflow_bootup() - out, _ = capfd.readouterr() - assert "Sequential Workflow Initializing..." in out + workflow = SequentialWorkflow( + name="Multi-Agent-Research-Workflow", + description="Workflow for comprehensive research, analysis, and summarization", + agents=[agent1, agent2, agent3], + max_loops=1, + ) + # Test that the workflow executes successfully + result = workflow.run( + "Analyze the impact of renewable energy on climate change" + ) + assert result is not None + # SequentialWorkflow may return different types based on output_type, just ensure it's not None -def test_sequential_workflow_workflow_dashboard(capfd): - workflow = SequentialWorkflow() - workflow.workflow_dashboard() - out, _ = capfd.readouterr() - assert "Sequential Workflow Dashboard" in out +def test_sequential_workflow_batched_execution(): + """Test batched execution of SequentialWorkflow""" + agent1 = Agent( + agent_name="Data-Collector", + agent_description="Agent for collecting data", + model_name="gpt-4o", + max_loops=1, + ) + agent2 = Agent( + agent_name="Data-Processor", + agent_description="Agent for processing collected data", + model_name="gpt-4o", + max_loops=1, + ) -# Mock Agent class for async testing -class MockAsyncAgent: - def __init__(self, *args, **kwargs): - pass + workflow = SequentialWorkflow( + name="Batched-Processing-Workflow", + agents=[agent1, agent2], + max_loops=1, + ) - async def arun(self, *args, **kwargs): - return "Mocked result" + # Test batched execution + tasks = [ + "Analyze solar energy trends", + "Evaluate wind power efficiency", + "Compare renewable energy sources", + ] + results = workflow.run_batched(tasks) + assert results is not None + # run_batched returns a list of results + assert isinstance(results, list) + assert len(results) == 3 -# Test async execution in SequentialWorkflow @pytest.mark.asyncio -async def test_sequential_workflow_arun(): - workflow = SequentialWorkflow() - task_description = "Sample Task" - task_flow = MockAsyncAgent() - workflow.add(task_description, task_flow) - await workflow.arun() - assert workflow.tasks[0].result == "Mocked result" - - -def test_real_world_usage_with_openai_key(): - # Initialize the language model - llm = OpenAIChat() - assert isinstance(llm, OpenAIChat) - - -def test_real_world_usage_with_flow_and_openai_key(): - # Initialize a agent with the language model - agent = Agent(llm=OpenAIChat()) - assert isinstance(agent, Agent) - +async def test_sequential_workflow_async_execution(): + """Test async execution of SequentialWorkflow""" + agent1 = Agent( + agent_name="Async-Research-Agent", + agent_description="Agent for async research tasks", + model_name="gpt-4o", + max_loops=1, + ) + agent2 = Agent( + agent_name="Async-Analysis-Agent", + agent_description="Agent for async analysis", + model_name="gpt-4o", + max_loops=1, + ) -def test_real_world_usage_with_sequential_workflow(): - # Initialize a sequential workflow - workflow = SequentialWorkflow() - assert isinstance(workflow, SequentialWorkflow) + workflow = SequentialWorkflow( + name="Async-Workflow", + agents=[agent1, agent2], + max_loops=1, + ) + # Test async execution + result = await workflow.run_async("Analyze AI trends in 2024") + assert result is not None -def test_real_world_usage_add_tasks(): - # Create a sequential workflow and add tasks - workflow = SequentialWorkflow() - task1_description = "Task 1" - task2_description = "Task 2" - task1_flow = OpenAIChat() - task2_flow = OpenAIChat() - workflow.add(task1_description, task1_flow) - workflow.add(task2_description, task2_flow) - assert len(workflow.tasks) == 2 - assert workflow.tasks[0].description == task1_description - assert workflow.tasks[1].description == task2_description - - -def test_real_world_usage_run_workflow(): - # Create a sequential workflow, add a task, and run the workflow - workflow = SequentialWorkflow() - task_description = "Sample Task" - task_flow = OpenAIChat() - workflow.add(task_description, task_flow) - workflow.run() - assert workflow.tasks[0].result is not None +@pytest.mark.asyncio +async def test_sequential_workflow_concurrent_execution(): + """Test concurrent execution of SequentialWorkflow""" + agent1 = Agent( + agent_name="Concurrent-Research-Agent", + agent_description="Agent for concurrent research", + model_name="gpt-4o", + max_loops=1, + ) + agent2 = Agent( + agent_name="Concurrent-Analysis-Agent", + agent_description="Agent for concurrent analysis", + model_name="gpt-4o", + max_loops=1, + ) + agent3 = Agent( + agent_name="Concurrent-Summary-Agent", + agent_description="Agent for concurrent summarization", + model_name="gpt-4o", + max_loops=1, + ) -def test_real_world_usage_dashboard_display(): - # Create a sequential workflow, add tasks, and display the dashboard - workflow = SequentialWorkflow() - task1_description = "Task 1" - task2_description = "Task 2" - task1_flow = OpenAIChat() - task2_flow = OpenAIChat() - workflow.add(task1_description, task1_flow) - workflow.add(task2_description, task2_flow) - with patch("builtins.print") as mock_print: - workflow.workflow_dashboard() - mock_print.assert_called() - - -def test_real_world_usage_async_execution(): - # Create a sequential workflow, add an async task, and run the workflow asynchronously - workflow = SequentialWorkflow() - task_description = "Sample Task" - async_task_flow = OpenAIChat() + workflow = SequentialWorkflow( + name="Concurrent-Workflow", + agents=[agent1, agent2, agent3], + max_loops=1, + ) - async def async_run_workflow(): - await workflow.arun() + # Test concurrent execution + tasks = [ + "Research quantum computing advances", + "Analyze blockchain technology trends", + "Evaluate machine learning applications", + ] + results = await workflow.run_concurrent(tasks) + assert results is not None + # run_concurrent returns a list of results + assert isinstance(results, list) + assert len(results) == 3 + + +def test_sequential_workflow_with_multi_agent_collaboration(): + """Test SequentialWorkflow with multi-agent collaboration prompts""" + agent1 = Agent( + agent_name="Market-Research-Agent", + agent_description="Agent for market research", + model_name="gpt-4o", + max_loops=1, + ) + agent2 = Agent( + agent_name="Competitive-Analysis-Agent", + agent_description="Agent for competitive analysis", + model_name="gpt-4o", + max_loops=1, + ) + agent3 = Agent( + agent_name="Strategy-Development-Agent", + agent_description="Agent for developing business strategies", + model_name="gpt-4o", + max_loops=1, + ) - workflow.add(task_description, async_task_flow) - asyncio.run(async_run_workflow()) - assert workflow.tasks[0].result is not None + workflow = SequentialWorkflow( + name="Business-Strategy-Workflow", + description="Comprehensive business strategy development workflow", + agents=[agent1, agent2, agent3], + max_loops=1, + multi_agent_collab_prompt=True, + ) + # Test that collaboration prompt is added + assert agent1.system_prompt is not None + assert agent2.system_prompt is not None + assert agent3.system_prompt is not None -def test_real_world_usage_multiple_loops(): - # Create a sequential workflow with multiple loops, add a task, and run the workflow - workflow = SequentialWorkflow(max_loops=3) - task_description = "Sample Task" - task_flow = OpenAIChat() - workflow.add(task_description, task_flow) - workflow.run() - assert workflow.tasks[0].result is not None + # Test execution + result = workflow.run( + "Develop a business strategy for entering the AI market" + ) + assert result is not None + + +def test_sequential_workflow_error_handling(): + """Test SequentialWorkflow error handling""" + # Test with invalid agents list + with pytest.raises( + ValueError, match="Agents list cannot be None or empty" + ): + SequentialWorkflow(agents=None) + + with pytest.raises( + ValueError, match="Agents list cannot be None or empty" + ): + SequentialWorkflow(agents=[]) + + # Test with zero max_loops + with pytest.raises(ValueError, match="max_loops cannot be 0"): + agent1 = Agent( + agent_name="Test-Agent", + agent_description="Test agent", + model_name="gpt-4o", + max_loops=1, + ) + SequentialWorkflow(agents=[agent1], max_loops=0) + + +def test_sequential_workflow_agent_names_extraction(): + """Test that SequentialWorkflow properly extracts agent names for flow""" + agent1 = Agent( + agent_name="Alpha-Agent", + agent_description="First agent", + model_name="gpt-4o", + max_loops=1, + ) + agent2 = Agent( + agent_name="Beta-Agent", + agent_description="Second agent", + model_name="gpt-4o", + max_loops=1, + ) + agent3 = Agent( + agent_name="Gamma-Agent", + agent_description="Third agent", + model_name="gpt-4o", + max_loops=1, + ) + workflow = SequentialWorkflow( + name="Test-Flow-Workflow", + agents=[agent1, agent2, agent3], + max_loops=1, + ) -def test_real_world_usage_autosave_state(): - # Create a sequential workflow with autosave, add a task, run the workflow, and check if state is saved - workflow = SequentialWorkflow(autosave=True) - task_description = "Sample Task" - task_flow = OpenAIChat() - workflow.add(task_description, task_flow) - workflow.run() - assert workflow.tasks[0].result is not None - assert os.path.exists("sequential_workflow_state.json") - os.remove("sequential_workflow_state.json") + # Test flow string generation + expected_flow = "Alpha-Agent -> Beta-Agent -> Gamma-Agent" + assert workflow.flow == expected_flow -def test_real_world_usage_load_state(): - # Create a sequential workflow, add a task, save state, load state, and run the workflow - workflow = SequentialWorkflow() - task_description = "Sample Task" - task_flow = OpenAIChat() - workflow.add(task_description, task_flow) - workflow.run() - workflow.save_workflow_state("test_state.json") - workflow.load_workflow_state("test_state.json") - workflow.run() - assert workflow.tasks[0].result is not None - os.remove("test_state.json") - - -def test_real_world_usage_update_task_args(): - # Create a sequential workflow, add a task, and update task arguments - workflow = SequentialWorkflow() - task_description = "Sample Task" - task_flow = OpenAIChat() - workflow.add(task_description, task_flow) - workflow.update_task(task_description, max_tokens=1000) - assert workflow.tasks[0].kwargs["max_tokens"] == 1000 +def test_sequential_workflow_team_awareness(): + """Test SequentialWorkflow with team awareness enabled""" + agent1 = Agent( + agent_name="Team-Member-1", + agent_description="First team member", + model_name="gpt-4o", + max_loops=1, + ) + agent2 = Agent( + agent_name="Team-Member-2", + agent_description="Second team member", + model_name="gpt-4o", + max_loops=1, + ) + workflow = SequentialWorkflow( + name="Team-Aware-Workflow", + description="Workflow with team awareness", + agents=[agent1, agent2], + max_loops=1, + team_awareness=True, + ) -def test_real_world_usage_remove_task(): - # Create a sequential workflow, add tasks, remove a task, and run the workflow - workflow = SequentialWorkflow() - task1_description = "Task 1" - task2_description = "Task 2" - task1_flow = OpenAIChat() - task2_flow = OpenAIChat() - workflow.add(task1_description, task1_flow) - workflow.add(task2_description, task2_flow) - workflow.remove_task(task1_description) - workflow.run() - assert len(workflow.tasks) == 1 - assert workflow.tasks[0].description == task2_description - - -def test_real_world_usage_with_environment_variables(): - # Ensure that the OpenAI API key is set using environment variables - assert "OPENAI_API_KEY" in os.environ - assert os.environ["OPENAI_API_KEY"] == "mocked_api_key" - del os.environ["OPENAI_API_KEY"] # Clean up after the test - - -def test_real_world_usage_no_openai_key(): - # Ensure that an exception is raised when the OpenAI API key is not set - with pytest.raises(ValueError): - OpenAIChat() # API key not provided, should raise an exception + # Test that workflow initializes successfully with team awareness + assert workflow.team_awareness is True + assert len(workflow.agents) == 2 diff --git a/tests/structs/test_swarm_router.py b/tests/structs/test_swarm_router.py new file mode 100644 index 00000000..2df7687d --- /dev/null +++ b/tests/structs/test_swarm_router.py @@ -0,0 +1,898 @@ +from unittest.mock import Mock, patch + +import pytest + +from swarms.structs.agent import Agent +from swarms.structs.swarm_router import ( + SwarmRouter, + SwarmRouterConfig, + SwarmRouterConfigError, + SwarmRouterRunError, +) + + +@pytest.fixture +def sample_agents(): + """Create sample agents for testing.""" + return [ + Agent( + agent_name="ResearchAgent", + agent_description="Specializes in researching topics", + system_prompt="You are a research specialist.", + max_loops=1, + ), + Agent( + agent_name="CodeAgent", + agent_description="Expert in coding", + system_prompt="You are a coding expert.", + max_loops=1, + ), + ] + + +def test_default_initialization(): + """Test SwarmRouter with default parameters.""" + router = SwarmRouter() + + assert router.name == "swarm-router" + assert ( + router.description == "Routes your task to the desired swarm" + ) + assert router.max_loops == 1 + assert router.agents == [] + assert router.swarm_type == "SequentialWorkflow" + assert router.autosave is False + assert router.return_json is False + assert router.auto_generate_prompts is False + assert router.shared_memory_system is None + assert router.rules is None + assert router.documents == [] + assert router.output_type == "dict-all-except-first" + assert router.verbose is False + assert router.telemetry_enabled is False + + +def test_custom_initialization(sample_agents): + """Test SwarmRouter with custom parameters.""" + router = SwarmRouter( + name="test-router", + description="Test router description", + max_loops=3, + agents=sample_agents, + swarm_type="ConcurrentWorkflow", + autosave=True, + return_json=True, + auto_generate_prompts=True, + rules="Test rules", + documents=["doc1.txt", "doc2.txt"], + output_type="json", + verbose=True, + telemetry_enabled=True, + ) + + assert router.name == "test-router" + assert router.description == "Test router description" + assert router.max_loops == 3 + assert router.agents == sample_agents + assert router.swarm_type == "ConcurrentWorkflow" + assert router.autosave is True + assert router.return_json is True + assert router.auto_generate_prompts is True + assert router.rules == "Test rules" + assert router.documents == ["doc1.txt", "doc2.txt"] + assert router.output_type == "json" + assert router.verbose is True + assert router.telemetry_enabled is True + + +def test_initialization_with_heavy_swarm_config(sample_agents): + """Test SwarmRouter with HeavySwarm specific configuration.""" + router = SwarmRouter( + agents=sample_agents, + swarm_type="HeavySwarm", + heavy_swarm_loops_per_agent=2, + heavy_swarm_question_agent_model_name="gpt-4", + heavy_swarm_worker_model_name="gpt-3.5-turbo", + heavy_swarm_swarm_show_output=False, + ) + + assert router.swarm_type == "HeavySwarm" + assert router.heavy_swarm_loops_per_agent == 2 + assert router.heavy_swarm_question_agent_model_name == "gpt-4" + assert router.heavy_swarm_worker_model_name == "gpt-3.5-turbo" + assert router.heavy_swarm_swarm_show_output is False + + +def test_initialization_with_council_judge_config(): + """Test SwarmRouter with CouncilAsAJudge specific configuration.""" + router = SwarmRouter( + swarm_type="CouncilAsAJudge", + council_judge_model_name="gpt-4o", + ) + + assert router.swarm_type == "CouncilAsAJudge" + assert router.council_judge_model_name == "gpt-4o" + + +def test_initialization_with_agent_rearrange_flow(sample_agents): + """Test SwarmRouter with AgentRearrange and flow configuration.""" + flow = "agent1 -> agent2 -> agent1" + router = SwarmRouter( + agents=sample_agents, + swarm_type="AgentRearrange", + rearrange_flow=flow, + ) + + assert router.swarm_type == "AgentRearrange" + assert router.rearrange_flow == flow + + + +def test_invalid_swarm_type(): + """Test error when invalid swarm type is provided.""" + with pytest.raises(ValueError): + SwarmRouter(swarm_type="InvalidSwarmType") + + +def test_no_agents_for_swarm_requiring_agents(): + """Test error when no agents provided for swarm requiring agents.""" + with pytest.raises(SwarmRouterConfigError): + SwarmRouter(swarm_type="SequentialWorkflow", agents=None) + + +def test_no_rearrange_flow_for_agent_rearrange(): + """Test error when no rearrange_flow provided for AgentRearrange.""" + agents = [Agent(agent_name="test", agent_description="test")] + with pytest.raises(SwarmRouterConfigError): + SwarmRouter( + agents=agents, + swarm_type="AgentRearrange", + rearrange_flow=None, + ) + + +def test_zero_max_loops(): + """Test error when max_loops is 0.""" + with pytest.raises(SwarmRouterConfigError): + SwarmRouter(max_loops=0) + + +def test_heavy_swarm_without_agents(): + """Test HeavySwarm can be created without agents.""" + router = SwarmRouter(swarm_type="HeavySwarm", agents=None) + assert router.swarm_type == "HeavySwarm" + + +def test_council_judge_without_agents(): + """Test CouncilAsAJudge can be created without agents.""" + router = SwarmRouter(swarm_type="CouncilAsAJudge", agents=None) + assert router.swarm_type == "CouncilAsAJudge" + + +def test_swarm_factory_initialization(sample_agents): + """Test that swarm factory is properly initialized.""" + router = SwarmRouter(agents=sample_agents) + factory = router._initialize_swarm_factory() + + expected_types = [ + "HeavySwarm", + "AgentRearrange", + "MALT", + "CouncilAsAJudge", + "InteractiveGroupChat", + "HiearchicalSwarm", + "MixtureOfAgents", + "MajorityVoting", + "GroupChat", + "MultiAgentRouter", + "SequentialWorkflow", + "ConcurrentWorkflow", + "BatchedGridWorkflow", + ] + + for swarm_type in expected_types: + assert swarm_type in factory + assert callable(factory[swarm_type]) + + +def test_create_heavy_swarm(sample_agents): + """Test HeavySwarm creation.""" + router = SwarmRouter( + agents=sample_agents, + swarm_type="HeavySwarm", + heavy_swarm_loops_per_agent=2, + heavy_swarm_question_agent_model_name="gpt-4", + heavy_swarm_worker_model_name="gpt-3.5-turbo", + ) + + swarm = router._create_heavy_swarm() + assert swarm is not None + assert hasattr(swarm, "run") + + +def test_create_agent_rearrange(sample_agents): + """Test AgentRearrange creation.""" + router = SwarmRouter( + agents=sample_agents, + swarm_type="AgentRearrange", + rearrange_flow="agent1 -> agent2", + ) + + swarm = router._create_agent_rearrange() + assert swarm is not None + assert hasattr(swarm, "run") + + +def test_create_sequential_workflow(sample_agents): + """Test SequentialWorkflow creation.""" + router = SwarmRouter( + agents=sample_agents, swarm_type="SequentialWorkflow" + ) + + swarm = router._create_sequential_workflow() + assert swarm is not None + assert hasattr(swarm, "run") + + +def test_create_concurrent_workflow(sample_agents): + """Test ConcurrentWorkflow creation.""" + router = SwarmRouter( + agents=sample_agents, swarm_type="ConcurrentWorkflow" + ) + + swarm = router._create_concurrent_workflow() + assert swarm is not None + assert hasattr(swarm, "run") + + +def test_create_group_chat(sample_agents): + """Test GroupChat creation.""" + router = SwarmRouter(agents=sample_agents, swarm_type="GroupChat") + + swarm = router._create_group_chat() + assert swarm is not None + assert hasattr(swarm, "run") + + +def test_create_multi_agent_router(sample_agents): + """Test MultiAgentRouter creation.""" + router = SwarmRouter( + agents=sample_agents, swarm_type="MultiAgentRouter" + ) + + swarm = router._create_multi_agent_router() + assert swarm is not None + assert hasattr(swarm, "run") + + +def test_create_mixture_of_agents(sample_agents): + """Test MixtureOfAgents creation.""" + router = SwarmRouter( + agents=sample_agents, swarm_type="MixtureOfAgents" + ) + + swarm = router._create_mixture_of_agents() + assert swarm is not None + assert hasattr(swarm, "run") + + +def test_create_majority_voting(sample_agents): + """Test MajorityVoting creation.""" + router = SwarmRouter( + agents=sample_agents, swarm_type="MajorityVoting" + ) + + swarm = router._create_majority_voting() + assert swarm is not None + assert hasattr(swarm, "run") + + +def test_create_malt(sample_agents): + """Test MALT creation.""" + router = SwarmRouter(agents=sample_agents, swarm_type="MALT") + + swarm = router._create_malt() + assert swarm is not None + assert hasattr(swarm, "run") + + +def test_create_council_as_judge(): + """Test CouncilAsAJudge creation.""" + router = SwarmRouter(swarm_type="CouncilAsAJudge") + + swarm = router._create_council_as_judge() + assert swarm is not None + assert hasattr(swarm, "run") + + +def test_create_interactive_group_chat(sample_agents): + """Test InteractiveGroupChat creation.""" + router = SwarmRouter( + agents=sample_agents, swarm_type="InteractiveGroupChat" + ) + + swarm = router._create_interactive_group_chat() + assert swarm is not None + assert hasattr(swarm, "run") + + +def test_create_hierarchical_swarm(sample_agents): + """Test HierarchicalSwarm creation.""" + router = SwarmRouter( + agents=sample_agents, swarm_type="HiearchicalSwarm" + ) + + swarm = router._create_hierarchical_swarm() + assert swarm is not None + assert hasattr(swarm, "run") + + +def test_create_batched_grid_workflow(sample_agents): + """Test BatchedGridWorkflow creation.""" + router = SwarmRouter( + agents=sample_agents, swarm_type="BatchedGridWorkflow" + ) + + swarm = router._create_batched_grid_workflow() + assert swarm is not None + assert hasattr(swarm, "run") + + +@pytest.mark.parametrize( + "swarm_type", + [ + "SequentialWorkflow", + "ConcurrentWorkflow", + "GroupChat", + "MultiAgentRouter", + "MixtureOfAgents", + "MajorityVoting", + "MALT", + "CouncilAsAJudge", + "InteractiveGroupChat", + "HiearchicalSwarm", + "BatchedGridWorkflow", + ], +) +def test_swarm_types_execution(sample_agents, swarm_type): + """Test execution of all swarm types with mock LLM.""" + with patch("swarms.structs.agent.LiteLLM") as mock_llm: + # Mock the LLM to return a simple response + mock_llm_instance = Mock() + mock_llm_instance.agenerate.return_value = ( + "Test response from agent" + ) + mock_llm.return_value = mock_llm_instance + + router = SwarmRouter( + agents=sample_agents, + swarm_type=swarm_type, + max_loops=1, + ) + + # Test with a simple task + task = "Write a simple Python function to add two numbers" + + try: + result = router.run(task) + assert ( + result is not None + ), f"Swarm type {swarm_type} returned None result" + except Exception: + # Some swarm types might have specific requirements + if swarm_type in ["AgentRearrange"]: + # AgentRearrange requires rearrange_flow + router = SwarmRouter( + agents=sample_agents, + swarm_type=swarm_type, + rearrange_flow="agent1 -> agent2", + max_loops=1, + ) + result = router.run(task) + assert ( + result is not None + ), f"Swarm type {swarm_type} returned None result" + + +def test_heavy_swarm_execution(): + """Test HeavySwarm execution.""" + with patch( + "swarms.structs.heavy_swarm.HeavySwarm" + ) as mock_heavy_swarm: + mock_instance = Mock() + mock_instance.run.return_value = "HeavySwarm response" + mock_heavy_swarm.return_value = mock_instance + + router = SwarmRouter(swarm_type="HeavySwarm") + + result = router.run("Test task") + assert result is not None + assert result == "HeavySwarm response" + + +def test_agent_rearrange_execution(sample_agents): + """Test AgentRearrange execution with flow.""" + with patch( + "swarms.structs.agent_rearrange.AgentRearrange" + ) as mock_agent_rearrange: + mock_instance = Mock() + mock_instance.run.return_value = "AgentRearrange response" + mock_agent_rearrange.return_value = mock_instance + + router = SwarmRouter( + agents=sample_agents, + swarm_type="AgentRearrange", + rearrange_flow="agent1 -> agent2", + ) + + result = router.run("Test task") + assert result is not None + assert result == "AgentRearrange response" + + +def test_run_method(sample_agents): + """Test basic run method.""" + with patch( + "swarms.structs.sequential_workflow.SequentialWorkflow" + ) as mock_workflow: + mock_instance = Mock() + mock_instance.run.return_value = "Test response" + mock_workflow.return_value = mock_instance + + router = SwarmRouter( + agents=sample_agents, swarm_type="SequentialWorkflow" + ) + + result = router.run("Test task") + assert result is not None + assert result == "Test response" + + +def test_run_with_image(sample_agents): + """Test run method with image input.""" + with patch( + "swarms.structs.sequential_workflow.SequentialWorkflow" + ) as mock_workflow: + mock_instance = Mock() + mock_instance.run.return_value = "Image analysis response" + mock_workflow.return_value = mock_instance + + router = SwarmRouter( + agents=sample_agents, swarm_type="SequentialWorkflow" + ) + + result = router.run( + "Analyze this image", img="test_image.jpg" + ) + assert result is not None + assert result == "Image analysis response" + + +def test_run_with_tasks_list(sample_agents): + """Test run method with tasks list.""" + with patch( + "swarms.structs.batched_grid_workflow.BatchedGridWorkflow" + ) as mock_workflow: + mock_instance = Mock() + mock_instance.run.return_value = "Batch response" + mock_workflow.return_value = mock_instance + + router = SwarmRouter( + agents=sample_agents, swarm_type="BatchedGridWorkflow" + ) + + tasks = ["Task 1", "Task 2", "Task 3"] + result = router.run(tasks=tasks) + assert result is not None + assert result == "Batch response" + + +def test_batch_run_method(sample_agents): + """Test batch_run method.""" + with patch( + "swarms.structs.sequential_workflow.SequentialWorkflow" + ) as mock_workflow: + mock_instance = Mock() + mock_instance.run.return_value = "Batch task response" + mock_workflow.return_value = mock_instance + + router = SwarmRouter( + agents=sample_agents, swarm_type="SequentialWorkflow" + ) + + tasks = ["Task 1", "Task 2", "Task 3"] + results = router.batch_run(tasks) + + assert results is not None + assert len(results) == 3 + assert all(result is not None for result in results) + + +def test_concurrent_run_method(sample_agents): + """Test concurrent_run method.""" + with patch( + "swarms.structs.sequential_workflow.SequentialWorkflow" + ) as mock_workflow: + mock_instance = Mock() + mock_instance.run.return_value = "Concurrent response" + mock_workflow.return_value = mock_instance + + router = SwarmRouter( + agents=sample_agents, swarm_type="SequentialWorkflow" + ) + + result = router.concurrent_run("Test task") + assert result is not None + assert result == "Concurrent response" + + +def test_call_method(sample_agents): + """Test __call__ method.""" + with patch( + "swarms.structs.sequential_workflow.SequentialWorkflow" + ) as mock_workflow: + mock_instance = Mock() + mock_instance.run.return_value = "Call response" + mock_workflow.return_value = mock_instance + + router = SwarmRouter( + agents=sample_agents, swarm_type="SequentialWorkflow" + ) + + result = router("Test task") + assert result is not None + assert result == "Call response" + + +def test_call_with_image(sample_agents): + """Test __call__ method with image.""" + with patch( + "swarms.structs.sequential_workflow.SequentialWorkflow" + ) as mock_workflow: + mock_instance = Mock() + mock_instance.run.return_value = "Image call response" + mock_workflow.return_value = mock_instance + + router = SwarmRouter( + agents=sample_agents, swarm_type="SequentialWorkflow" + ) + + result = router("Test task", img="test.jpg") + assert result is not None + assert result == "Image call response" + + +def test_call_with_images_list(sample_agents): + """Test __call__ method with images list.""" + with patch( + "swarms.structs.sequential_workflow.SequentialWorkflow" + ) as mock_workflow: + mock_instance = Mock() + mock_instance.run.return_value = "Images call response" + mock_workflow.return_value = mock_instance + + router = SwarmRouter( + agents=sample_agents, swarm_type="SequentialWorkflow" + ) + + result = router("Test task", imgs=["test1.jpg", "test2.jpg"]) + assert result is not None + assert result == "Images call response" + + +def test_to_dict_method(sample_agents): + """Test to_dict method serialization.""" + router = SwarmRouter( + agents=sample_agents, + name="test-router", + description="Test description", + swarm_type="SequentialWorkflow", + ) + + result_dict = router.to_dict() + + assert isinstance(result_dict, dict) + assert result_dict["name"] == "test-router" + assert result_dict["description"] == "Test description" + assert result_dict["swarm_type"] == "SequentialWorkflow" + assert "agents" in result_dict + + +def test_activate_ape(sample_agents): + """Test activate_ape method.""" + router = SwarmRouter( + agents=sample_agents, + auto_generate_prompts=True, + ) + + # Mock the auto_generate_prompt attribute + for agent in router.agents: + agent.auto_generate_prompt = False + + router.activate_ape() + + # Check that APE was activated for agents that support it + for agent in router.agents: + if hasattr(agent, "auto_generate_prompt"): + assert agent.auto_generate_prompt is True + + +def test_handle_rules(sample_agents): + """Test handle_rules method.""" + rules = "Always be helpful and accurate." + router = SwarmRouter( + agents=sample_agents, + rules=rules, + ) + + original_prompts = [ + agent.system_prompt for agent in router.agents + ] + router.handle_rules() + + # Check that rules were added to system prompts + for i, agent in enumerate(router.agents): + assert rules in agent.system_prompt + assert ( + agent.system_prompt + == original_prompts[i] + f"### Swarm Rules ### {rules}" + ) + + + +def test_update_system_prompt_for_agent_in_swarm(sample_agents): + """Test update_system_prompt_for_agent_in_swarm method.""" + router = SwarmRouter( + agents=sample_agents, + multi_agent_collab_prompt=True, + ) + + original_prompts = [ + agent.system_prompt for agent in router.agents + ] + router.update_system_prompt_for_agent_in_swarm() + + # Check that collaboration prompt was added + for i, agent in enumerate(router.agents): + assert len(agent.system_prompt) > len(original_prompts[i]) + + +def test_agent_config(sample_agents): + """Test agent_config method.""" + router = SwarmRouter(agents=sample_agents) + + config = router.agent_config() + + assert isinstance(config, dict) + assert len(config) == len(sample_agents) + + for agent in sample_agents: + assert agent.agent_name in config + + +def test_fetch_message_history_as_string(sample_agents): + """Test fetch_message_history_as_string method.""" + router = SwarmRouter(agents=sample_agents) + + # Mock the swarm and conversation + mock_conversation = Mock() + mock_conversation.return_all_except_first_string.return_value = ( + "Test history" + ) + router.swarm = Mock() + router.swarm.conversation = mock_conversation + + result = router.fetch_message_history_as_string() + assert result == "Test history" + + +def test_fetch_message_history_as_string_error(sample_agents): + """Test fetch_message_history_as_string method with error.""" + router = SwarmRouter(agents=sample_agents) + + # Mock the swarm to raise an exception + router.swarm = Mock() + router.swarm.conversation.return_all_except_first_string.side_effect = Exception( + "Test error" + ) + + result = router.fetch_message_history_as_string() + assert result is None + + +def test_swarm_creation_error(): + """Test error handling when swarm creation fails.""" + router = SwarmRouter(swarm_type="SequentialWorkflow", agents=None) + + with pytest.raises(SwarmRouterConfigError): + router.run("Test task") + + +def test_run_error_handling(): + """Test error handling during task execution.""" + agents = [Agent(agent_name="test", agent_description="test")] + + with patch( + "swarms.structs.sequential_workflow.SequentialWorkflow" + ) as mock_workflow: + mock_instance = Mock() + mock_instance.run.side_effect = Exception( + "Test execution error" + ) + mock_workflow.return_value = mock_instance + + router = SwarmRouter( + agents=agents, swarm_type="SequentialWorkflow" + ) + + with pytest.raises(SwarmRouterRunError): + router.run("Test task") + + +def test_batch_run_error_handling(): + """Test error handling during batch execution.""" + agents = [Agent(agent_name="test", agent_description="test")] + + with patch( + "swarms.structs.sequential_workflow.SequentialWorkflow" + ) as mock_workflow: + mock_instance = Mock() + mock_instance.run.side_effect = Exception("Test batch error") + mock_workflow.return_value = mock_instance + + router = SwarmRouter( + agents=agents, swarm_type="SequentialWorkflow" + ) + + with pytest.raises(RuntimeError): + router.batch_run(["Task 1", "Task 2"]) + + +def test_invalid_swarm_type_error(): + """Test error when creating swarm with invalid type.""" + router = SwarmRouter(swarm_type="SequentialWorkflow") + + # Manually set an invalid swarm type to test the factory + router.swarm_type = "InvalidType" + + with pytest.raises(ValueError): + router._create_swarm("Test task") + + +def test_swarm_caching(): + """Test that swarms are cached for performance.""" + agents = [Agent(agent_name="test", agent_description="test")] + router = SwarmRouter( + agents=agents, swarm_type="SequentialWorkflow" + ) + + # Create swarm first time + swarm1 = router._create_swarm("Task 1") + + # Create swarm second time with same parameters + swarm2 = router._create_swarm("Task 1") + + # Should be the same cached instance + assert swarm1 is swarm2 + + +def test_swarm_cache_different_parameters(): + """Test that different parameters create different cached swarms.""" + agents = [Agent(agent_name="test", agent_description="test")] + router = SwarmRouter( + agents=agents, swarm_type="SequentialWorkflow" + ) + + # Create swarms with different parameters + swarm1 = router._create_swarm("Task 1", param1="value1") + swarm2 = router._create_swarm("Task 2", param1="value2") + + # Should be different instances + assert swarm1 is not swarm2 + + +@pytest.mark.parametrize( + "output_type", + [ + "string", + "str", + "list", + "json", + "dict", + "yaml", + "xml", + "dict-all-except-first", + "dict-first", + "list-all-except-first", + ], +) +def test_output_types(sample_agents, output_type): + """Test different output types.""" + with patch( + "swarms.structs.sequential_workflow.SequentialWorkflow" + ) as mock_workflow: + mock_instance = Mock() + mock_instance.run.return_value = f"Response for {output_type}" + mock_workflow.return_value = mock_instance + + router = SwarmRouter( + agents=sample_agents, + swarm_type="SequentialWorkflow", + output_type=output_type, + ) + + result = router.run("Test task") + assert result is not None + assert result == f"Response for {output_type}" + + +def test_full_workflow_with_sequential_workflow(): + """Test complete workflow with SequentialWorkflow.""" + agents = [ + Agent( + agent_name="ResearchAgent", + agent_description="Research specialist", + system_prompt="You are a research specialist.", + max_loops=1, + ), + Agent( + agent_name="CodeAgent", + agent_description="Code expert", + system_prompt="You are a code expert.", + max_loops=1, + ), + ] + + with patch( + "swarms.structs.sequential_workflow.SequentialWorkflow" + ) as mock_workflow: + mock_instance = Mock() + mock_instance.run.return_value = "Complete workflow response" + mock_workflow.return_value = mock_instance + + router = SwarmRouter( + agents=agents, + swarm_type="SequentialWorkflow", + max_loops=2, + rules="Always provide accurate information", + multi_agent_collab_prompt=True, + verbose=True, + ) + + # Test various methods + result = router.run("Research and code a Python function") + assert result is not None + + batch_results = router.batch_run(["Task 1", "Task 2"]) + assert batch_results is not None + assert len(batch_results) == 2 + + concurrent_result = router.concurrent_run("Concurrent task") + assert concurrent_result is not None + + # Test serialization + config_dict = router.to_dict() + assert isinstance(config_dict, dict) + assert config_dict["swarm_type"] == "SequentialWorkflow" + + +def test_swarm_router_config_model(): + """Test SwarmRouterConfig model.""" + config = SwarmRouterConfig( + name="test-config", + description="Test configuration", + swarm_type="SequentialWorkflow", + task="Test task", + multi_agent_collab_prompt=True, + ) + + assert config.name == "test-config" + assert config.description == "Test configuration" + assert config.swarm_type == "SequentialWorkflow" + assert config.task == "Test task" + assert config.multi_agent_collab_prompt is True + + + +if __name__ == "__main__": + pytest.main([__file__, "-v"]) diff --git a/tests/structs/test_yaml_model.py b/tests/structs/test_yaml_model.py deleted file mode 100644 index 24684612..00000000 --- a/tests/structs/test_yaml_model.py +++ /dev/null @@ -1,97 +0,0 @@ -from pydantic import BaseModel -from dataclasses import dataclass -from swarms import ( - create_yaml_schema_from_dict, - YamlModel, -) - - -@dataclass -class TestDataClass: - name: str - age: int - is_active: bool - - -class TestPydanticModel(BaseModel): - name: str - age: int - is_active: bool - - -def test_create_yaml_schema_from_dict_dataclass(): - data = {"name": "Alice", "age": 30, "is_active": True} - result = create_yaml_schema_from_dict(data, TestDataClass) - expected_result = """ - name: - type: str - default: None - description: No description provided - age: - type: int - default: None - description: No description provided - is_active: - type: bool - default: None - description: No description provided - """ - assert result == expected_result - - -def test_create_yaml_schema_from_dict_pydantic(): - data = {"name": "Alice", "age": 30, "is_active": True} - result = create_yaml_schema_from_dict(data, TestPydanticModel) - expected_result = """ - name: - type: str - default: None - description: No description provided - age: - type: int - default: None - description: No description provided - is_active: - type: bool - default: None - description: No description provided - """ - assert result == expected_result - - -def test_create_yaml_schema_from_dict_regular_class(): - class TestRegularClass: - def __init__(self, name, age, is_active): - self.name = name - self.age = age - self.is_active = is_active - - data = {"name": "Alice", "age": 30, "is_active": True} - result = create_yaml_schema_from_dict(data, TestRegularClass) - expected_result = """ - name: - type: str - description: No description provided - age: - type: int - description: No description provided - is_active: - type: bool - description: No description provided - """ - assert result == expected_result - - -class User(YamlModel): - name: str - age: int - is_active: bool - - -def test_yaml_model(): - # Create an instance of the User model - user = User(name="Alice", age=30, is_active=True) - - assert user.name == "Alice" - assert user.age == 30 - assert user.is_active is True diff --git a/tests/tools/test_base_tool.py b/tests/tools/test_base_tool.py index 1b6cdeeb..e07ba7dc 100644 --- a/tests/tools/test_base_tool.py +++ b/tests/tools/test_base_tool.py @@ -1,6 +1,7 @@ -from pydantic import BaseModel -from typing import Optional import json +from typing import Optional + +from pydantic import BaseModel from swarms.tools.base_tool import BaseTool diff --git a/tests/utils/test_acompletions.py b/tests/utils/test_acompletions.py deleted file mode 100644 index 9a318cdd..00000000 --- a/tests/utils/test_acompletions.py +++ /dev/null @@ -1,62 +0,0 @@ -from litellm import completion -from dotenv import load_dotenv - -load_dotenv() - -tools = [ - { - "type": "function", - "function": { - "name": "get_current_weather", - "description": "Retrieve detailed current weather information for a specified location, including temperature, humidity, wind speed, and atmospheric conditions.", - "parameters": { - "type": "object", - "properties": { - "location": { - "type": "string", - "description": "The city and state, e.g. San Francisco, CA, or a specific geographic coordinate in the format 'latitude,longitude'.", - }, - "unit": { - "type": "string", - "enum": ["celsius", "fahrenheit", "kelvin"], - "description": "The unit of temperature measurement to be used in the response.", - }, - "include_forecast": { - "type": "boolean", - "description": "Indicates whether to include a short-term weather forecast along with the current conditions.", - }, - "time": { - "type": "string", - "format": "date-time", - "description": "Optional parameter to specify the time for which the weather data is requested, in ISO 8601 format.", - }, - }, - "required": [ - "location", - "unit", - "include_forecast", - "time", - ], - }, - }, - } -] - -messages = [ - { - "role": "user", - "content": "What's the weather like in Boston today?", - } -] - - -response = completion( - model="gpt-4o-mini", - messages=messages, - tools=tools, - tool_choice="auto", - parallel_tool_calls=True, -) - -print(response.choices[0].message.tool_calls[0].function.arguments) -print(response.choices[0].message) diff --git a/tests/utils/test_auto_check_download.py b/tests/utils/test_auto_check_download.py deleted file mode 100644 index ac8fee3d..00000000 --- a/tests/utils/test_auto_check_download.py +++ /dev/null @@ -1,104 +0,0 @@ -from swarms.utils.auto_download_check_packages import ( - auto_check_and_download_package, - check_and_install_package, -) - - -def test_check_and_install_package_pip(): - result = check_and_install_package("numpy", package_manager="pip") - print(f"Test result for 'numpy' installation using pip: {result}") - assert result, "Failed to install or verify 'numpy' using pip" - - -def test_check_and_install_package_conda(): - result = check_and_install_package( - "numpy", package_manager="conda" - ) - print( - f"Test result for 'numpy' installation using conda: {result}" - ) - assert result, "Failed to install or verify 'numpy' using conda" - - -def test_check_and_install_specific_version(): - result = check_and_install_package( - "numpy", package_manager="pip", version="1.21.0" - ) - print( - f"Test result for specific version of 'numpy' installation using pip: {result}" - ) - assert ( - result - ), "Failed to install or verify specific version of 'numpy' using pip" - - -def test_check_and_install_with_upgrade(): - result = check_and_install_package( - "numpy", package_manager="pip", upgrade=True - ) - print(f"Test result for 'numpy' upgrade using pip: {result}") - assert result, "Failed to upgrade 'numpy' using pip" - - -def test_auto_check_and_download_single_package(): - result = auto_check_and_download_package( - "scipy", package_manager="pip" - ) - print(f"Test result for 'scipy' installation using pip: {result}") - assert result, "Failed to install or verify 'scipy' using pip" - - -def test_auto_check_and_download_multiple_packages(): - packages = ["scipy", "pandas"] - result = auto_check_and_download_package( - packages, package_manager="pip" - ) - print( - f"Test result for multiple packages installation using pip: {result}" - ) - assert ( - result - ), f"Failed to install or verify one or more packages in {packages} using pip" - - -def test_auto_check_and_download_multiple_packages_with_versions(): - packages = ["numpy:1.21.0", "pandas:1.3.0"] - result = auto_check_and_download_package( - packages, package_manager="pip" - ) - print( - f"Test result for multiple packages with versions installation using pip: {result}" - ) - assert ( - result - ), f"Failed to install or verify one or more packages in {packages} with specific versions using pip" - - -# Example of running tests -if __name__ == "__main__": - try: - test_check_and_install_package_pip() - print("test_check_and_install_package_pip passed") - - test_check_and_install_package_conda() - print("test_check_and_install_package_conda passed") - - test_check_and_install_specific_version() - print("test_check_and_install_specific_version passed") - - test_check_and_install_with_upgrade() - print("test_check_and_install_with_upgrade passed") - - test_auto_check_and_download_single_package() - print("test_auto_check_and_download_single_package passed") - - test_auto_check_and_download_multiple_packages() - print("test_auto_check_and_download_multiple_packages passed") - - test_auto_check_and_download_multiple_packages_with_versions() - print( - "test_auto_check_and_download_multiple_packages_with_versions passed" - ) - - except AssertionError as e: - print(f"Test failed: {str(e)}") diff --git a/tests/utils/test_display_markdown_message.py b/tests/utils/test_display_markdown_message.py deleted file mode 100644 index 1b7cadaa..00000000 --- a/tests/utils/test_display_markdown_message.py +++ /dev/null @@ -1,67 +0,0 @@ -# import necessary modules -from unittest import mock - -import pytest -from rich.console import Console -from rich.markdown import Markdown -from rich.rule import Rule - -from swarms.utils import display_markdown_message - - -def test_basic_message(): - # Test basic message functionality - with mock.patch.object(Console, "print") as mock_print: - display_markdown_message("This is a test") - mock_print.assert_called_once_with( - Markdown("This is a test", style="cyan") - ) - - -def test_empty_message(): - # Test how function handles empty input - with mock.patch.object(Console, "print") as mock_print: - display_markdown_message("") - mock_print.assert_called_once_with("") - - -@pytest.mark.parametrize("color", ["cyan", "red", "blue"]) -def test_colors(color): - # Test different colors - with mock.patch.object(Console, "print") as mock_print: - display_markdown_message("This is a test", color) - mock_print.assert_called_once_with( - Markdown("This is a test", style=color) - ) - - -def test_dash_line(): - # Test how function handles "---" - with mock.patch.object(Console, "print") as mock_print: - display_markdown_message("---") - mock_print.assert_called_once_with(Rule(style="cyan")) - - -def test_message_with_whitespace(): - # Test how function handles message with whitespaces - with mock.patch.object(Console, "print") as mock_print: - display_markdown_message(" \n Test \n --- \n Test \n") - calls = [ - mock.call(""), - mock.call(Markdown("Test", style="cyan")), - mock.call(Rule(style="cyan")), - mock.call(Markdown("Test", style="cyan")), - mock.call(""), - ] - mock_print.assert_has_calls(calls) - - -def test_message_start_with_greater_than(): - # Test how function handles message line starting with ">" - with mock.patch.object(Console, "print") as mock_print: - display_markdown_message(">This is a test") - calls = [ - mock.call(Markdown(">This is a test", style="cyan")), - mock.call(""), - ] - mock_print.assert_has_calls(calls) diff --git a/tests/utils/test_docstring_parser.py b/tests/utils/test_docstring_parser.py index 2f1f2114..956d0927 100644 --- a/tests/utils/test_docstring_parser.py +++ b/tests/utils/test_docstring_parser.py @@ -1,14 +1,8 @@ -""" -Test suite for the custom docstring parser implementation. - -This module contains comprehensive tests to ensure the docstring parser -works correctly with various docstring formats and edge cases. -""" - import pytest + from swarms.utils.docstring_parser import ( - parse, DocstringParam, + parse, ) diff --git a/tests/agent/agents/test_litellm_args_kwargs.py b/tests/utils/test_litellm_args_kwargs.py similarity index 93% rename from tests/agent/agents/test_litellm_args_kwargs.py rename to tests/utils/test_litellm_args_kwargs.py index a9674908..09eacca6 100644 --- a/tests/agent/agents/test_litellm_args_kwargs.py +++ b/tests/utils/test_litellm_args_kwargs.py @@ -1,15 +1,3 @@ -#!/usr/bin/env python3 -""" -Test script to verify that the LiteLLM class properly handles args and kwargs -from both __init__ and run methods. -""" - -import sys -import os - -# Add the swarms directory to the path -sys.path.insert(0, os.path.join(os.path.dirname(__file__), "swarms")) - from swarms.utils.litellm_wrapper import LiteLLM diff --git a/tests/utils/test_math_eval.py b/tests/utils/test_math_eval.py deleted file mode 100644 index 642865b6..00000000 --- a/tests/utils/test_math_eval.py +++ /dev/null @@ -1,41 +0,0 @@ -from swarms.utils.math_eval import math_eval - - -def func1_no_exception(x): - return x + 2 - - -def func2_no_exception(x): - return x + 2 - - -def func1_with_exception(x): - raise ValueError() - - -def func2_with_exception(x): - raise ValueError() - - -def test_same_results_no_exception(caplog): - @math_eval(func1_no_exception, func2_no_exception) - def test_func(x): - return x - - result1, result2 = test_func(5) - assert result1 == result2 == 7 - assert "Outputs do not match" not in caplog.text - - -def test_func1_exception(caplog): - @math_eval(func1_with_exception, func2_no_exception) - def test_func(x): - return x - - result1, result2 = test_func(5) - assert result1 is None - assert result2 == 7 - assert "Error in func1:" in caplog.text - - -# similar tests for func2_with_exception and when func1 and func2 return different results diff --git a/tests/utils/test_md_output.py b/tests/utils/test_md_output.py deleted file mode 100644 index 57316226..00000000 --- a/tests/utils/test_md_output.py +++ /dev/null @@ -1,545 +0,0 @@ -import os - -from dotenv import load_dotenv - -# Load environment variables -load_dotenv() - -from swarms import ( - Agent, - ConcurrentWorkflow, - GroupChat, - SequentialWorkflow, -) - -from swarms.utils.formatter import Formatter - - -class MarkdownTestSwarm: - """A test swarm that demonstrates markdown output capabilities""" - - def __init__(self): - self.formatter = Formatter(markdown=True) - self.setup_agents() - self.setup_swarm() - - def setup_agents(self): - """Setup specialized agents for markdown testing""" - - # Research Agent - Generates structured markdown reports - self.research_agent = Agent( - agent_name="Research Agent", - system_prompt="""You are a research specialist. When given a topic, create a comprehensive markdown report with: - - Clear headers and subheaders - - Code examples when relevant - - Bullet points and numbered lists - - Bold and italic text for emphasis - - Tables for data comparison - - Code blocks with syntax highlighting - - Always format your response as clean markdown with proper structure.""", - model_name="gpt-4o-mini", # Use a more capable model - temperature=0.7, - max_tokens=4000, - max_loops=1, - context_length=8000, # Limit context to prevent overflow - return_history=False, # Don't return history to reduce context - ) - - # Code Analysis Agent - Generates code-heavy markdown - self.code_agent = Agent( - agent_name="Code Analysis Agent", - system_prompt="""You are a code analysis specialist. When given code or programming concepts, create markdown documentation with: - - Syntax-highlighted code blocks - - Function documentation - - Code examples - - Performance analysis - - Best practices - - Use proper markdown formatting with code blocks, inline code, and structured content.""", - model_name="gpt-4o-mini", # Use a more capable model - temperature=0.5, - max_tokens=4000, - max_loops=1, - context_length=8000, # Limit context to prevent overflow - return_history=False, # Don't return history to reduce context - ) - - # Data Visualization Agent - Creates data-focused markdown - self.data_agent = Agent( - agent_name="Data Visualization Agent", - system_prompt="""You are a data visualization specialist. When given data or analysis requests, create markdown reports with: - - Data tables - - Statistical analysis - - Charts and graphs descriptions - - Key insights with bold formatting - - Recommendations in structured lists - - Format everything as clean, readable markdown.""", - model_name="gpt-4o-mini", # Use a more capable model - temperature=0.6, - max_tokens=4000, - max_loops=1, - context_length=8000, # Limit context to prevent overflow - return_history=False, # Don't return history to reduce context - ) - - def setup_swarm(self): - """Setup the swarm with the agents""" - # Create different swarm types for testing - self.sequential_swarm = SequentialWorkflow( - name="Markdown Test Sequential", - description="Sequential workflow for markdown testing", - agents=[ - self.research_agent, - self.code_agent, - self.data_agent, - ], - max_loops=1, # Reduce loops to prevent context overflow - ) - - self.concurrent_swarm = ConcurrentWorkflow( - name="Markdown Test Concurrent", - description="Concurrent workflow for markdown testing", - agents=[ - self.research_agent, - self.code_agent, - self.data_agent, - ], - max_loops=1, # Reduce loops to prevent context overflow - ) - - self.groupchat_swarm = GroupChat( - name="Markdown Test Group Chat", - description="A group chat for testing markdown output", - agents=[ - self.research_agent, - self.code_agent, - self.data_agent, - ], - max_loops=1, # Reduce loops to prevent context overflow - ) - - # Default swarm for main tests - self.swarm = self.sequential_swarm - - def test_basic_markdown_output(self): - """Test basic markdown output with a simple topic""" - print("\n" + "=" * 60) - print("TEST 1: Basic Markdown Output") - print("=" * 60) - - topic = "Python Web Development with FastAPI" - - self.formatter.print_panel( - f"Starting research on: {topic}", - title="Research Topic", - style="bold blue", - ) - - # Run the research agent - result = self.research_agent.run(topic) - - self.formatter.print_markdown( - result, title="Research Report", border_style="green" - ) - - def test_code_analysis_markdown(self): - """Test markdown output with code analysis""" - print("\n" + "=" * 60) - print("TEST 2: Code Analysis Markdown") - print("=" * 60) - - code_sample = """ -def fibonacci(n): - if n <= 1: - return n - return fibonacci(n-1) + fibonacci(n-2) - -# Test the function -for i in range(10): - print(fibonacci(i)) - """ - - self.formatter.print_panel( - "Analyzing Python code sample", - title="Code Analysis", - style="bold cyan", - ) - - # Run the code analysis agent - result = self.code_agent.run( - f"Analyze this Python code and provide improvements:\n\n{code_sample}" - ) - - self.formatter.print_markdown( - result, - title="Code Analysis Report", - border_style="yellow", - ) - - def test_data_analysis_markdown(self): - """Test markdown output with data analysis""" - print("\n" + "=" * 60) - print("TEST 3: Data Analysis Markdown") - print("=" * 60) - - data_request = """ - Analyze the following dataset: - - Sales: $1.2M (Q1), $1.5M (Q2), $1.8M (Q3), $2.1M (Q4) - - Growth Rate: 8%, 12%, 15%, 18% - - Customer Count: 1000, 1200, 1400, 1600 - - Provide insights and recommendations in markdown format. - """ - - self.formatter.print_panel( - "Analyzing quarterly business data", - title="Data Analysis", - style="bold magenta", - ) - - # Run the data analysis agent - result = self.data_agent.run(data_request) - - self.formatter.print_markdown( - result, title="Data Analysis Report", border_style="red" - ) - - def test_swarm_collaboration_markdown(self): - """Test markdown output with swarm collaboration""" - print("\n" + "=" * 60) - print("TEST 4: Swarm Collaboration Markdown") - print("=" * 60) - - complex_topic = """ - Create a comprehensive guide on building a machine learning pipeline that includes: - 1. Data preprocessing techniques - 2. Model selection strategies - 3. Performance evaluation metrics - 4. Deployment considerations - - Each agent should contribute their expertise and the final output should be well-formatted markdown. - """ - - self.formatter.print_panel( - "Swarm collaboration on ML pipeline guide", - title="Swarm Task", - style="bold green", - ) - - # Run the swarm - results = self.swarm.run(complex_topic) - - # Display individual agent results - # SequentialWorkflow returns a list of results, not a dict - for i, result in enumerate(results, 1): - agent_name = f"Agent {i}" - - # Handle different result types - if isinstance(result, dict): - # Extract the output from dict result - result_content = result.get("output", str(result)) - else: - result_content = str(result) - self.formatter.print_markdown( - result_content, - title=f"Agent {i}: {agent_name}", - border_style="blue", - ) - - def test_markdown_toggle_functionality(self): - """Test the markdown enable/disable functionality""" - print("\n" + "=" * 60) - print("TEST 5: Markdown Toggle Functionality") - print("=" * 60) - - test_content = """ -# Test Content - -This is a **bold** test with `inline code`. - -## Code Block -```python -def test_function(): - return "Hello, World!" -``` - -## List -- Item 1 -- Item 2 -- Item 3 - """ - - # Test with markdown enabled - self.formatter.print_panel( - "Testing with markdown ENABLED", - title="Markdown Enabled", - style="bold green", - ) - self.formatter.print_markdown(test_content, "Markdown Output") - - # Disable markdown - self.formatter.disable_markdown() - self.formatter.print_panel( - "Testing with markdown DISABLED", - title="Markdown Disabled", - style="bold red", - ) - self.formatter.print_panel(test_content, "Plain Text Output") - - # Re-enable markdown - self.formatter.enable_markdown() - self.formatter.print_panel( - "Testing with markdown RE-ENABLED", - title="Markdown Re-enabled", - style="bold blue", - ) - self.formatter.print_markdown( - test_content, "Markdown Output Again" - ) - - def test_different_swarm_types(self): - """Test markdown output with different swarm types""" - print("\n" + "=" * 60) - print("TEST 6: Different Swarm Types") - print("=" * 60) - - simple_topic = ( - "Explain the benefits of using Python for data science" - ) - - # Test Sequential Workflow - print("\n--- Sequential Workflow ---") - self.formatter.print_panel( - "Testing Sequential Workflow (agents work in sequence)", - title="Swarm Type Test", - style="bold blue", - ) - sequential_results = self.sequential_swarm.run(simple_topic) - for i, result in enumerate(sequential_results, 1): - # Handle different result types - if isinstance(result, dict): - result_content = result.get("output", str(result)) - else: - result_content = str(result) - - self.formatter.print_markdown( - result_content, - title=f"Sequential Agent {i}", - border_style="blue", - ) - - # Test Concurrent Workflow - print("\n--- Concurrent Workflow ---") - self.formatter.print_panel( - "Testing Concurrent Workflow (agents work in parallel)", - title="Swarm Type Test", - style="bold green", - ) - concurrent_results = self.concurrent_swarm.run(simple_topic) - for i, result in enumerate(concurrent_results, 1): - # Handle different result types - if isinstance(result, dict): - result_content = result.get("output", str(result)) - else: - result_content = str(result) - - self.formatter.print_markdown( - result_content, - title=f"Concurrent Agent {i}", - border_style="green", - ) - - # Test Group Chat - print("\n--- Group Chat ---") - self.formatter.print_panel( - "Testing Group Chat (agents collaborate in conversation)", - title="Swarm Type Test", - style="bold magenta", - ) - groupchat_results = self.groupchat_swarm.run(simple_topic) - - # Handle different result types for GroupChat - if isinstance(groupchat_results, dict): - result_content = groupchat_results.get( - "output", str(groupchat_results) - ) - else: - result_content = str(groupchat_results) - - self.formatter.print_markdown( - result_content, - title="Group Chat Result", - border_style="magenta", - ) - - def test_simple_formatter_only(self): - """Test just the formatter functionality without agents""" - print("\n" + "=" * 60) - print("TEST 7: Simple Formatter Test (No Agents)") - print("=" * 60) - - # Test basic markdown rendering - simple_markdown = """ -# Simple Test - -This is a **bold** test with `inline code`. - -## Code Block -```python -def hello_world(): - print("Hello, World!") - return "Success" -``` - -## List -- Item 1 -- Item 2 -- Item 3 - """ - - self.formatter.print_panel( - "Testing formatter without agents", - title="Formatter Test", - style="bold cyan", - ) - - self.formatter.print_markdown( - simple_markdown, - title="Simple Markdown Test", - border_style="green", - ) - - # Test toggle functionality - self.formatter.disable_markdown() - self.formatter.print_panel( - "Markdown disabled - this should be plain text", - title="Plain Text Test", - style="bold red", - ) - self.formatter.enable_markdown() - - def test_error_handling_markdown(self): - """Test markdown output with error handling""" - print("\n" + "=" * 60) - print("TEST 8: Error Handling in Markdown") - print("=" * 60) - - # Test with malformed markdown - malformed_content = """ -# Incomplete header -**Unclosed bold -```python -def incomplete_code(): - # Missing closing backticks - """ - - self.formatter.print_panel( - "Testing error handling with malformed markdown", - title="Error Handling Test", - style="bold yellow", - ) - - # This should handle the error gracefully - self.formatter.print_markdown( - malformed_content, - title="Malformed Markdown Test", - border_style="yellow", - ) - - # Test with empty content - self.formatter.print_markdown( - "", title="Empty Content Test", border_style="cyan" - ) - - # Test with None content - self.formatter.print_markdown( - None, title="None Content Test", border_style="magenta" - ) - - def run_all_tests(self): - """Run all markdown output tests""" - print(" Starting Swarm Markdown Output Tests") - print("=" * 60) - - try: - # Test 1: Basic markdown output - self.test_basic_markdown_output() - - # Test 2: Code analysis markdown - self.test_code_analysis_markdown() - - # Test 3: Data analysis markdown - self.test_data_analysis_markdown() - - # Test 4: Swarm collaboration - self.test_swarm_collaboration_markdown() - - # Test 5: Markdown toggle functionality - self.test_markdown_toggle_functionality() - - # Test 6: Different swarm types - self.test_different_swarm_types() - - # Test 7: Simple formatter test (no agents) - self.test_simple_formatter_only() - - # Test 8: Error handling - self.test_error_handling_markdown() - - print("\n" + "=" * 60) - print(" All tests completed successfully!") - print("=" * 60) - - except Exception as e: - print(f"\n Test failed with error: {str(e)}") - import traceback - - traceback.print_exc() - - -def main(): - """Main function to run the markdown output tests""" - print("Swarms Markdown Output Test Suite") - print( - "Testing the current state of formatter.py with real swarm agents" - ) - print("=" * 60) - - # Check environment setup - api_key = os.getenv("OPENAI_API_KEY") or os.getenv( - "SWARMS_API_KEY" - ) - if not api_key: - print( - "āš  Warning: No API key found. Please set OPENAI_API_KEY or SWARMS_API_KEY environment variable." - ) - print( - " You can create a .env file with: OPENAI_API_KEY=your_api_key_here" - ) - print( - " Or set it in your environment: export OPENAI_API_KEY=your_api_key_here" - ) - print() - - try: - # Create and run the test swarm - test_swarm = MarkdownTestSwarm() - test_swarm.run_all_tests() - except Exception as e: - print(f"\n Test failed with error: {str(e)}") - print("\n Troubleshooting tips:") - print( - "1. Make sure you have set your API key (OPENAI_API_KEY or SWARMS_API_KEY)" - ) - print("2. Check your internet connection") - print("3. Verify you have sufficient API credits") - print("4. Try running with a simpler test first") - import traceback - - traceback.print_exc() - - -if __name__ == "__main__": - main() diff --git a/tests/utils/test_metrics_decorator.py b/tests/utils/test_metrics_decorator.py deleted file mode 100644 index 8c3a8af9..00000000 --- a/tests/utils/test_metrics_decorator.py +++ /dev/null @@ -1,88 +0,0 @@ -# pytest imports -import time -from unittest.mock import Mock - -import pytest - -# Imports from your project -from swarms.utils import metrics_decorator - - -# Basic successful test -def test_metrics_decorator_success(): - @metrics_decorator - def decorated_func(): - time.sleep(0.1) - return [1, 2, 3, 4, 5] - - metrics = decorated_func() - assert "Time to First Token" in metrics - assert "Generation Latency" in metrics - assert "Throughput:" in metrics - - -@pytest.mark.parametrize( - "wait_time, return_val", - [ - (0, []), - (0.1, [1, 2, 3]), - (0.5, list(range(50))), - ], -) -def test_metrics_decorator_with_various_wait_times_and_return_vals( - wait_time, return_val -): - @metrics_decorator - def decorated_func(): - time.sleep(wait_time) - return return_val - - metrics = decorated_func() - assert "Time to First Token" in metrics - assert "Generation Latency" in metrics - assert "Throughput:" in metrics - - -# Test to ensure that mocked time function was called and throughputs are calculated as expected -def test_metrics_decorator_with_mocked_time(mocker): - mocked_time = Mock() - mocker.patch("time.time", mocked_time) - - mocked_time.side_effect = [0, 5, 10, 20] - - @metrics_decorator - def decorated_func(): - return ["tok_1", "tok_2"] - - metrics = decorated_func() - assert ( - metrics - == """ - Time to First Token: 5 - Generation Latency: 20 - Throughput: 0.1 - """ - ) - mocked_time.assert_any_call() - - -# Test to ensure that exceptions in the decorated function are propagated -def test_metrics_decorator_raises_exception(): - @metrics_decorator - def decorated_func(): - raise ValueError("Oops!") - - with pytest.raises(ValueError, match="Oops!"): - decorated_func() - - -# Test to ensure proper handling when decorated function returns non-list value -def test_metrics_decorator_with_non_list_return_val(): - @metrics_decorator - def decorated_func(): - return "Hello, world!" - - metrics = decorated_func() - assert "Time to First Token" in metrics - assert "Generation Latency" in metrics - assert "Throughput:" in metrics diff --git a/tests/utils/test_pdf_to_text.py b/tests/utils/test_pdf_to_text.py deleted file mode 100644 index 257364b4..00000000 --- a/tests/utils/test_pdf_to_text.py +++ /dev/null @@ -1,41 +0,0 @@ -import pypdf -import pytest - -from swarms.utils import pdf_to_text - - -@pytest.fixture -def pdf_file(tmpdir): - pdf_writer = pypdf.PdfWriter() - pdf_page = pypdf.PageObject.create_blank_page(None, 200, 200) - pdf_writer.add_page(pdf_page) - pdf_file = tmpdir.join("temp.pdf") - with open(pdf_file, "wb") as output: - pdf_writer.write(output) - return str(pdf_file) - - -def test_valid_pdf_to_text(pdf_file): - result = pdf_to_text(pdf_file) - assert isinstance(result, str) - - -def test_non_existing_file(): - with pytest.raises(FileNotFoundError): - pdf_to_text("non_existing_file.pdf") - - -def test_passing_non_pdf_file(tmpdir): - file = tmpdir.join("temp.txt") - file.write("This is a test") - with pytest.raises( - Exception, - match=r"An error occurred while reading the PDF file", - ): - pdf_to_text(str(file)) - - -@pytest.mark.parametrize("invalid_pdf_file", [None, 123, {}, []]) -def test_invalid_pdf_to_text(invalid_pdf_file): - with pytest.raises(Exception): - pdf_to_text(invalid_pdf_file)