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AgentRearrange

Dynamically reorganizes agents to optimize task performance and efficiency

Swarm Type: AgentRearrange

Overview

The AgentRearrange swarm type dynamically reorganizes the workflow between agents based on task requirements and performance metrics. This architecture is particularly useful when the effectiveness of agents depends on their sequence or arrangement, allowing for optimal task distribution and execution flow.

Key features:

Dynamic Reorganization: Automatically adjusts agent order based on task needs
Performance Optimization: Optimizes workflow for maximum efficiency
Adaptive Sequencing: Learns from execution patterns to improve arrangement
Flexible Task Distribution: Distributes work based on agent capabilities

Use Cases

Complex workflows where task order matters
Multi-step processes requiring optimization
Tasks where agent performance varies by sequence
Adaptive workflow management systems

API Usage

Basic AgentRearrange Example

=== "Shell (curl)" bash curl -X POST "https://api.swarms.world/v1/swarm/completions" \ -H "x-api-key: $SWARMS_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "name": "Document Processing Rearrange", "description": "Process documents with dynamic agent reorganization", "swarm_type": "AgentRearrange", "task": "Analyze this legal document and extract key insights, then summarize findings and identify action items", "agents": [ { "agent_name": "Document Analyzer", "description": "Analyzes document content and structure", "system_prompt": "You are an expert document analyst. Extract key information, themes, and insights from documents.", "model_name": "gpt-4o", "max_loops": 1, "temperature": 0.3 }, { "agent_name": "Legal Expert", "description": "Provides legal context and interpretation", "system_prompt": "You are a legal expert. Analyze documents for legal implications, risks, and compliance issues.", "model_name": "gpt-4o", "max_loops": 1, "temperature": 0.2 }, { "agent_name": "Summarizer", "description": "Creates concise summaries and action items", "system_prompt": "You are an expert at creating clear, actionable summaries from complex information.", "model_name": "gpt-4o", "max_loops": 1, "temperature": 0.4 } ], "rearrange_flow": "Summarizer -> Legal Expert -> Document Analyzer", "max_loops": 1 }'

=== "Python (requests)" ```python import requests import json

API_BASE_URL = "https://api.swarms.world"
API_KEY = "your_api_key_here"

headers = {
    "x-api-key": API_KEY,
    "Content-Type": "application/json"
}

swarm_config = {
    "name": "Document Processing Rearrange",
    "description": "Process documents with dynamic agent reorganization",
    "swarm_type": "AgentRearrange",
    "task": "Analyze this legal document and extract key insights, then summarize findings and identify action items",
    "agents": [
        {
            "agent_name": "Document Analyzer",
            "description": "Analyzes document content and structure",
            "system_prompt": "You are an expert document analyst. Extract key information, themes, and insights from documents.",
            "model_name": "gpt-4o",
            "max_loops": 1,
            "temperature": 0.3
        },
        {
            "agent_name": "Legal Expert", 
            "description": "Provides legal context and interpretation",
            "system_prompt": "You are a legal expert. Analyze documents for legal implications, risks, and compliance issues.",
            "model_name": "gpt-4o",
            "max_loops": 1,
            "temperature": 0.2
        },
        {
            "agent_name": "Summarizer",
            "description": "Creates concise summaries and action items", 
            "system_prompt": "You are an expert at creating clear, actionable summaries from complex information.",
            "model_name": "gpt-4o",
            "max_loops": 1,
            "temperature": 0.4
        }
    ],
    "rearrange_flow": "Summarizer -> Legal Expert -> Document Analyzer",
    "max_loops": 1
}

response = requests.post(
    f"{API_BASE_URL}/v1/swarm/completions",
    headers=headers,
    json=swarm_config
)

if response.status_code == 200:
    result = response.json()
    print("AgentRearrange swarm completed successfully!")
    print(f"Cost: ${result['metadata']['billing_info']['total_cost']}")
    print(f"Execution time: {result['metadata']['execution_time_seconds']} seconds")
    print(f"Output: {result['output']}")
else:
    print(f"Error: {response.status_code} - {response.text}")
```

Example Response:

{
    "job_id": "swarms-Uc8R7UcepLmNNPwcU7JC6YPy5wiI",
    "status": "success",
    "swarm_name": "Document Processing Rearrange",
    "description": "Process documents with dynamic agent reorganization",
    "swarm_type": "AgentRearrange",
    "output": [
        {
            "role": "Summarizer",
            "content": "\"Of course! Please provide the legal document you would like me to analyze, and I'll help extract key insights, summarize findings, and identify any action items.\""
        },
        {
            "role": "Legal Expert",
            "content": "\"\"Absolutely! Please upload or describe the legal document you need assistance with, and I'll provide an analysis that highlights key insights, summarizes the findings, and identifies any action items that may be necessary.\"\""
        },
        {
            "role": "Document Analyzer",
            "content": "\"Of course! Please provide the legal document you would like me to analyze, and I'll help extract key insights, summarize findings, and identify any action items.\""
        }
    ],
    "number_of_agents": 3,
    "service_tier": "standard",
    "execution_time": 7.898931264877319,
    "usage": {
        "input_tokens": 22,
        "output_tokens": 144,
        "total_tokens": 166,
        "billing_info": {
            "cost_breakdown": {
                "agent_cost": 0.03,
                "input_token_cost": 0.000066,
                "output_token_cost": 0.00216,
                "token_counts": {
                    "total_input_tokens": 22,
                    "total_output_tokens": 144,
                    "total_tokens": 166
                },
                "num_agents": 3,
                "service_tier": "standard",
                "night_time_discount_applied": true
            },
            "total_cost": 0.032226,
            "discount_active": true,
            "discount_type": "night_time",
            "discount_percentage": 75
        }
    }
}

Configuration Options

Parameter	Type	Description	Default
`rearrange_flow`	string	Instructions for how agents should be rearranged	None
`agents`	Array	List of agents to be dynamically arranged	Required
`max_loops`	integer	Maximum rearrangement iterations	1

Best Practices

Provide clear rearrange_flow instructions for optimal reorganization
Design agents with complementary but flexible roles
Use when task complexity requires adaptive sequencing
Monitor execution patterns to understand rearrangement decisions

SequentialWorkflow - For fixed sequential processing
AutoSwarmBuilder - For automatic swarm construction
HierarchicalSwarm - For structured agent hierarchies

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