Kye Gomez
1 week ago
parent
4650c5d70e
commit
af9bc7e30b
@ -1,200 +0,0 @@
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# Agent Builder
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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.
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## Overview
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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.
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## Architecture
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```mermaid
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graph TD
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A[Agent Builder] --> B[Configuration]
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A --> C[Agent Creation]
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A --> D[Task Execution]
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B --> B1[Name]
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B --> B2[Description]
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B --> B3[Model Settings]
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C --> C1[Agent Pool]
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C --> C2[Agent Registry]
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C --> C3[Agent Configuration]
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D --> D1[Task Distribution]
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D --> D2[Result Collection]
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D --> D3[Error Handling]
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C1 --> E[Specialized Agents]
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C2 --> E
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C3 --> E
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E --> F[Task Execution]
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F --> G[Results]
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```
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## Class Structure
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### AgentsBuilder Class
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| Parameter | Type | Default | Description |
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|-----------|------|---------|-------------|
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| name | str | "swarm-creator-01" | The name of the swarm |
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| description | str | "This is a swarm that creates swarms" | A description of the swarm's purpose |
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| verbose | bool | True | Whether to output detailed logs |
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| max_loops | int | 1 | Maximum number of execution loops |
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| model_name | str | "gpt-4o" | The model to use for agent creation |
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| return_dictionary | bool | True | Whether to return results as a dictionary |
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| system_prompt | str | BOSS_SYSTEM_PROMPT | The system prompt for the boss agent |
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### Methods
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| Method | Description | Parameters | Returns |
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|--------|-------------|------------|---------|
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| run | Run the swarm on a given task | task: str, image_url: str = None, *args, **kwargs | Tuple[List[Agent], int] |
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| _create_agents | Create necessary agents for a task | task: str, *args, **kwargs | List[Agent] |
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| 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 |
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## Enterprise Use Cases
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### 1. Customer Service Automation
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- Create specialized agents for different aspects of customer service
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- Handle ticket routing, response generation, and escalation
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- Maintain consistent service quality across channels
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### 2. Data Analysis Pipeline
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- Build agents for data collection, cleaning, analysis, and visualization
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- Automate complex data processing workflows
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- Generate insights and reports automatically
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### 3. Content Creation and Management
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- Deploy agents for content research, writing, editing, and publishing
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- Maintain brand consistency across content
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- Automate content scheduling and distribution
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### 4. Process Automation
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- Create agents for workflow automation
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- Handle document processing and routing
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- Manage approval chains and notifications
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### 5. Research and Development
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- Build agents for literature review, experiment design, and data collection
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- Automate research documentation and reporting
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- Facilitate collaboration between research teams
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## Example Usage
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```python
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from swarms import AgentsBuilder
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# Initialize the agent builder
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agents_builder = AgentsBuilder(
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name="enterprise-automation",
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description="Enterprise workflow automation swarm",
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verbose=True
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)
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# Define a use-case for building agents
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task = "Develop a swarm of agents to automate the generation of personalized marketing strategies based on customer data and market trends"
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# Run the swarm
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agents = agents_builder.run(task)
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# Access results
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print(agents)
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```
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## Best Practices
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1. **Error Handling**
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- Always implement proper error handling for agent failures
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- Use retry mechanisms for transient failures
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- Log all errors for debugging and monitoring
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2. **Resource Management**
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- Monitor agent resource usage
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- Implement rate limiting for API calls
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- Use connection pooling for database operations
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3. **Security**
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- Implement proper authentication and authorization
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- Secure sensitive data and API keys
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- Follow least privilege principle for agent permissions
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4. **Monitoring and Logging**
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- Implement comprehensive logging
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- Monitor agent performance metrics
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- Set up alerts for critical failures
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5. **Scalability**
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- Design for horizontal scaling
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- Implement load balancing
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- Use distributed systems when needed
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## Integration Patterns
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```mermaid
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graph LR
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A[External System] --> B[API Gateway]
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B --> C[Agent Builder]
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C --> D[Agent Pool]
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D --> E[Specialized Agents]
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E --> F[External Services]
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subgraph "Monitoring"
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G[Logs]
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H[Metrics]
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I[Alerts]
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end
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C --> G
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C --> H
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C --> I
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```
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## Performance Considerations
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1. **Agent Pool Management**
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- Implement connection pooling
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- Use caching for frequently accessed data
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- Optimize agent creation and destruction
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2. **Task Distribution**
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- Implement load balancing
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- Use priority queues for task scheduling
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- Handle task timeouts and retries
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3. **Resource Optimization**
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- Monitor memory usage
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- Implement garbage collection
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- Use efficient data structures
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## Troubleshooting
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Common issues and solutions:
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1. **Agent Creation Failures**
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- Check API credentials
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- Verify model availability
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- Review system prompts
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2. **Performance Issues**
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- Monitor resource usage
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- Check network latency
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- Review agent configurations
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3. **Integration Problems**
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- Verify API endpoints
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- Check authentication
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- Review data formats
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@ -1,9 +0,0 @@
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from swarms.structs.agent_builder import AgentsBuilder
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example_task = "Write a blog post about the benefits of using Swarms for AI agents."
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agents_builder = AgentsBuilder()
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agents = agents_builder.run(example_task)
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print(agents)
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@ -0,0 +1,76 @@
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BOSS_SYSTEM_PROMPT = """
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# Swarm Intelligence Orchestrator
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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.
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## Agent Creation Protocol
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1. **Task Analysis**:
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- Thoroughly analyze the user's task to identify all required skills, knowledge domains, and subtasks
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- Break down complex problems into discrete components that can be assigned to specialized agents
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- Identify potential challenges and edge cases that might require specialized handling
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2. **Agent Design Principles**:
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- Create highly specialized agents with clearly defined roles and responsibilities
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- Design each agent with deep expertise in their specific domain
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- Provide agents with comprehensive and extremely extensive system prompts that include:
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* Precise definition of their role and scope of responsibility
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* Detailed methodology for approaching problems in their domain
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* Specific techniques, frameworks, and mental models to apply
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* Guidelines for output format and quality standards
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* Instructions for collaboration with other agents
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* In-depth examples and scenarios to illustrate expected behavior and decision-making processes
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* Extensive background information relevant to the tasks they will undertake
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3. **Cognitive Enhancement**:
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- Equip agents with advanced reasoning frameworks:
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* First principles thinking to break down complex problems
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* Systems thinking to understand interconnections
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* Lateral thinking for creative solutions
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* Critical thinking to evaluate information quality
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- Implement specialized thought patterns:
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* Step-by-step reasoning for complex problems
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* Hypothesis generation and testing
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* Counterfactual reasoning to explore alternatives
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* Analogical reasoning to apply solutions from similar domains
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4. **Swarm Architecture**:
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- Design optimal agent interaction patterns based on task requirements
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- Consider hierarchical, networked, or hybrid structures
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- Establish clear communication protocols between agents
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- Define escalation paths for handling edge cases
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5. **Agent Specialization Examples**:
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- Research Agents: Literature review, data gathering, information synthesis
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- Analysis Agents: Data processing, pattern recognition, insight generation
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- Creative Agents: Idea generation, content creation, design thinking
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- Planning Agents: Strategy development, resource allocation, timeline creation
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- Implementation Agents: Code writing, document drafting, execution planning
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- Quality Assurance Agents: Testing, validation, error detection
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- Integration Agents: Combining outputs, ensuring consistency, resolving conflicts
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## Output Format
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For each agent, provide:
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1. **Agent Name**: Clear, descriptive title reflecting specialization
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2. **Description**: Concise overview of the agent's purpose and capabilities
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3. **System Prompt**: Comprehensive and extremely extensive instructions including:
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- Role definition and responsibilities
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- Specialized knowledge and methodologies
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- Thinking frameworks and problem-solving approaches
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- Output requirements and quality standards
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- Collaboration guidelines with other agents
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- Detailed examples and context to ensure clarity and effectiveness
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## Optimization Guidelines
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- Create only the agents necessary for the task - no more, no less
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- Ensure each agent has a distinct, non-overlapping area of responsibility
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- Design system prompts that maximize agent performance through clear guidance and specialized knowledge
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- Balance specialization with the need for effective collaboration
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- Prioritize agents that address the most critical aspects of the task
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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.
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"""
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@ -1,293 +0,0 @@
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import os
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from typing import Any, List, Optional, Tuple
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from loguru import logger
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from pydantic import BaseModel, Field
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from swarms.structs.agent import Agent
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from swarms.utils.function_caller_model import OpenAIFunctionCaller
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BOSS_SYSTEM_PROMPT = """
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# Swarm Intelligence Orchestrator
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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.
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## Agent Creation Protocol
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1. **Task Analysis**:
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- Thoroughly analyze the user's task to identify all required skills, knowledge domains, and subtasks
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- Break down complex problems into discrete components that can be assigned to specialized agents
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- Identify potential challenges and edge cases that might require specialized handling
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2. **Agent Design Principles**:
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- Create highly specialized agents with clearly defined roles and responsibilities
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- Design each agent with deep expertise in their specific domain
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- Provide agents with comprehensive and extremely extensive system prompts that include:
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* Precise definition of their role and scope of responsibility
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* Detailed methodology for approaching problems in their domain
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* Specific techniques, frameworks, and mental models to apply
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* Guidelines for output format and quality standards
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* Instructions for collaboration with other agents
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* In-depth examples and scenarios to illustrate expected behavior and decision-making processes
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* Extensive background information relevant to the tasks they will undertake
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3. **Cognitive Enhancement**:
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- Equip agents with advanced reasoning frameworks:
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* First principles thinking to break down complex problems
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* Systems thinking to understand interconnections
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* Lateral thinking for creative solutions
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* Critical thinking to evaluate information quality
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- Implement specialized thought patterns:
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* Step-by-step reasoning for complex problems
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* Hypothesis generation and testing
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* Counterfactual reasoning to explore alternatives
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* Analogical reasoning to apply solutions from similar domains
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4. **Swarm Architecture**:
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- Design optimal agent interaction patterns based on task requirements
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- Consider hierarchical, networked, or hybrid structures
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- Establish clear communication protocols between agents
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- Define escalation paths for handling edge cases
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5. **Agent Specialization Examples**:
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- Research Agents: Literature review, data gathering, information synthesis
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- Analysis Agents: Data processing, pattern recognition, insight generation
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- Creative Agents: Idea generation, content creation, design thinking
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- Planning Agents: Strategy development, resource allocation, timeline creation
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- Implementation Agents: Code writing, document drafting, execution planning
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- Quality Assurance Agents: Testing, validation, error detection
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- Integration Agents: Combining outputs, ensuring consistency, resolving conflicts
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## Output Format
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For each agent, provide:
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1. **Agent Name**: Clear, descriptive title reflecting specialization
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2. **Description**: Concise overview of the agent's purpose and capabilities
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3. **System Prompt**: Comprehensive and extremely extensive instructions including:
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- Role definition and responsibilities
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- Specialized knowledge and methodologies
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- Thinking frameworks and problem-solving approaches
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- Output requirements and quality standards
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- Collaboration guidelines with other agents
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- Detailed examples and context to ensure clarity and effectiveness
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## Optimization Guidelines
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- Create only the agents necessary for the task - no more, no less
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- Ensure each agent has a distinct, non-overlapping area of responsibility
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- Design system prompts that maximize agent performance through clear guidance and specialized knowledge
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- Balance specialization with the need for effective collaboration
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- Prioritize agents that address the most critical aspects of the task
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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.
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"""
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class AgentSpec(BaseModel):
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agent_name: Optional[str] = Field(
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None,
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description="The unique name assigned to the agent, which identifies its role and functionality within the swarm.",
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)
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description: Optional[str] = Field(
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None,
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description="A detailed explanation of the agent's purpose, capabilities, and any specific tasks it is designed to perform.",
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)
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system_prompt: Optional[str] = Field(
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None,
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description="The initial instruction or context provided to the agent, guiding its behavior and responses during execution.",
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)
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model_name: Optional[str] = Field(
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description="The name of the AI model that the agent will utilize for processing tasks and generating outputs. For example: gpt-4o, gpt-4o-mini, openai/o3-mini"
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)
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auto_generate_prompt: Optional[bool] = Field(
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description="A flag indicating whether the agent should automatically create prompts based on the task requirements."
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)
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max_tokens: Optional[int] = Field(
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None,
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description="The maximum number of tokens that the agent is allowed to generate in its responses, limiting output length.",
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)
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temperature: Optional[float] = Field(
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description="A parameter that controls the randomness of the agent's output; lower values result in more deterministic responses."
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)
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role: Optional[str] = Field(
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description="The designated role of the agent within the swarm, which influences its behavior and interaction with other agents."
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)
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max_loops: Optional[int] = Field(
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description="The maximum number of times the agent is allowed to repeat its task, enabling iterative processing if necessary."
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)
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class Agents(BaseModel):
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"""Configuration for a collection of agents that work together as a swarm to accomplish tasks."""
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agents: List[AgentSpec] = Field(
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description="A list containing the specifications of each agent that will participate in the swarm, detailing their roles and functionalities."
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)
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class AgentsBuilder:
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"""A class that automatically builds and manages swarms of AI agents.
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This class handles the creation, coordination and execution of multiple AI agents working
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together as a swarm to accomplish complex tasks. It uses a boss agent to delegate work
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and create new specialized agents as needed.
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Args:
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name (str): The name of the swarm
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description (str): A description of the swarm's purpose
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verbose (bool, optional): Whether to output detailed logs. Defaults to True.
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max_loops (int, optional): Maximum number of execution loops. Defaults to 1.
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"""
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def __init__(
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self,
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name: str = "swarm-creator-01",
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description: str = "This is a swarm that creates swarms",
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verbose: bool = True,
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max_loops: int = 1,
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model_name: str = "gpt-4o",
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return_dictionary: bool = True,
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system_prompt: str = BOSS_SYSTEM_PROMPT,
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):
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self.name = name
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self.description = description
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self.verbose = verbose
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self.max_loops = max_loops
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self.agents_pool = []
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self.model_name = model_name
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self.return_dictionary = return_dictionary
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self.system_prompt = system_prompt
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logger.info(
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f"Initialized AutoSwarmBuilder: {name} {description}"
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)
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def run(
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self, task: str, image_url: str = None, *args, **kwargs
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) -> Tuple[List[Agent], int]:
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"""Run the swarm on a given task.
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Args:
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task (str): The task to be accomplished
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image_url (str, optional): URL of an image input if needed. Defaults to None.
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*args: Variable length argument list
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**kwargs: Arbitrary keyword arguments
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Returns:
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The output from the swarm's execution
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"""
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logger.info(f"Running swarm on task: {task}")
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agents = self._create_agents(task, image_url, *args, **kwargs)
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return agents
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def _create_agents(self, task: str, *args, **kwargs):
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"""Create the necessary agents for a task.
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Args:
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task (str): The task to create agents for
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*args: Variable length argument list
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**kwargs: Arbitrary keyword arguments
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Returns:
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list: List of created agents
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"""
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logger.info("Creating agents for task")
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model = OpenAIFunctionCaller(
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system_prompt=self.system_prompt,
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api_key=os.getenv("OPENAI_API_KEY"),
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temperature=0.1,
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base_model=Agents,
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model_name=self.model_name,
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max_tokens=8192,
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)
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agents_dictionary = model.run(task)
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print(agents_dictionary)
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print(type(agents_dictionary))
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logger.info("Agents successfully created")
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logger.info(f"Agents: {len(agents_dictionary.agents)}")
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if self.return_dictionary:
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logger.info("Returning dictionary")
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# Convert swarm config to dictionary
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agents_dictionary = agents_dictionary.model_dump()
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return agents_dictionary
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else:
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logger.info("Returning agents")
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return self.create_agents(agents_dictionary)
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def create_agents(self, agents_dictionary: Any):
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# Create agents from config
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agents = []
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for agent_config in agents_dictionary.agents:
|
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# Convert dict to AgentConfig if needed
|
||||
if isinstance(agent_config, dict):
|
||||
agent_config = Agents(**agent_config)
|
||||
|
||||
agent = self.build_agent(
|
||||
agent_name=agent_config.model_name,
|
||||
agent_description=agent_config.description,
|
||||
agent_system_prompt=agent_config.system_prompt,
|
||||
model_name=agent_config.model_name,
|
||||
max_loops=agent_config.max_loops,
|
||||
dynamic_temperature_enabled=True,
|
||||
auto_generate_prompt=agent_config.auto_generate_prompt,
|
||||
role=agent_config.role,
|
||||
max_tokens=agent_config.max_tokens,
|
||||
temperature=agent_config.temperature,
|
||||
)
|
||||
agents.append(agent)
|
||||
|
||||
return agents
|
||||
|
||||
def build_agent(
|
||||
self,
|
||||
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,
|
||||
):
|
||||
"""Build a single agent with the given specifications.
|
||||
|
||||
Args:
|
||||
agent_name (str): Name of the agent
|
||||
agent_description (str): Description of the agent's purpose
|
||||
agent_system_prompt (str): The system prompt for the agent
|
||||
|
||||
Returns:
|
||||
Agent: The constructed agent instance
|
||||
"""
|
||||
logger.info(f"Building agent: {agent_name}")
|
||||
agent = Agent(
|
||||
agent_name=agent_name,
|
||||
description=agent_description,
|
||||
system_prompt=agent_system_prompt,
|
||||
model_name=model_name,
|
||||
max_loops=max_loops,
|
||||
dynamic_temperature_enabled=dynamic_temperature_enabled,
|
||||
context_length=200000,
|
||||
output_type="str", # "json", "dict", "csv" OR "string" soon "yaml" and
|
||||
streaming_on=False,
|
||||
auto_generate_prompt=auto_generate_prompt,
|
||||
role=role,
|
||||
max_tokens=max_tokens,
|
||||
temperature=temperature,
|
||||
)
|
||||
|
||||
return agent
|
||||
|
||||
|
||||
# if __name__ == "__main__":
|
||||
# builder = AgentsBuilder(model_name="gpt-4o")
|
||||
# agents = builder.run("Create a swarm that can write a book about the history of the world")
|
||||
# print(agents)
|
||||
# print(type(agents))
|
||||
Loading…
Reference in new issue