swarms/docs/swarms/structs/auto_agent_builder.md

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

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

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

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