Swarms API Documentation

Enterprise-grade Agent Swarm Management API

Base URL: https://api.swarms.world
API Key Management: https://swarms.world/platform/api-keys

Overview

The Swarms API provides a robust, scalable infrastructure for deploying and managing intelligent agent swarms in the cloud. This enterprise-grade API enables organizations to create, execute, and orchestrate sophisticated AI agent workflows without managing the underlying infrastructure.

Key capabilities include:

• Intelligent Swarm Management: Create and execute swarms of specialized AI agents that collaborate to solve complex tasks
• Automatic Agent Generation: Dynamically create optimized agents based on task requirements
• Multiple Swarm Architectures: Choose from various swarm patterns to match your specific workflow needs
• Scheduled Execution: Set up automated, scheduled swarm executions
• Comprehensive Logging: Track and analyze all API interactions
• Cost Management: Predictable, transparent pricing with optimized resource utilization
• Enterprise Security: Full API key authentication and management

Swarms API is designed for production use cases requiring sophisticated AI orchestration, with applications in finance, healthcare, legal, research, and other domains where complex reasoning and multi-agent collaboration are needed.

Authentication

All API requests require a valid API key, which must be included in the header of each request:

x-api-key: your_api_key_here

API keys can be obtained and managed at https://swarms.world/platform/api-keys.

API Reference

Endpoints Summary

Endpoint	Method	Description
/health	GET	Simple health check endpoint
/v1/swarm/completions	POST	Run a swarm with specified configuration
/v1/swarm/batch/completions	POST	Run multiple swarms in batch mode
/v1/swarm/schedule	POST	Schedule a swarm to run at a specific time
/v1/swarm/schedule	GET	Get all scheduled swarm jobs
/v1/swarm/schedule/{job_id}	DELETE	Cancel a scheduled swarm job
/v1/swarm/logs	GET	Retrieve API request logs

SwarmType Reference

The swarm_type parameter defines the architecture and collaboration pattern of the agent swarm:

SwarmType	Description
AgentRearrange	Dynamically reorganizes the workflow between agents based on task requirements
MixtureOfAgents	Combines multiple agent types to tackle diverse aspects of a problem
SpreadSheetSwarm	Specialized for spreadsheet data analysis and manipulation
SequentialWorkflow	Agents work in a predefined sequence, each handling specific subtasks
ConcurrentWorkflow	Multiple agents work simultaneously on different aspects of the task
GroupChat	Agents collaborate in a discussion format to solve problems
MultiAgentRouter	Routes subtasks to specialized agents based on their capabilities
AutoSwarmBuilder	Automatically designs and builds an optimal swarm based on the task
HiearchicalSwarm	Organizes agents in a hierarchical structure with managers and workers
MajorityVoting	Uses a consensus mechanism where multiple agents vote on the best solution
auto	Automatically selects the most appropriate swarm type for the given task

Endpoint Details

Health Check

Check if the API service is available and functioning correctly.

Endpoint: /health
Method: GET
Rate Limit: 100 requests per 60 seconds

Example Request:

curl -X GET "https://api.swarms.world/health" \
     -H "x-api-key: your_api_key_here"

Example Response:

{
  "status": "ok"
}

Run Swarm

Run a swarm with the specified configuration to complete a task.

Endpoint: /v1/swarm/completions
Method: POST
Rate Limit: 100 requests per 60 seconds

Request Parameters:

Field	Type	Description	Required
name	string	Identifier for the swarm	No
description	string	Description of the swarm's purpose	No
agents	Array	List of agent specifications	No
max_loops	integer	Maximum number of execution loops	No
swarm_type	SwarmType	Architecture of the swarm	No
rearrange_flow	string	Instructions for rearranging task flow	No
task	string	The main task for the swarm to accomplish	Yes
img	string	Optional image URL for the swarm	No
return_history	boolean	Whether to return execution history	No
rules	string	Guidelines for swarm behavior	No
schedule	ScheduleSpec	Scheduling information	No

Example Request:

# Run single swarm
curl -X POST "https://api.swarms.world/v1/swarm/completions" \
  -H "x-api-key: $SWARMS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "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
      },
      {
        "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
      }
    ],
    "max_loops": 1,
    "swarm_type": "ConcurrentWorkflow",
    "task": "What are the best etfs and index funds for ai and tech?",
    "output_type": "dict"
  }'

Example Response:

{
  "status": "success",
  "swarm_name": "financial-analysis-swarm",
  "description": "Analyzes financial data for risk assessment",
  "swarm_type": "SequentialWorkflow",
  "task": "Analyze the provided quarterly financials for Company XYZ and identify potential risk factors. Summarize key insights and provide recommendations for risk mitigation.",
  "output": {
    "financial_analysis": {
      "risk_factors": [...],
      "key_insights": [...],
      "recommendations": [...]
    }
  },
  "metadata": {
    "max_loops": 2,
    "num_agents": 3,
    "execution_time_seconds": 12.45,
    "completion_time": 1709563245.789,
    "billing_info": {
      "cost_breakdown": {
        "agent_cost": 0.03,
        "input_token_cost": 0.002134,
        "output_token_cost": 0.006789,
        "token_counts": {
          "total_input_tokens": 1578,
          "total_output_tokens": 3456,
          "total_tokens": 5034,
          "per_agent": {...}
        },
        "num_agents": 3,
        "execution_time_seconds": 12.45
      },
      "total_cost": 0.038923
    }
  }
}

Run Batch Completions

Run multiple swarms as a batch operation.

Endpoint: /v1/swarm/batch/completions
Method: POST
Rate Limit: 100 requests per 60 seconds

Request Parameters:

Field	Type	Description	Required
swarms	Array	List of swarm specifications	Yes

Example Request:

# Batch swarm completions
curl -X POST "https://api.swarms.world/v1/swarm/batch/completions" \
  -H "x-api-key: $SWARMS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '[
    {
      "name": "Batch Swarm 1",
      "description": "First swarm in the batch",
      "agents": [
        {
          "agent_name": "Research Agent",
          "description": "Conducts research",
          "system_prompt": "You are a research assistant.",
          "model_name": "gpt-4o",
          "role": "worker",
          "max_loops": 1
        },
        {
          "agent_name": "Analysis Agent",
          "description": "Analyzes data",
          "system_prompt": "You are a data analyst.",
          "model_name": "gpt-4o",
          "role": "worker",
          "max_loops": 1
        }
      ],
      "max_loops": 1,
      "swarm_type": "SequentialWorkflow",
      "task": "Research AI advancements."
    },
    {
      "name": "Batch Swarm 2",
      "description": "Second swarm in the batch",
      "agents": [
        {
          "agent_name": "Writing Agent",
          "description": "Writes content",
          "system_prompt": "You are a content writer.",
          "model_name": "gpt-4o",
          "role": "worker",
          "max_loops": 1
        },
        {
          "agent_name": "Editing Agent",
          "description": "Edits content",
          "system_prompt": "You are an editor.",
          "model_name": "gpt-4o",
          "role": "worker",
          "max_loops": 1
        }
      ],
      "max_loops": 1,
      "swarm_type": "SequentialWorkflow",
      "task": "Write a summary of AI research."
    }
  ]'

Example Response:

[
  {
    "status": "success",
    "swarm_name": "risk-analysis",
    "task": "Analyze risk factors for investment portfolio",
    "output": {...},
    "metadata": {...}
  },
  {
    "status": "success",
    "swarm_name": "market-sentiment",
    "task": "Assess current market sentiment for technology sector",
    "output": {...},
    "metadata": {...}
  }
]

Schedule Swarm

Schedule a swarm to run at a specific time.

Endpoint: /v1/swarm/schedule
Method: POST
Rate Limit: 100 requests per 60 seconds

Request Parameters:

Field	Type	Description	Required
name	string	Identifier for the swarm	No
description	string	Description of the swarm's purpose	No
agents	Array	List of agent specifications	No
max_loops	integer	Maximum number of execution loops	No
swarm_type	SwarmType	Architecture of the swarm	No
task	string	The main task for the swarm to accomplish	Yes
schedule	ScheduleSpec	Scheduling information	Yes

Example Request:

curl -X POST "https://api.swarms.world/v1/swarm/schedule" \
     -H "x-api-key: your_api_key_here" \
     -H "Content-Type: application/json" \
     -d '{
       "name": "daily-market-analysis",
       "description": "Daily analysis of market conditions",
       "task": "Analyze today's market movements and prepare a summary report",
       "schedule": {
         "scheduled_time": "2025-03-05T17:00:00Z",
         "timezone": "UTC"
       }
     }'

Example Response:

{
  "status": "success",
  "message": "Swarm scheduled successfully",
  "job_id": "swarm_daily-market-analysis_1709563245",
  "scheduled_time": "2025-03-05T17:00:00Z",
  "timezone": "UTC"
}

Get Scheduled Jobs

Retrieve all scheduled swarm jobs.

Endpoint: /v1/swarm/schedule
Method: GET
Rate Limit: 100 requests per 60 seconds

Example Request:

curl -X GET "https://api.swarms.world/v1/swarm/schedule" \
     -H "x-api-key: your_api_key_here"

Example Response:

{
  "status": "success",
  "scheduled_jobs": [
    {
      "job_id": "swarm_daily-market-analysis_1709563245",
      "swarm_name": "daily-market-analysis",
      "scheduled_time": "2025-03-05T17:00:00Z",
      "timezone": "UTC"
    },
    {
      "job_id": "swarm_weekly-report_1709563348",
      "swarm_name": "weekly-report",
      "scheduled_time": "2025-03-09T12:00:00Z",
      "timezone": "UTC"
    }
  ]
}

Cancel Scheduled Job

Cancel a previously scheduled swarm job.

Endpoint: /v1/swarm/schedule/{job_id}
Method: DELETE
Rate Limit: 100 requests per 60 seconds

Path Parameters:

Parameter	Description
job_id	ID of the scheduled job to cancel

Example Request:

curl -X DELETE "https://api.swarms.world/v1/swarm/schedule/swarm_daily-market-analysis_1709563245" \
     -H "x-api-key: your_api_key_here"

Example Response:

{
  "status": "success",
  "message": "Scheduled job cancelled successfully",
  "job_id": "swarm_daily-market-analysis_1709563245"
}

Get API Logs

Retrieve logs of API requests made with your API key.

Endpoint: /v1/swarm/logs
Method: GET
Rate Limit: 100 requests per 60 seconds

Example Request:

curl -X GET "https://api.swarms.world/v1/swarm/logs" \
     -H "x-api-key: your_api_key_here"

Example Response:

{
  "status": "success",
  "count": 25,
  "logs": [
    {
      "id": "log_id_12345",
      "api_key": "api_key_redacted",
      "data": {
        "action": "run_swarm",
        "swarm_name": "financial-analysis-swarm",
        "task": "Analyze quarterly financials...",
        "timestamp": "2025-03-04T14:22:45Z"
      }
    },
    ...
  ]
}

Data Models

SwarmSpec

The SwarmSpec model defines the configuration of a swarm.

Field	Type	Description	Required
name	string	Identifier for the swarm	No
description	string	Description of the swarm's purpose	No
agents	Array	List of agent specifications	No
max_loops	integer	Maximum number of execution loops	No
swarm_type	SwarmType	Architecture of the swarm	No
rearrange_flow	string	Instructions for rearranging task flow	No
task	string	The main task for the swarm to accomplish	Yes
img	string	Optional image URL for the swarm	No
return_history	boolean	Whether to return execution history	No
rules	string	Guidelines for swarm behavior	No
schedule	ScheduleSpec	Scheduling information	No

AgentSpec

The AgentSpec model defines the configuration of an individual agent.

Field	Type	Description	Required
agent_name	string	Unique name for the agent	Yes*
description	string	Description of the agent's purpose	No
system_prompt	string	Instructions for the agent	No
model_name	string	AI model to use (e.g., "gpt-4o")	Yes*
auto_generate_prompt	boolean	Whether to auto-generate prompts	No
max_tokens	integer	Maximum tokens in response	No
temperature	float	Randomness of responses (0-1)	No
role	string	Agent's role in the swarm	No
max_loops	integer	Maximum iterations for this agent	No

*Required if agents are manually specified; not required if using auto-generated agents

ScheduleSpec

The ScheduleSpec model defines when a swarm should be executed.

Field	Type	Description	Required
scheduled_time	datetime	Time when the swarm should run	Yes
timezone	string	Timezone for the scheduled time	No (defaults to "UTC")

Production Examples

Python Examples

Financial Risk Assessment (Python)

This example demonstrates creating a swarm for comprehensive financial risk assessment.

import requests
import json
from datetime import datetime, timedelta

# API Configuration
API_BASE_URL = "https://api.swarms.world"
API_KEY = "your_api_key_here"
HEADERS = {
    "x-api-key": API_KEY,
    "Content-Type": "application/json"
}

def financial_risk_assessment(company_data, market_conditions, risk_tolerance):
    """
    Creates and runs a swarm to perform comprehensive financial risk assessment.

    Args:
        company_data (str): Description or data about the company
        market_conditions (str): Current market conditions
        risk_tolerance (str): Risk tolerance level (e.g., "conservative", "moderate", "aggressive")

    Returns:
        dict: Risk assessment results
    """
    # Prepare the task description with all relevant information
    task = f"""
    Perform a comprehensive financial risk assessment with the following data:

    COMPANY DATA:
    {company_data}

    MARKET CONDITIONS:
    {market_conditions}

    RISK TOLERANCE:
    {risk_tolerance}

    Analyze all potential risk factors including market risks, credit risks, 
    operational risks, and regulatory compliance risks. Quantify each risk factor 
    on a scale of 1-10 and provide specific mitigation strategies.

    Return a detailed report with executive summary, risk scores, detailed analysis,
    and actionable recommendations.
    """

    # Define specialized financial agents
    financial_analysts = [
        {
            "agent_name": "MarketAnalyst",
            "description": "Specialist in market risk assessment and forecasting",
            "system_prompt": "You are an expert market analyst with deep expertise in financial markets. Analyze market conditions, trends, and external factors that could impact financial performance. Provide quantitative and qualitative analysis of market-related risks.",
            "model_name": "gpt-4o",
            "temperature": 0.3,
            "role": "analyst",
            "max_loops": 1
        },
        {
            "agent_name": "CreditRiskAnalyst",
            "description": "Expert in assessing credit and counterparty risks",
            "system_prompt": "You are a specialist in credit risk analysis with experience in banking and financial institutions. Evaluate creditworthiness, default probabilities, and counterparty exposures. Provide detailed analysis of credit-related risks and recommended safeguards.",
            "model_name": "gpt-4o",
            "temperature": 0.2,
            "role": "analyst",
            "max_loops": 1
        },
        {
            "agent_name": "RegulatoryExpert",
            "description": "Expert in financial regulations and compliance",
            "system_prompt": "You are a regulatory compliance expert with deep knowledge of financial regulations. Identify potential regulatory risks, compliance issues, and governance concerns. Recommend compliance measures and risk mitigation strategies.",
            "model_name": "gpt-4o",
            "temperature": 0.2,
            "role": "analyst",
            "max_loops": 1
        },
        {
            "agent_name": "RiskSynthesizer",
            "description": "Integrates all risk factors into comprehensive assessment",
            "system_prompt": "You are a senior risk management professional responsible for synthesizing multiple risk analyses into a coherent, comprehensive risk assessment. Integrate analyses from various domains, resolve conflicting assessments, and provide a holistic view of risk exposure with prioritized recommendations.",
            "model_name": "gpt-4o",
            "temperature": 0.4,
            "role": "manager",
            "max_loops": 1
        }
    ]

    # Create the swarm specification
    swarm_spec = {
        "name": "financial-risk-assessment",
        "description": "Comprehensive financial risk assessment swarm",
        "agents": financial_analysts,
        "max_loops": 2,
        "swarm_type": "HiearchicalSwarm",
        "task": task,
        "return_history": True
    }

    # Execute the swarm
    response = requests.post(
        f"{API_BASE_URL}/v1/swarm/completions",
        headers=HEADERS,
        json=swarm_spec
    )

    if response.status_code == 200:
        result = response.json()
        print(f"Risk assessment completed. Cost: ${result['metadata']['billing_info']['total_cost']}")
        return result["output"]
    else:
        print(f"Error: {response.status_code} - {response.text}")
        return None

# Usage example
if __name__ == "__main__":
    company_data = """
    XYZ Financial Services
    Annual Revenue: $125M
    Current Debt: $45M
    Credit Rating: BBB+
    Primary Markets: North America, Europe
    Key Products: Asset management, retirement planning, commercial lending
    Recent Events: Expanding into Asian markets, New CEO appointed 6 months ago
    """

    market_conditions = """
    Current interest rates rising (Federal Reserve increased rates by 0.25% last month)
    Inflation at 3.2% (12-month outlook projects 3.5-4.0%)
    Market volatility index (VIX) at 22.4 (elevated)
    Regulatory environment: New financial reporting requirements taking effect next quarter
    Sector performance: Financial services sector underperforming broader market by 2.7%
    """

    risk_tolerance = "moderate"

    result = financial_risk_assessment(company_data, market_conditions, risk_tolerance)

    if result:
        # Process and use the risk assessment
        print(json.dumps(result, indent=2))

        # Optionally, schedule a follow-up assessment
        tomorrow = datetime.utcnow() + timedelta(days=30)
        schedule_spec = {
            "name": "monthly-risk-update",
            "description": "Monthly update to risk assessment",
            "task": f"Update the risk assessment for XYZ Financial Services based on current market conditions. Previous assessment: {json.dumps(result)}",
            "schedule": {
                "scheduled_time": tomorrow.isoformat() + "Z",
                "timezone": "UTC"
            }
        }

        schedule_response = requests.post(
            f"{API_BASE_URL}/v1/swarm/schedule",
            headers=HEADERS,
            json=schedule_spec
        )

        if schedule_response.status_code == 200:
            print("Follow-up assessment scheduled successfully")
            print(schedule_response.json())

Healthcare Patient Data Analysis (Python)

This example demonstrates creating a swarm for analyzing patient health data and generating insights.

import requests
import json
import os
from datetime import datetime

# API Configuration
API_BASE_URL = "https://api.swarms.world"
API_KEY = os.environ.get("SWARMS_API_KEY")
HEADERS = {
    "x-api-key": API_KEY,
    "Content-Type": "application/json"
}

def analyze_patient_health_data(patient_data, medical_history, lab_results, treatment_goals):
    """
    Creates and runs a swarm to analyze patient health data and generate insights.

    Args:
        patient_data (str): Basic patient information
        medical_history (str): Patient's medical history
        lab_results (str): Recent laboratory results
        treatment_goals (str): Treatment objectives

    Returns:
        dict: Comprehensive health analysis and recommendations
    """
    # Prepare the detailed task description
    task = f"""
    Perform a comprehensive analysis of the following patient health data:

    PATIENT INFORMATION:
    {patient_data}

    MEDICAL HISTORY:
    {medical_history}

    LABORATORY RESULTS:
    {lab_results}

    TREATMENT GOALS:
    {treatment_goals}

    Analyze all aspects of the patient's health status, identify potential concerns,
    evaluate treatment effectiveness, and provide evidence-based recommendations for
    optimizing care. Consider medication interactions, lifestyle factors, and preventive measures.

    Return a detailed clinical report with key findings, risk stratification, 
    prioritized recommendations, and suggested follow-up timeline.
    """

    # Create the swarm specification with auto-generated agents
    # (letting the system create specialized medical experts)
    swarm_spec = {
        "name": "patient-health-analysis",
        "description": "Comprehensive patient health data analysis",
        "swarm_type": "AutoSwarmBuilder",
        "task": task,
        "max_loops": 3,
        "return_history": True
    }

    # Execute the swarm
    try:
        response = requests.post(
            f"{API_BASE_URL}/v1/swarm/completions",
            headers=HEADERS,
            json=swarm_spec
        )

        response.raise_for_status()
        result = response.json()

        # Log the execution metadata
        execution_time = result["metadata"]["execution_time_seconds"]
        cost = result["metadata"]["billing_info"]["total_cost"]
        num_agents = result["metadata"]["num_agents"]

        print(f"Analysis completed in {execution_time:.2f} seconds")
        print(f"Used {num_agents} specialized medical agents")
        print(f"Total cost: ${cost:.4f}")

        # Return just the analysis results
        return result["output"]

    except requests.exceptions.RequestException as e:
        print(f"API request failed: {str(e)}")
        if hasattr(e, 'response') and e.response:
            print(f"Response: {e.response.text}")
        return None
    except Exception as e:
        print(f"Error: {str(e)}")
        return None

# Usage example
if __name__ == "__main__":
    # Sample patient data (would typically come from EHR system)
    patient_data = """
    ID: PT-28456
    Age: 67
    Gender: Female
    Height: 162 cm
    Weight: 78 kg
    Vitals:
    - Blood Pressure: 142/88 mmHg
    - Heart Rate: 76 bpm
    - Respiratory Rate: 16/min
    - Temperature: 37.1°C
    - Oxygen Saturation: 97%
    """

    medical_history = """
    Diagnoses:
    - Type 2 Diabetes Mellitus (diagnosed 12 years ago)
    - Hypertension (diagnosed 8 years ago)
    - Osteoarthritis (knees, diagnosed 5 years ago)
    - Hyperlipidemia

    Surgical History:
    - Cholecystectomy (15 years ago)
    - Right knee arthroscopy (3 years ago)

    Medications:
    - Metformin 1000mg BID
    - Lisinopril 20mg daily
    - Atorvastatin 40mg daily
    - Aspirin 81mg daily
    - Acetaminophen 500mg PRN for joint pain

    Allergies:
    - Penicillin (rash)
    - Sulfa drugs (hives)

    Family History:
    - Father: MI at age 70, died at 76
    - Mother: Breast cancer at 68, Type 2 Diabetes, died at 82
    - Sister: Type 2 Diabetes, Hypertension
    """

    lab_results = """
    CBC (2 days ago):
    - WBC: 7.2 x10^9/L (normal)
    - RBC: 4.1 x10^12/L (low-normal)
    - Hemoglobin: 12.8 g/dL (low-normal)
    - Hematocrit: 38% (low-normal)
    - Platelets: 245 x10^9/L (normal)

    Comprehensive Metabolic Panel:
    - Glucose (fasting): 142 mg/dL (elevated)
    - HbA1c: 7.8% (elevated)
    - BUN: 22 mg/dL (normal)
    - Creatinine: 1.1 mg/dL (normal)
    - eGFR: 62 mL/min/1.73m² (mildly reduced)
    - Sodium: 138 mEq/L (normal)
    - Potassium: 4.2 mEq/L (normal)
    - Chloride: 101 mEq/L (normal)
    - Calcium: 9.4 mg/dL (normal)
    - ALT: 32 U/L (normal)
    - AST: 28 U/L (normal)

    Lipid Panel:
    - Total Cholesterol: 198 mg/dL
    - Triglycerides: 172 mg/dL (elevated)
    - HDL: 42 mg/dL (low)
    - LDL: 122 mg/dL (borderline elevated)

    Urinalysis:
    - Microalbumin/Creatinine ratio: 45 mg/g (elevated)
    """

    treatment_goals = """
    Primary Goals:
    - Improve glycemic control (target HbA1c < 7.0%)
    - Blood pressure control (target < 130/80 mmHg)
    - Lipid management (target LDL < 100 mg/dL)
    - Renal protection (reduce microalbuminuria)
    - Weight management (target BMI < 27)
    - Pain management for osteoarthritis
    - Maintain functional independence

    Patient Preferences:
    - Prefers to minimize medication changes if possible
    - Interested in dietary approaches
    - Concerned about memory changes
    - Limited exercise tolerance due to knee pain
    """

    result = analyze_patient_health_data(patient_data, medical_history, lab_results, treatment_goals)

    if result:
        # Write the analysis to a report file
        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
        with open(f"patient_analysis_{timestamp}.json", "w") as f:
            json.dump(result, f, indent=2)

        print(f"Analysis saved to patient_analysis_{timestamp}.json")

        # Display key findings
        if "key_findings" in result:
            print("\nKEY FINDINGS:")
            for i, finding in enumerate(result["key_findings"]):
                print(f"  {i+1}. {finding}")

        # Display recommendations
        if "recommendations" in result:
            print("\nRECOMMENDATIONS:")
            for i, rec in enumerate(result["recommendations"]):
                print(f"  {i+1}. {rec}")

TypeScript/NodeJS Examples

Financial Fraud Detection (TypeScript)

This example demonstrates creating a swarm for financial transaction fraud detection.

import axios from 'axios';
import * as fs from 'fs';
import * as dotenv from 'dotenv';

// Load environment variables
dotenv.config();

// API Configuration
const API_BASE_URL = "https://api.swarms.world";
const API_KEY = process.env.SWARMS_API_KEY;

// Define interfaces for type safety
interface TransactionData {
  transaction_id: string;
  amount: number;
  timestamp: string;
  merchant: string;
  merchant_category: string;
  payment_method: string;
  location: string;
  device_id?: string;
  ip_address?: string;
}

interface UserProfile {
  user_id: string;
  account_age_days: number;
  typical_transaction_amount: number;
  typical_merchants: string[];
  typical_locations: string[];
  risk_score: number;
}

interface FraudDetectionResult {
  transaction_id: string;
  fraud_score: number;
  is_fraudulent: boolean;
  risk_factors: string[];
  confidence: number;
  recommended_action: string;
  explanation: string;
}

/**
 * Detects potential fraud in financial transactions using a specialized agent swarm
 * 
 * @param transactions - Array of transaction data to analyze
 * @param userProfile - User profile information for context
 * @param historicalPatterns - Description of historical patterns and behaviors
 * @returns Promise resolving to fraud detection results
 */
async function detectTransactionFraud(
  transactions: TransactionData[],
  userProfile: UserProfile,
  historicalPatterns: string
): Promise<FraudDetectionResult[]> {
  try {
    // Prepare the task with all relevant information
    const task = `
    Analyze the following financial transactions for potential fraud.

    USER PROFILE:
    ${JSON.stringify(userProfile, null, 2)}

    HISTORICAL PATTERNS:
    ${historicalPatterns}

    TRANSACTIONS TO ANALYZE:
    ${JSON.stringify(transactions, null, 2)}

    For each transaction, determine the fraud risk score (0-100), whether it's likely fraudulent,
    identified risk factors, confidence level, recommended action (allow, flag for review, block),
    and a detailed explanation of the analysis.

    Return results for each transaction in a structured format.
    `;

    // Define specialized fraud detection agents
    const fraudDetectionAgents = [
      {
        agent_name: "BehavioralAnalyst",
        description: "Analyzes user behavior patterns to identify anomalies",
        system_prompt: `You are an expert in behavioral analysis for fraud detection. 
        Your role is to analyze transaction patterns against historical user behavior
        to identify potential anomalies or deviations that may indicate fraud.

        Consider:
        - Timing patterns (day of week, time of day)
        - Transaction amount patterns
        - Merchant category patterns
        - Geographic location patterns
        - Device usage patterns

        Provide a detailed breakdown of behavioral anomalies with confidence scores.`,
        model_name: "gpt-4o",
        temperature: 0.2,
        role: "analyst",
        max_loops: 1
      },
      {
        agent_name: "TechnicalFraudDetector",
        description: "Analyzes technical indicators of fraud",
        system_prompt: `You are a technical fraud detection specialist.
        Your role is to analyze technical aspects of transactions to identify
        potential indicators of fraud.

        Focus on:
        - IP address analysis
        - Device ID consistency
        - Geolocation feasibility (impossible travel)
        - Known fraud patterns
        - Technical manipulation markers

        Provide a technical assessment with specific indicators of potential fraud.`,
        model_name: "gpt-4o",
        temperature: 0.2,
        role: "analyst",
        max_loops: 1
      },
      {
        agent_name: "FinancialPatternAnalyst",
        description: "Specializes in financial transaction patterns",
        system_prompt: `You are a financial pattern analysis expert specializing in fraud detection.
        Your role is to analyze the financial aspects of transactions for fraud indicators.

        Focus on:
        - Transaction amount anomalies
        - Merchant risk profiles
        - Transaction velocity
        - Transaction sequence patterns
        - Round-number amounts or other suspicious values

        Provide a financial pattern analysis with risk assessment.`,
        model_name: "gpt-4o",
        temperature: 0.2,
        role: "analyst",
        max_loops: 1
      },
      {
        agent_name: "FraudInvestigator",
        description: "Synthesizes all analysis into final fraud determination",
        system_prompt: `You are a senior fraud investigator responsible for making the final
        determination on transaction fraud risk. 

        Your role is to:
        1. Synthesize inputs from behavioral, technical, and financial analyses
        2. Weigh different risk factors appropriately
        3. Calculate an overall fraud risk score (0-100)
        4. Make a clear determination (legitimate, suspicious, fraudulent)
        5. Recommend specific actions (allow, review, block)
        6. Provide a clear, detailed explanation for each determination

        Balance false positives and false negatives appropriately.`,
        model_name: "gpt-4o",
        temperature: 0.3,
        role: "manager",
        max_loops: 1
      }
    ];

    // Create the swarm specification
    const swarmSpec = {
      name: "fraud-detection-swarm",
      description: "Financial transaction fraud detection swarm",
      agents: fraudDetectionAgents,
      max_loops: 2,
      swarm_type: "HiearchicalSwarm",
      task: task,
      return_history: false
    };

    // Execute the swarm
    const response = await axios.post(
      `${API_BASE_URL}/v1/swarm/completions`,
      swarmSpec,
      {
        headers: {
          'x-api-key': API_KEY,
          'Content-Type': 'application/json'
        }
      }
    );

    if (response.status === 200) {
      console.log(`Fraud detection completed. Cost: ${response.data.metadata.billing_info.total_cost}`);
      return response.data.output.fraud_analysis as FraudDetectionResult[];
    } else {
      throw new Error(`API request failed with status: ${response.status}`);
    }

  } catch (error) {
    console.error('Error in fraud detection:', error);
    if (axios.isAxiosError(error) && error.response) {
      console.error('API response:', error.response.data);
    }
    throw error;
  }
}

// Usage example
async function main() {
  // Sample transaction data
  const transactions: TransactionData[] = [
    {
      transaction_id: "T-12345-89012",
      amount: 1299.99,
      timestamp: "2025-03-04T09:23:42Z",
      merchant: "ElectronicsPro",
      merchant_category: "Electronics",
      payment_method: "Credit Card",
      location: "San Francisco, CA",
      device_id: "D-472910",
      ip_address: "192.168.1.127"
    },
    {
      transaction_id: "T-12345-89013",
      amount: 849.50,
      timestamp: "2025-03-04T09:45:18Z",
      merchant: "LuxuryBrands",
      merchant_category: "Fashion",
      payment_method: "Credit Card",
      location: "Miami, FL",
      device_id: "D-891245",
      ip_address: "45.23.189.44"
    },
    {
      transaction_id: "T-12345-89014",
      amount: 24.99,
      timestamp: "2025-03-04T10:12:33Z",
      merchant: "CoffeeDeluxe",
      merchant_category: "Restaurant",
      payment_method: "Mobile Wallet",
      location: "San Francisco, CA",
      device_id: "D-472910",
      ip_address: "192.168.1.127"
    }
  ];

  // Sample user profile
  const userProfile: UserProfile = {
    user_id: "U-78901-23456",
    account_age_days: 487,
    typical_transaction_amount: 150.75,
    typical_merchants: ["Groceries", "Restaurant", "Retail", "Streaming"],
    typical_locations: ["San Francisco, CA", "Oakland, CA"],
    risk_score: 12
  };

  // Sample historical patterns
  const historicalPatterns = `
  User typically makes 15-20 transactions per month
  Average transaction amount: $50-$200
  Largest previous transaction: $750 (furniture)
  No previous transactions over $1000
  Typically shops in San Francisco Bay Area
  No international transactions in past 12 months
  Usually uses mobile wallet for small purchases (<$50)
  Credit card used for larger purchases
  Typical activity times: weekdays 8am-10pm PST
  No previous purchases in Miami, FL
  `;

  try {
    const results = await detectTransactionFraud(
      transactions,
      userProfile,
      historicalPatterns
    );

    console.log("Fraud Detection Results:");
    console.log(JSON.stringify(results, null, 2));

    // Save results to file
    fs.writeFileSync(
      `fraud_detection_results_${new Date().toISOString().replace(/:/g, '-')}.json`,
      JSON.stringify(results, null, 2)
    );

    // Process high-risk transactions
    const highRiskTransactions = results.filter(r => r.fraud_score > 75);
    if (highRiskTransactions.length > 0) {
      console.log(`WARNING: ${highRiskTransactions.length} high-risk transactions detected!`);
      // In production, you would trigger alerts, notifications, etc.
    }

  } catch (error) {
    console.error("Failed to complete fraud detection:", error);
  }
}

main();

Healthcare Report Generation (TypeScript)

This example demonstrates creating a swarm for generating comprehensive healthcare reports from patient data.

import axios from 'axios';
import * as fs from 'fs';
import * as path from 'path';
import * as dotenv from 'dotenv';

// Load environment variables
dotenv.config();

// API Configuration
const API_BASE_URL = "https://api.swarms.world";
const API_KEY = process.env.SWARMS_API_KEY;

// Define interfaces
interface PatientData {
  patient_id: string;
  demographics: {
    age: number;
    gender: string;
    ethnicity?: string;
    weight_kg?: number;
    height_cm?: number;
  };
  vitals: {
    blood_pressure?: string;
    heart_rate?: number;
    respiratory_rate?: number;
    temperature_c?: number;
    oxygen_saturation?: number;
  };
  conditions: string[];
  medications: {
    name: string;
    dosage: string;
    frequency: string;
  }[];
  allergies: string[];
  lab_results: {
    test_name: string;
    value: number | string;
    unit: string;
    reference_range?: string;
    collection_date: string;
  }[];
  imaging_results?: {
    study_type: string;
    body_area: string;
    findings: string;
    date: string;
  }[];
  notes?: string[];
}

interface MedicalReport {
  summary: string;
  assessment: {
    primary_diagnosis: string;
    secondary_diagnoses: string[];
    clinical_impression: string;
  };
  recommendations: string[];
  medication_review: {
    current_medications: {
      medication: string;
      assessment: string;
    }[];
    potential_interactions: string[];
    recommended_changes: string[];
  };
  care_plan: {
    short_term_goals: string[];
    long_term_goals: string[];
    follow_up: string;
  };
  lab_interpretation: {
    flagged_results: {
      test: string;
      value: string;
      interpretation: string;
    }[];
    trends: string[];
  };
  clinical_reasoning: string;
}

/**
 * Generates a comprehensive clinical report for a patient using a specialized medical agent swarm
 * 
 * @param patientData - Structured patient medical data
 * @param clinicalGuidelines - Relevant clinical guidelines to consider
 * @param reportType - Type of report to generate (e.g., 'comprehensive', 'follow-up', 'specialist')
 * @returns Promise resolving to structured medical report
 */
async function generateClinicalReport(
  patientData: PatientData,
  clinicalGuidelines: string,
  reportType: string
): Promise<MedicalReport> {
  try {
    // Prepare detailed task description
    const task = `
    Generate a comprehensive clinical report for the following patient:

    PATIENT DATA:
    ${JSON.stringify(patientData, null, 2)}

    CLINICAL GUIDELINES TO CONSIDER:
    ${clinicalGuidelines}

    REPORT TYPE:
    ${reportType}

    Analyze all aspects of the patient's health status including symptoms, lab results, 
    medications, conditions, and other relevant factors. Provide a detailed clinical assessment, 
    evidence-based recommendations, medication review with potential interactions, 
    comprehensive care plan, and clear follow-up instructions.

    Structure the report to include a concise executive summary, detailed assessment with 
    clinical reasoning, specific actionable recommendations, and a clear care plan.

    Ensure all interpretations and recommendations align with current clinical guidelines 
    and evidence-based medicine.
    `;

    // Use Auto Swarm Builder for this complex medical task
    const swarmSpec = {
      name: "clinical-report-generator",
      description: "Medical report generation and analysis swarm",
      swarm_type: "AutoSwarmBuilder",
      task: task,
      max_loops: 3,
      return_history: false
    };

    // Execute the swarm
    console.log("Generating clinical report...");
    const response = await axios.post(
      `${API_BASE_URL}/v1/swarm/completions`,
      swarmSpec,
      {
        headers: {
          'x-api-key': API_KEY,
          'Content-Type': 'application/json'
        }
      }
    );

    if (response.status === 200) {
      const executionTime = response.data.metadata.execution_time_seconds;
      const cost = response.data.metadata.billing_info.total_cost;
      const numAgents = response.data.metadata.num_agents;

      console.log(`Report generation completed in ${executionTime.toFixed(2)} seconds`);
      console.log(`Used ${numAgents} specialized medical agents`);
      console.log(`Total cost: ${cost.toFixed(4)}`);

      return response.data.output.medical_report as MedicalReport;
    } else {
      throw new Error(`API request failed with status: ${response.status}`);
    }

  } catch (error) {
    console.error('Error generating clinical report:', error);
    if (axios.isAxiosError(error) && error.response) {
      console.error('API response:', error.response.data);
    }
    throw error;
  }
}

/**
 * Schedules regular report generation for a patient
 */
async function scheduleRecurringReports(
  patientId: string,
  reportType: string,
  intervalDays: number
): Promise<void> {
  try {
    // Schedule the next report
    const nextReportDate = new Date();
    nextReportDate.setDate(nextReportDate.getDate() + intervalDays);

    const scheduleSpec = {
      name: `${patientId}-${reportType}-report`,
      description: `Scheduled ${reportType} report for patient ${patientId}`,
      task: `Generate a ${reportType} clinical report for patient ${patientId} following standard protocols. Retrieve the most recent patient data and produce a comprehensive clinical assessment with recommendations.`,
      schedule: {
        scheduled_time: nextReportDate.toISOString(),
        timezone: "UTC"
      }
    };

    const response = await axios.post(
      `${API_BASE_URL}/v1/swarm/schedule`,
      scheduleSpec,
      {
        headers: {
          'x-api-key': API_KEY,
          'Content-Type': 'application/json'
        }
      }
    );

    if (response.status === 200) {
      console.log(`Successfully scheduled next report for ${nextReportDate.toISOString()}`);
      console.log(`Job ID: ${response.data.job_id}`);
    } else {
      throw new Error(`Failed to schedule report: ${response.status}`);
    }

  } catch (error) {
    console.error('Error scheduling report:', error);
    if (axios.isAxiosError(error) && error.response) {
      console.error('API response:', error.response.data);
    }
  }
}

// Usage example
async function main() {
  // Sample patient data
  const patientData: PatientData = {
    patient_id: "P-12345-67890",
    demographics: {
      age: 58,
      gender: "Male",
      ethnicity: "Caucasian",
      weight_kg: 92.5,
      height_cm: 178
    },
    vitals: {
      blood_pressure: "148/92",
      heart_rate: 82,
      respiratory_rate: 16,
      temperature_c: 36.8,
      oxygen_saturation: 96
    },
    conditions: [
      "Type 2 Diabetes Mellitus",
      "Hypertension",
      "Coronary Artery Disease",
      "Hyperlipidemia",
      "Obesity"
    ],
    medications: [
      {
        name: "Metformin",
        dosage: "1000mg",
        frequency: "BID"
      },
      {
        name: "Lisinopril",
        dosage: "20mg",
        frequency: "QD"
      },
      {
        name: "Atorvastatin",
        dosage: "40mg",
        frequency: "QD"
      },
      {
        name: "Aspirin",
        dosage: "81mg",
        frequency: "QD"
      }
    ],
    allergies: ["Penicillin", "Sulfa drugs"],
    lab_results: [
      {
        test_name: "HbA1c",
        value: 8.2,
        unit: "%",
        reference_range: "<7.0",
        collection_date: "2025-02-15"
      },
      {
        test_name: "Fasting Glucose",
        value: 165,
        unit: "mg/dL",
        reference_range: "70-100",
        collection_date: "2025-02-15"
      },
      {
        test_name: "LDL Cholesterol",
        value: 118,
        unit: "mg/dL",
        reference_range: "<100",
        collection_date: "2025-02-15"
      },
      {
        test_name: "HDL Cholesterol",
        value: 38,
        unit: "mg/dL",
        reference_range: ">40",
        collection_date: "2025-02-15"
      },
      {
        test_name: "eGFR",
        value: 68,
        unit: "mL/min/1.73m²",
        reference_range: ">90",
        collection_date: "2025-02-15"
      }
    ],
    imaging_results: [
      {
        study_type: "Cardiac CT Angiography",
        body_area: "Heart and coronary arteries",
        findings: "Moderate calcification in LAD. 50-70% stenosis in proximal LAD. No significant stenosis in other coronary arteries.",
        date: "2025-01-10"
      }
    ],
    notes: [
      "Patient reports increased fatigue over the past month",
      "Complains of occasional chest discomfort with exertion",
      "Currently following low-carb diet but admits to poor adherence",
      "Exercise limited by knee pain"
    ]
  };

  // Clinical guidelines to consider
  const clinicalGuidelines = `
  ADA 2025 Guidelines for Type 2 Diabetes:
  - HbA1c target <7.0% for most adults
  - Consider less stringent targets (e.g., <8.0%) for patients with multiple comorbidities
  - First-line therapy: Metformin + lifestyle modifications
  - For patients with ASCVD: consider GLP-1 RA or SGLT2 inhibitor with proven CV benefit

  ACC/AHA 2024 Hypertension Guidelines:
  - BP target <130/80 mmHg for patients with diabetes and/or CAD
  - First-line: ACE inhibitor or ARB for patients with diabetes

  ACC/AHA 2024 Cholesterol Guidelines:
  - LDL-C target <70 mg/dL for very high-risk ASCVD
  - Consider adding ezetimibe or PCSK9 inhibitor for very high-risk patients not at goal
  `;

  try {
    // Generate the report
    const report = await generateClinicalReport(
      patientData,
      clinicalGuidelines,
      "comprehensive"
    );

    // Save report to file
    const timestamp = new Date().toISOString().replace(/:/g, '-');
    const outputDir = './reports';

    if (!fs.existsSync(outputDir)) {
      fs.mkdirSync(outputDir);
    }

    fs.writeFileSync(
      path.join(outputDir, `clinical_report_${patientData.patient_id}_${timestamp}.json`),
      JSON.stringify(report, null, 2)
    );

    // Display executive summary
    console.log("\nREPORT SUMMARY:");
    console.log(report.summary);

    console.log("\nPRIMARY DIAGNOSIS:");
    console.log(report.assessment.primary_diagnosis);

    console.log("\nKEY RECOMMENDATIONS:");
    report.recommendations.forEach((rec, i) => {
      console.log(`  ${i+1}. ${rec}`);
    });

    // Schedule the next report in 90 days
    await scheduleRecurringReports(patientData.patient_id, "follow-up", 90);

  } catch (error) {
    console.error("Failed to complete report generation:", error);
  }
}

main();

Error Handling

The Swarms API follows standard HTTP status codes for error responses:

Status Code	Meaning	Handling Strategy
400	Bad Request	Validate request parameters before sending
401	Unauthorized	Check API key validity
403	Forbidden	Verify API key permissions
404	Not Found	Check endpoint URL and resource IDs
429	Too Many Requests	Implement exponential backoff retry logic
500	Internal Server Error	Retry with backoff, then contact support

Error responses include a detailed message explaining the issue:

{
  "detail": "Failed to create swarm: Invalid swarm_type specified"
}

Rate Limiting

The API enforces a rate limit of 100 requests per 60-second window. When exceeded, a 429 status code is returned. Implement appropriate retry logic with exponential backoff in production applications.

Billing & Cost Management

The API uses a credit-based billing system with costs calculated based on:

1. Agent Count: Base cost per agent
2. Input Tokens: Cost based on the size of input data and prompts
3. Output Tokens: Cost based on the length of generated responses
4. Time of Day: Reduced rates during nighttime hours (8 PM to 6 AM PT)

Cost information is included in each response's metadata for transparency and forecasting.

Best Practices

1. Task Description
2. Provide detailed, specific task descriptions
3. Include all necessary context and constraints

4. Structure complex inputs for easier processing

5. Agent Configuration

6. For simple tasks, use AutoSwarmBuilder to automatically generate optimal agents
7. For complex or specialized tasks, manually define agents with specific expertise

8. Use appropriate swarm_type for your workflow pattern

9. Production Implementation

10. Implement robust error handling and retries
11. Log API responses for debugging and auditing
12. Monitor costs closely during development and testing

13. Use scheduled jobs for recurring tasks instead of continuous polling

14. Cost Optimization

15. Batch related tasks when possible
16. Schedule non-urgent tasks during discount hours
17. Carefully scope task descriptions to reduce token usage
18. Cache results when appropriate

Support

For technical assistance with the Swarms API, please contact:

• Documentation: https://docs.swarms.world
• Email: kye@swarms.world
• Community Discord: https://discord.gg/swarms
• Swarms Marketplace: https://swarms.world
• Swarms AI Website: https://swarms.ai