This guide covers setting up and using the Swarms API with tools for creating AI agent swarms.
The Swarms API enables you to conditionally integrate structured outputs and tools into one or multiple agents based on your requirements. You can define tool schemas that are compatible with your chosen model provider - for example, if you’re using OpenAI models, you can use OpenAI-compatible schemas, or if you’re using Anthropic models, you can use Anthropic-compatible schemas.

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Creating a Swarm with Tools

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Step 1: Define Your Tool Dictionary

tool_dictionary = {
    "type": "function",
    "function": {
        "name": "search_topic",
        "description": "Conduct an in-depth search on a specified topic",
        "parameters": {
            "type": "object",
            "properties": {
                "depth": {
                    "type": "integer",
                    "description": "Search depth (1-3)"
                },
                "detailed_queries": {
                    "type": "array",
                    "items": {
                        "type": "string",
                        "description": "Specific search queries"
                    }
                }
            },
            "required": ["depth", "detailed_queries"]
        }
    }
}

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Step 2: Create Agent Configurations

agent_config = {
    "agent_name": "Market Analyst",
    "description": "Analyzes market trends",
    "system_prompt": "You are a financial analyst expert.",
    "model_name": "openai/gpt-4",
    "role": "worker",
    "max_loops": 1,
    "max_tokens": 8192,
    "temperature": 0.5,
    "auto_generate_prompt": False,
    "tools_dictionary": [tool_dictionary]  # Optional: Add tools if needed
}

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Step 3: Create the Swarm Payload

payload = {
    "name": "Your Swarm Name",
    "description": "Swarm description",
    "agents": [agent_config],
    "max_loops": 1,
    "swarm_type": "ConcurrentWorkflow",
    "task": "Your task description",
    "output_type": "dict"
}

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Step 4: Make the API Request

def run_swarm(payload):
    response = requests.post(
        f"{BASE_URL}/v1/swarm/completions",
        headers=headers,
        json=payload
    )
    return response.json()

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Complete Example

Here’s a complete example of a financial analysis swarm: 
def run_financial_analysis_swarm():
    payload = {
        "name": "Financial Analysis Swarm",
        "description": "Market analysis swarm",
        "agents": [
            {
                "agent_name": "Market Analyst",
                "description": "Analyzes market trends",
                "system_prompt": "You are a financial analyst expert.",
                "model_name": "openai/gpt-4",
                "role": "worker",
                "max_loops": 1,
                "max_tokens": 8192,
                "temperature": 0.5,
                "auto_generate_prompt": False,
                "tools_dictionary": [
                    {
                        "type": "function",
                        "function": {
                            "name": "search_topic",
                            "description": "Conduct market research",
                            "parameters": {
                                "type": "object",
                                "properties": {
                                    "depth": {
                                        "type": "integer",
                                        "description": "Search depth (1-3)"
                                    },
                                    "detailed_queries": {
                                        "type": "array",
                                        "items": {"type": "string"}
                                    }
                                },
                                "required": ["depth", "detailed_queries"]
                            }
                        }
                    }
                ]
            }
        ],
        "max_loops": 1,
        "swarm_type": "ConcurrentWorkflow",
        "task": "Analyze top performing tech ETFs",
        "output_type": "dict"
    }
    
    response = requests.post(
        f"{BASE_URL}/v1/swarm/completions",
        headers=headers,
        json=payload
    )
    return response.json()

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Advanced Implementation

Here’s an advanced example with multiple agents and tools: 
import os
import requests
from dotenv import load_dotenv
import json

load_dotenv()

API_KEY = os.getenv("SWARMS_API_KEY")
BASE_URL = "https://api.swarms.world"

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

def run_health_check():
    response = requests.get(f"{BASE_URL}/health", headers=headers)
    return response.json()

def run_single_swarm():
    payload = {
        "name": "Financial Analysis Swarm",
        "description": "Market analysis swarm",
        "agents": [
            {
                "agent_name": "Market Analyst",
                "description": "Analyzes market trends",
                "system_prompt": "You are a financial analyst expert.",
                "model_name": "openai/gpt-4o",
                "role": "worker",
                "max_loops": 1,
                "max_tokens": 8192,
                "temperature": 0.5,
                "auto_generate_prompt": False,
                "tools_dictionary": [
                    {
                        "type": "function",
                        "function": {
                            "name": "search_topic",
                            "description": "Conduct an in-depth search on a specified topic or subtopic, generating a comprehensive array of highly detailed search queries tailored to the input parameters.",
                            "parameters": {
                                "type": "object",
                                "properties": {
                                    "depth": {
                                        "type": "integer",
                                        "description": "Indicates the level of thoroughness for the search. Values range from 1 to 3, where 1 represents a superficial search and 3 signifies an exploration of the topic.",
                                    },
                                    "detailed_queries": {
                                        "type": "array",
                                        "description": "An array of highly specific search queries that are generated based on the input query and the specified depth. Each query should be designed to elicit detailed and relevant information from various sources.",
                                        "items": {
                                            "type": "string",
                                            "description": "Each item in this array should represent a unique search query that targets a specific aspect of the main topic, ensuring a comprehensive exploration of the subject matter.",
                                        },
                                    },
                                },
                                "required": ["depth", "detailed_queries"],
                            },
                        },
                    },
                ],
            },
            {
                "agent_name": "Economic Forecaster",
                "description": "Predicts economic trends",
                "system_prompt": "You are an expert in economic forecasting.",
                "model_name": "gpt-4o",
                "role": "worker",
                "max_loops": 1,
                "max_tokens": 8192,
                "temperature": 0.5,
                "auto_generate_prompt": False,
                "tools_dictionary": [
                    {
                        "type": "function",
                        "function": {
                            "name": "search_topic",
                            "description": "Conduct an in-depth search on a specified topic or subtopic, generating a comprehensive array of highly detailed search queries tailored to the input parameters.",
                            "parameters": {
                                "type": "object",
                                "properties": {
                                    "depth": {
                                        "type": "integer",
                                        "description": "Indicates the level of thoroughness for the search. Values range from 1 to 3, where 1 represents a superficial search and 3 signifies an exploration of the topic.",
                                    },
                                    "detailed_queries": {
                                        "type": "array",
                                        "description": "An array of highly specific search queries that are generated based on the input query and the specified depth. Each query should be designed to elicit detailed and relevant information from various sources.",
                                        "items": {
                                            "type": "string",
                                            "description": "Each item in this array should represent a unique search query that targets a specific aspect of the main topic, ensuring a comprehensive exploration of the subject matter.",
                                        },
                                    },
                                },
                                "required": ["depth", "detailed_queries"],
                            },
                        },
                    },
                ],
            },
        ],
        "max_loops": 1,
        "swarm_type": "ConcurrentWorkflow",
        "task": "What are the best etfs and index funds for ai and tech?",
        "output_type": "dict",
    }

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

    print(response)
    print(response.status_code)
    output = response.json()
    return json.dumps(output, indent=4)

if __name__ == "__main__":
    result = run_single_swarm()
    print("Swarm Result:")
    print(result)

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Best Practices

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Error Handling

Always implement proper error handling: 
def safe_run_swarm(payload):
    try:
        response = requests.post(
            f"{BASE_URL}/v1/swarm/completions",
            headers=headers,
            json=payload
        )
        response.raise_for_status()
        return response.json()
    except requests.exceptions.RequestException as e:
        print(f"Error running swarm: {e}")
        return None

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Security & Configuration

  • Environment Variables: Never hardcode API keys
  • Tool Design: Keep tools simple and focused
  • Testing: Validate swarm configurations before production use

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FAQ

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Troubleshooting

This advanced implementation demonstrates how to create a multi-agent swarm with sophisticated tool configurations for financial analysis tasks.