AutoSwarmBuilder Documentation¶

The AutoSwarmBuilder is a powerful class that automatically builds and manages swarms of AI agents to accomplish complex tasks. It uses a boss agent to delegate work and create specialized agents as needed.

Overview¶

The AutoSwarmBuilder is designed to:

Automatically create and coordinate multiple AI agents
Delegate tasks to specialized agents
Manage communication between agents
Handle complex workflows through a swarm router

Parameters¶

Parameter	Type	Default	Description
name	str	None	The name of the swarm
description	str	None	A description of the swarm's purpose
verbose	bool	True	Whether to output detailed logs
max_loops	int	1	Maximum number of execution loops
random_models	bool	True	Whether to use random models for agents

Core Methods¶

run(task: str, *args, **kwargs)¶

Executes the swarm on a given task.

Parameters:

task (str): The task to execute
*args: Additional positional arguments
**kwargs: Additional keyword arguments

Returns:

The result of the swarm execution

create_agents(task: str)¶

Creates specialized agents for a given task.

Parameters:

task (str): The task to create agents for

Returns:

List[Agent]: List of created agents

build_agent(agent_name: str, agent_description: str, agent_system_prompt: str)¶

Builds a single agent with specified parameters.

Parameters: - agent_name (str): Name of the agent

agent_description (str): Description of the agent
agent_system_prompt (str): System prompt for the agent

Returns:

Agent: The constructed agent

batch_run(tasks: List[str])¶

Executes the swarm on multiple tasks.

Parameters:

tasks (List[str]): List of tasks to execute

Returns:

List[Any]: Results from each task execution

Examples¶

Example 1: Content Creation Swarm¶

from swarms.structs.auto_swarm_builder import AutoSwarmBuilder

# Initialize the swarm builder
swarm = AutoSwarmBuilder(
    name="Content Creation Swarm",
    description="A swarm specialized in creating high-quality content"
)

# Run the swarm on a content creation task
result = swarm.run(
    "Create a comprehensive blog post about artificial intelligence in healthcare, "
    "including current applications, future trends, and ethical considerations."
)

Example 2: Data Analysis Swarm¶

from swarms.structs.auto_swarm_builder import AutoSwarmBuilder

# Initialize the swarm builder
swarm = AutoSwarmBuilder(
    name="Data Analysis Swarm",
    description="A swarm specialized in data analysis and visualization"
)

# Run the swarm on a data analysis task
result = swarm.run(
    "Analyze the provided sales data and create a detailed report with visualizations "
    "showing trends, patterns, and recommendations for improvement."
)

Example 3: Batch Processing Multiple Tasks¶

from swarms.structs.auto_swarm_builder import AutoSwarmBuilder

# Initialize the swarm builder
swarm = AutoSwarmBuilder(
    name="Multi-Task Swarm",
    description="A swarm capable of handling multiple diverse tasks"
)

# Define multiple tasks
tasks = [
    "Create a marketing strategy for a new product launch",
    "Analyze customer feedback and generate improvement suggestions",
    "Develop a project timeline for the next quarter"
]

# Run the swarm on all tasks
results = swarm.batch_run(tasks)

Best Practices¶

Task Definition

Provide clear, specific task descriptions
Include any relevant context or constraints
Specify expected output format if needed

Configuration

Set appropriate max_loops based on task complexity
Use verbose=True during development for debugging
Consider using random_models=True for diverse agent capabilities

Error Handling

The class includes comprehensive error handling
All methods include try-catch blocks with detailed logging
Errors are propagated with full stack traces for debugging

Notes¶

Architecture

The AutoSwarmBuilder uses a sophisticated boss agent system to coordinate tasks
Agents are created dynamically based on task requirements
The system includes built-in logging and error handling
Results are returned in a structured format for easy processing