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Documentation Index

Fetch the complete documentation index at: https://docs.swarms.ai/llms.txt

Use this file to discover all available pages before exploring further.

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What This Example Shows

  • Creating a concurrent workflow swarm for parallel analysis
  • Implementing multiple specialized medical analysis agents
  • Coordinating agents to work simultaneously for faster results
  • Comprehensive medical code interpretation and analysis

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Installation

pip3 install -U swarms-client

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Get Your Swarms API Key

  1. Visit https://swarms.world/platform/api-keys
  2. Create an account or sign in
  3. Generate a new API key
  4. Store it securely in your environment variables

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Code

import json
import os
from swarms_client import SwarmsClient
from dotenv import load_dotenv

# Load environment variables
load_dotenv()

# Initialize the client
client = SwarmsClient(
    api_key=os.getenv("SWARMS_API_KEY"),
)

# Example patient symptoms for analysis
patient_symptoms = """
Patient: 45-year-old female
Chief Complaint: Chest pain and shortness of breath for 2 days

Symptoms:
- Sharp chest pain that worsens with deep breathing
- Shortness of breath, especially when lying down
- Mild fever (100.2°F)
- Dry cough
- Fatigue
"""

# Create and run the ICD analysis swarm
out = client.swarms.run(
    name="ICD Analysis Swarm",
    description="A swarm that analyzes ICD codes",
    swarm_type="ConcurrentWorkflow",
    task=patient_symptoms,
    agents=[
        {
            "agent_name": "ICD-Analyzer",
            "description": "An agent that analyzes ICD codes",
            "system_prompt": "You are an expert ICD code analyzer. Your task is to analyze the ICD codes and provide a detailed explanation of the codes.",
            "model_name": "groq/openai/gpt-oss-120b",
            "role": "worker",
            "max_loops": 1,
            "max_tokens": 8192,
            "temperature": 0.5,
        },
        {
            "agent_name": "ICD-Code-Explainer-Primary",
            "description": "An agent that provides primary explanations for ICD codes",
            "system_prompt": "You are an expert ICD code explainer. Your task is to provide a clear and thorough explanation of the ICD codes to the user, focusing on primary meanings and clinical context.",
            "model_name": "groq/openai/gpt-oss-120b",
            "role": "worker",
            "max_loops": 1,
            "max_tokens": 8192,
            "temperature": 0.5,
        },
        {
            "agent_name": "ICD-Code-Explainer-Secondary",
            "description": "An agent that provides additional context and secondary explanations for ICD codes",
            "system_prompt": "You are an expert ICD code explainer. Your task is to provide additional context, nuances, and secondary explanations for the ICD codes, including possible differential diagnoses and related codes.",
            "model_name": "groq/openai/gpt-oss-120b",
            "role": "worker",
            "max_loops": 1,
            "max_tokens": 8192,
            "temperature": 0.5,
        },
    ],
)

print(json.dumps(out, indent=4))

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Swarm Architecture Explained

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Concurrent Workflow

This swarm type processes all agents simultaneously:
  • ICD-Analyzer: Analyzes and identifies relevant ICD codes
  • ICD-Code-Explainer-Primary: Provides primary code explanations
  • ICD-Code-Explainer-Secondary: Offers additional context and nuances

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Benefits of Concurrent Processing

  • Speed: All agents work simultaneously for faster results
  • Efficiency: No waiting for sequential completion
  • Comprehensive Coverage: Multiple perspectives delivered together
  • Scalability: Easy to add more parallel agents

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Expected Output

The concurrent swarm will provide:
  • ICD Code Analysis: Relevant medical codes for the symptoms
  • Primary Explanations: Clear, clinical context for each code
  • Secondary Context: Additional insights, differential diagnoses, and related codes
  • Comprehensive Coverage: Multiple perspectives on the same medical case

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Use Cases

This pattern is ideal for:
  • Medical Diagnosis: Symptom analysis and code identification
  • Clinical Documentation: Medical record coding and validation
  • Medical Education: Teaching ICD code interpretation
  • Healthcare Billing: Accurate medical code assignment
  • Clinical Research: Medical condition classification and analysis

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Environment Setup

Create a .env file in your project directory: 
SWARMS_API_KEY=your_api_key_here

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Customization Ideas

Adapt this pattern for:
  • Radiology Analysis: Multiple imaging specialists working in parallel
  • Laboratory Results: Multiple lab technicians analyzing different tests
  • Pharmaceutical Review: Multiple pharmacists reviewing medication interactions
  • Surgical Planning: Multiple specialists planning surgical procedures
  • Emergency Response: Multiple emergency responders coordinating care

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Advanced Concurrent Workflows

You can extend this pattern to:
  • Dynamic Agent Allocation: Automatically assign agents based on workload
  • Load Balancing: Distribute tasks evenly across available agents
  • Result Aggregation: Combine parallel results into unified insights
  • Quality Assurance: Multiple agents validating each other’s work

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Next Steps

After mastering concurrent workflows, explore:
  • Sequential workflows for dependent tasks
  • Hierarchical swarms for team coordination
  • Majority voting for consensus-based decisions
  • Agent routing for intelligent task distribution
  • Mixture of agents for specialized expertise combinations