AgentRearrange Example¶

Overview

Learn how to create flexible multi-agent workflows using AgentRearrange. Define custom flow patterns with sequential execution (->) and concurrent execution (,) to orchestrate agents in sophisticated workflows.

Prerequisites¶

Before You Begin

Make sure you have: - Python 3.7+ installed - A valid API key for your model provider - The Swarms package installed

Installation¶

pip3 install -U swarms

Environment Setup¶

API Key Configuration

Set your API key in the .env file:

OPENAI_API_KEY="your-api-key-here"

Code Implementation¶

Import Required Modules¶

from swarms import Agent, AgentRearrange

Configure Agents¶

Agent Configuration

Here's how to set up your specialized agents:

# Research Agent
researcher = Agent(
    agent_name="Researcher",
    system_prompt="You are a research specialist. Gather information, analyze data, and provide comprehensive findings.",
    model_name="gpt-4o-mini",
    max_loops=1,
)

# Writer Agent
writer = Agent(
    agent_name="Writer",
    system_prompt="You are a professional writer. Create clear and engaging content based on research findings.",
    model_name="gpt-4o-mini",
    max_loops=1,
)

# Editor Agent
editor = Agent(
    agent_name="Editor",
    system_prompt="You are an expert editor. Review content for clarity, accuracy, and style.",
    model_name="gpt-4o-mini",
    max_loops=1,
)

Initialize AgentRearrange¶

Workflow Setup

Configure AgentRearrange with your agents and flow pattern:

# Sequential flow: Researcher -> Writer -> Editor
flow = "Researcher -> Writer -> Editor"

workflow = AgentRearrange(
    name="content-creation-workflow",
    agents=[researcher, writer, editor],
    flow=flow,
    max_loops=1,
)

Run the Workflow¶

Execute the Workflow

Start the workflow:

result = workflow.run(
    "Research and write a comprehensive article about the impact of AI on healthcare"
)
print(result)

Complete Example¶

Full Implementation

Here's the complete code combined:

from swarms import Agent, AgentRearrange

# Create agents
researcher = Agent(
    agent_name="Researcher",
    system_prompt="You are a research specialist. Gather information, analyze data, and provide comprehensive findings.",
    model_name="gpt-4o-mini",
    max_loops=1,
)

writer = Agent(
    agent_name="Writer",
    system_prompt="You are a professional writer. Create clear and engaging content based on research findings.",
    model_name="gpt-4o-mini",
    max_loops=1,
)

editor = Agent(
    agent_name="Editor",
    system_prompt="You are an expert editor. Review content for clarity, accuracy, and style.",
    model_name="gpt-4o-mini",
    max_loops=1,
)

# Define flow pattern
flow = "Researcher -> Writer -> Editor"

# Create workflow
workflow = AgentRearrange(
    name="content-creation-workflow",
    agents=[researcher, writer, editor],
    flow=flow,
    max_loops=1,
)

# Execute workflow
result = workflow.run(
    "Research and write a comprehensive article about the impact of AI on healthcare"
)
print(result)

Flow Pattern Examples¶

Flow Pattern Syntax

Sequential: "Agent1 -> Agent2 -> Agent3" - Agents run one after another
Parallel: "Agent1, Agent2 -> Agent3" - Agent1 and Agent2 run simultaneously, then Agent3
Mixed: "Agent1 -> Agent2, Agent3 -> Agent4" - Combine sequential and parallel execution

Configuration Options¶

Key Parameters

Parameter	Description	Default
`agents`	List of Agent objects	Required
`flow`	Flow pattern string defining execution order	Required
`max_loops`	Maximum number of execution loops	1
`team_awareness`	Enable sequential awareness for agents	False

Next Steps¶

What to Try Next

Experiment with parallel execution: "Agent1, Agent2 -> Agent3"
Enable team_awareness=True for better agent coordination
Try more complex flows combining sequential and parallel patterns
Use SwarmRouter with swarm_type="AgentRearrange" for unified interface

Troubleshooting¶

Common Issues

Ensure agent names in flow match agent_name exactly
Check for typos in agent names
Verify all agents in flow are included in agents list
Enable verbose mode for debugging: verbose=True