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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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@ -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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