OpenAI Swarm Tutorial: Lightweight Multi-Agent Orchestration

June 1, 2026 · View on GitHub

Stars License: MIT Python

🐝 Build Agent Teams That Work Together Seamlessly

Why This Track Matters

OpenAI Swarm is an experimental, educational framework — it is not intended for production use. Its value is in teaching multi-agent concepts: handoffs, context variables, and agent composition patterns. This track covers getting started with Swarm, agent design, function calling, and multi-agent orchestration to help you understand how lightweight agent coordination works before building with more robust solutions.

This track focuses on:

  • Design Specialized Agents with distinct roles and capabilities
  • Implement Smooth Handoffs between agents based on context
  • Build Complex Workflows with multi-agent collaboration
  • Manage Shared State using context variables

🎯 What is Swarm?

SwarmView Repo is an educational framework from OpenAI designed for lightweight multi-agent orchestration. It provides a simple, ergonomic way to coordinate multiple AI agents, enabling seamless handoffs and collaborative problem-solving.

Key Concepts

ConceptDescription
AgentsAutonomous units with instructions, tools, and behaviors
RoutinesPredefined sequences of actions an agent can perform
HandoffsSeamless transfer of control between agents
Context VariablesShared state passed between agents during handoffs
Function CallingTool integration for agent capabilities

Mental Model

flowchart LR
    A[User Request] --> B[Triage Agent]
    B -->|Technical Issue| C[Support Agent]
    B -->|Sales Query| D[Sales Agent]
    B -->|General Info| E[Info Agent]
    
    C -->|Escalation| F[Expert Agent]
    D -->|Complex Deal| G[Manager Agent]
    
    C --> H[Resolution]
    D --> H
    E --> H
    F --> H
    G --> H
    
    classDef entry fill:#e1f5fe,stroke:#01579b
    classDef agents fill:#f3e5f5,stroke:#4a148c
    classDef escalation fill:#fff3e0,stroke:#ef6c00
    classDef output fill:#e8f5e8,stroke:#1b5e20
    
    class A entry
    class B,C,D,E agents
    class F,G escalation
    class H output

Current Snapshot (auto-updated)

Chapter Guide

  1. Chapter 1: Getting Started - Installation, setup, and your first Swarm agent
  2. Chapter 2: Agent Design - Creating agents with instructions and personalities
  3. Chapter 3: Function Calling & Tools - Equipping agents with capabilities
  4. Chapter 4: Routines - Building predefined action sequences
  5. Chapter 5: Agent Handoffs - Seamless control transfer between agents
  6. Chapter 6: Context Variables - Managing shared state across agents
  7. Chapter 7: Multi-Agent Patterns - Complex orchestration strategies
  8. Chapter 8: Production Considerations - Scaling, monitoring, and best practices

What You Will Learn

  • Design Specialized Agents with distinct roles and capabilities
  • Implement Smooth Handoffs between agents based on context
  • Build Complex Workflows with multi-agent collaboration
  • Manage Shared State using context variables
  • Integrate External Tools through function calling
  • Create Resilient Systems with proper error handling
  • Scale Agent Systems for production use cases

Prerequisites

  • Python 3.10+
  • OpenAI API key
  • Basic understanding of LLMs and prompting
  • Familiarity with async Python (helpful)

Quick Start

# Install Swarm
pip install git+https://github.com/openai/swarm.git

# Set your OpenAI API key
export OPENAI_API_KEY="your-api-key"

from swarm import Swarm, Agent

client = Swarm()

# Define a simple agent
agent = Agent(
    name="Helper",
    instructions="You are a helpful assistant. Be concise and friendly."
)

# Run the agent
response = client.run(
    agent=agent,
    messages=[{"role": "user", "content": "Hello!"}]
)

print(response.messages[-1]["content"])

Example: Customer Service System

from swarm import Swarm, Agent

client = Swarm()

# Transfer functions
def transfer_to_sales():
    """Transfer to sales agent for pricing and purchases."""
    return sales_agent

def transfer_to_support():
    """Transfer to support agent for technical issues."""
    return support_agent

# Define agents
triage_agent = Agent(
    name="Triage",
    instructions="""You are a customer service triage agent.
    Determine the nature of the customer's request and transfer to the appropriate agent:
    - Sales questions → transfer_to_sales()
    - Technical issues → transfer_to_support()
    """,
    functions=[transfer_to_sales, transfer_to_support]
)

sales_agent = Agent(
    name="Sales",
    instructions="You are a sales agent. Help with pricing, plans, and purchases."
)

support_agent = Agent(
    name="Support", 
    instructions="You are a technical support agent. Help troubleshoot issues."
)

# Run the system
response = client.run(
    agent=triage_agent,
    messages=[{"role": "user", "content": "I need help setting up my account"}]
)

print(f"Final agent: {response.agent.name}")
print(f"Response: {response.messages[-1]['content']}")

Learning Path

🟢 Beginner Track

  1. Chapters 1-3: Setup, basic agents, and function calling
  2. Focus on single-agent workflows

🟡 Intermediate Track

  1. Chapters 4-6: Routines, handoffs, and context management
  2. Build multi-agent systems

🔴 Advanced Track

  1. Chapters 7-8: Complex patterns and production deployment
  2. Master enterprise-grade agent orchestration

Ready to build multi-agent systems? Let's begin with Chapter 1: Getting Started!

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Full Chapter Map

  1. Chapter 1: Getting Started with OpenAI Swarm
  2. Chapter 2: Agent Design
  3. Chapter 3: Function Calling & Tools
  4. Chapter 4: Routines
  5. Chapter 5: Agent Handoffs
  6. Chapter 6: Context Variables
  7. Chapter 7: Multi-Agent Patterns
  8. Chapter 8: Production Considerations

Source References

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