diff --git a/docs/mkdocs.yml b/docs/mkdocs.yml index 944e8a03..c88f709d 100644 --- a/docs/mkdocs.yml +++ b/docs/mkdocs.yml @@ -290,7 +290,6 @@ nav: - Overview: "swarms/structs/multi_swarm_orchestration.md" - HierarchicalSwarm: "swarms/structs/hierarchical_swarm.md" - Hierarchical Structured Communication Framework: "swarms/structs/hierarchical_structured_communication_framework.md" - - Auto Agent Builder: "swarms/structs/auto_agent_builder.md" - Hybrid Hierarchical-Cluster Swarm: "swarms/structs/hhcs.md" - Auto Swarm Builder: "swarms/structs/auto_swarm_builder.md" - Swarm Matcher: "swarms/structs/swarm_matcher.md" diff --git a/docs/swarms/structs/auto_agent_builder.md b/docs/swarms/structs/auto_agent_builder.md deleted file mode 100644 index 2fa06bdd..00000000 --- a/docs/swarms/structs/auto_agent_builder.md +++ /dev/null @@ -1,200 +0,0 @@ -# Agent Builder - -The Agent Builder is a powerful class that automatically builds and manages swarms of AI agents. It provides a flexible and extensible framework for creating, coordinating, and executing multiple AI agents working together to accomplish complex tasks. - -## Overview - -The Agent Builder uses a boss agent to delegate work and create new specialized agents as needed. It's designed to be production-ready with robust error handling, logging, and configuration options. - -## Architecture - -```mermaid -graph TD - A[Agent Builder] --> B[Configuration] - A --> C[Agent Creation] - A --> D[Task Execution] - - B --> B1[Name] - B --> B2[Description] - B --> B3[Model Settings] - - C --> C1[Agent Pool] - C --> C2[Agent Registry] - C --> C3[Agent Configuration] - - D --> D1[Task Distribution] - D --> D2[Result Collection] - D --> D3[Error Handling] - - C1 --> E[Specialized Agents] - C2 --> E - C3 --> E - - E --> F[Task Execution] - F --> G[Results] -``` - -## Class Structure - -### AgentsBuilder Class - -| Parameter | Type | Default | Description | -|-----------|------|---------|-------------| -| name | str | "swarm-creator-01" | The name of the swarm | -| description | str | "This is a swarm that creates swarms" | A description of the swarm's purpose | -| verbose | bool | True | Whether to output detailed logs | -| max_loops | int | 1 | Maximum number of execution loops | -| model_name | str | "gpt-4o" | The model to use for agent creation | -| return_dictionary | bool | True | Whether to return results as a dictionary | -| system_prompt | str | BOSS_SYSTEM_PROMPT | The system prompt for the boss agent | - -### Methods - -| Method | Description | Parameters | Returns | -|--------|-------------|------------|---------| -| run | Run the swarm on a given task | task: str, image_url: str = None, *args, **kwargs | Tuple[List[Agent], int] | -| _create_agents | Create necessary agents for a task | task: str, *args, **kwargs | List[Agent] | -| build_agent | Build a single agent with specifications | agent_name: str, agent_description: str, agent_system_prompt: str, max_loops: int = 1, model_name: str = "gpt-4o", dynamic_temperature_enabled: bool = True, auto_generate_prompt: bool = False, role: str = "worker", max_tokens: int = 8192, temperature: float = 0.5 | Agent | - -## Enterprise Use Cases - -### 1. Customer Service Automation -- Create specialized agents for different aspects of customer service - -- Handle ticket routing, response generation, and escalation - -- Maintain consistent service quality across channels - -### 2. Data Analysis Pipeline -- Build agents for data collection, cleaning, analysis, and visualization - -- Automate complex data processing workflows - -- Generate insights and reports automatically - -### 3. Content Creation and Management -- Deploy agents for content research, writing, editing, and publishing - -- Maintain brand consistency across content - -- Automate content scheduling and distribution - -### 4. Process Automation -- Create agents for workflow automation - -- Handle document processing and routing - -- Manage approval chains and notifications - -### 5. Research and Development -- Build agents for literature review, experiment design, and data collection - -- Automate research documentation and reporting - -- Facilitate collaboration between research teams - -## Example Usage - -```python -from swarms import AgentsBuilder - -# Initialize the agent builder -agents_builder = AgentsBuilder( - name="enterprise-automation", - description="Enterprise workflow automation swarm", - verbose=True -) - -# Define a use-case for building agents -task = "Develop a swarm of agents to automate the generation of personalized marketing strategies based on customer data and market trends" - -# Run the swarm -agents = agents_builder.run(task) - -# Access results -print(agents) -``` - -## Best Practices - -1. **Error Handling** - - Always implement proper error handling for agent failures - - Use retry mechanisms for transient failures - - Log all errors for debugging and monitoring - -2. **Resource Management** - - Monitor agent resource usage - - Implement rate limiting for API calls - - Use connection pooling for database operations - -3. **Security** - - Implement proper authentication and authorization - - Secure sensitive data and API keys - - Follow least privilege principle for agent permissions - -4. **Monitoring and Logging** - - Implement comprehensive logging - - Monitor agent performance metrics - - Set up alerts for critical failures - -5. **Scalability** - - Design for horizontal scaling - - Implement load balancing - - Use distributed systems when needed - -## Integration Patterns - -```mermaid -graph LR - A[External System] --> B[API Gateway] - B --> C[Agent Builder] - C --> D[Agent Pool] - D --> E[Specialized Agents] - E --> F[External Services] - - subgraph "Monitoring" - G[Logs] - H[Metrics] - I[Alerts] - end - - C --> G - C --> H - C --> I -``` - -## Performance Considerations - -1. **Agent Pool Management** - - Implement connection pooling - - Use caching for frequently accessed data - - Optimize agent creation and destruction - -2. **Task Distribution** - - Implement load balancing - - Use priority queues for task scheduling - - Handle task timeouts and retries - -3. **Resource Optimization** - - Monitor memory usage - - Implement garbage collection - - Use efficient data structures - -## Troubleshooting - -Common issues and solutions: - -1. **Agent Creation Failures** - - Check API credentials - - Verify model availability - - Review system prompts - -2. **Performance Issues** - - Monitor resource usage - - Check network latency - - Review agent configurations - -3. **Integration Problems** - - Verify API endpoints - - Check authentication - - Review data formats diff --git a/docs/swarms/structs/multi_swarm_orchestration.md b/docs/swarms/structs/multi_swarm_orchestration.md index 68f36a4e..93db978f 100644 --- a/docs/swarms/structs/multi_swarm_orchestration.md +++ b/docs/swarms/structs/multi_swarm_orchestration.md @@ -154,11 +154,6 @@ flowchart TD - Covers detailed implementation, constructor arguments, and full examples -### Auto Agent Builder Documentation: - -- [Agent Builder Documentation](https://docs.swarms.world/en/latest/swarms/structs/auto_agent_builder/) - -- Includes enterprise use cases, best practices, and integration patterns 3. SwarmRouter Documentation: diff --git a/docs/swarms/structs/overview.md b/docs/swarms/structs/overview.md index ba869cbc..6c63cfb0 100644 --- a/docs/swarms/structs/overview.md +++ b/docs/swarms/structs/overview.md @@ -36,7 +36,6 @@ This page provides a comprehensive overview of all available multi-agent archite | Architecture | Use Case | Key Functionality | Documentation | |-------------|----------|-------------------|---------------| | HierarchicalSwarm | Hierarchical task orchestration | Director agent coordinates specialized worker agents | [Docs](hierarchical_swarm.md) | - | Auto Agent Builder | Automated agent creation | Automatically creates and configures agents | [Docs](auto_agent_builder.md) | | Hybrid Hierarchical-Cluster Swarm | Complex organization | Combines hierarchical and cluster-based organization | [Docs](hhcs.md) | | Auto Swarm Builder | Automated swarm creation | Automatically creates and configures swarms | [Docs](auto_swarm_builder.md) | diff --git a/swarms/prompts/agent_orchestration_prompt.py b/swarms/prompts/agent_orchestration_prompt.py new file mode 100644 index 00000000..08ac58ae --- /dev/null +++ b/swarms/prompts/agent_orchestration_prompt.py @@ -0,0 +1,76 @@ + +BOSS_SYSTEM_PROMPT = """ +# Swarm Intelligence Orchestrator + +You are the Chief Orchestrator of a sophisticated agent swarm. Your primary responsibility is to analyze tasks and create the optimal team of specialized agents to accomplish complex objectives efficiently. + +## Agent Creation Protocol + +1. **Task Analysis**: + - Thoroughly analyze the user's task to identify all required skills, knowledge domains, and subtasks + - Break down complex problems into discrete components that can be assigned to specialized agents + - Identify potential challenges and edge cases that might require specialized handling + +2. **Agent Design Principles**: + - Create highly specialized agents with clearly defined roles and responsibilities + - Design each agent with deep expertise in their specific domain + - Provide agents with comprehensive and extremely extensive system prompts that include: + * Precise definition of their role and scope of responsibility + * Detailed methodology for approaching problems in their domain + * Specific techniques, frameworks, and mental models to apply + * Guidelines for output format and quality standards + * Instructions for collaboration with other agents + * In-depth examples and scenarios to illustrate expected behavior and decision-making processes + * Extensive background information relevant to the tasks they will undertake + +3. **Cognitive Enhancement**: + - Equip agents with advanced reasoning frameworks: + * First principles thinking to break down complex problems + * Systems thinking to understand interconnections + * Lateral thinking for creative solutions + * Critical thinking to evaluate information quality + - Implement specialized thought patterns: + * Step-by-step reasoning for complex problems + * Hypothesis generation and testing + * Counterfactual reasoning to explore alternatives + * Analogical reasoning to apply solutions from similar domains + +4. **Swarm Architecture**: + - Design optimal agent interaction patterns based on task requirements + - Consider hierarchical, networked, or hybrid structures + - Establish clear communication protocols between agents + - Define escalation paths for handling edge cases + +5. **Agent Specialization Examples**: + - Research Agents: Literature review, data gathering, information synthesis + - Analysis Agents: Data processing, pattern recognition, insight generation + - Creative Agents: Idea generation, content creation, design thinking + - Planning Agents: Strategy development, resource allocation, timeline creation + - Implementation Agents: Code writing, document drafting, execution planning + - Quality Assurance Agents: Testing, validation, error detection + - Integration Agents: Combining outputs, ensuring consistency, resolving conflicts + +## Output Format + +For each agent, provide: + +1. **Agent Name**: Clear, descriptive title reflecting specialization +2. **Description**: Concise overview of the agent's purpose and capabilities +3. **System Prompt**: Comprehensive and extremely extensive instructions including: + - Role definition and responsibilities + - Specialized knowledge and methodologies + - Thinking frameworks and problem-solving approaches + - Output requirements and quality standards + - Collaboration guidelines with other agents + - Detailed examples and context to ensure clarity and effectiveness + +## Optimization Guidelines + +- Create only the agents necessary for the task - no more, no less +- Ensure each agent has a distinct, non-overlapping area of responsibility +- Design system prompts that maximize agent performance through clear guidance and specialized knowledge +- Balance specialization with the need for effective collaboration +- Prioritize agents that address the most critical aspects of the task + +Remember: Your goal is to create a swarm of agents that collectively possesses the intelligence, knowledge, and capabilities to deliver exceptional results for the user's task. +""" diff --git a/swarms/structs/__init__.py b/swarms/structs/__init__.py index fc05d99c..7b99e637 100644 --- a/swarms/structs/__init__.py +++ b/swarms/structs/__init__.py @@ -1,5 +1,4 @@ from swarms.structs.agent import Agent -from swarms.structs.agent_builder import AgentsBuilder from swarms.structs.agent_loader import AgentLoader from swarms.structs.agent_rearrange import AgentRearrange, rearrange from swarms.structs.auto_swarm_builder import AutoSwarmBuilder @@ -155,7 +154,6 @@ __all__ = [ "MultiAgentRouter", "MemeAgentGenerator", "ModelRouter", - "AgentsBuilder", "MALT", "HybridHierarchicalClusterSwarm", "get_agents_info", diff --git a/swarms/utils/litellm_wrapper.py b/swarms/utils/litellm_wrapper.py index e45b16a8..76e647b8 100644 --- a/swarms/utils/litellm_wrapper.py +++ b/swarms/utils/litellm_wrapper.py @@ -496,6 +496,7 @@ class LiteLLM: if isinstance(out, BaseModel): out = out.model_dump() + return out def output_for_reasoning(self, response: any):