Swarm (OpenAI)

Why It Matters

Complex tasks often require multiple specialized agents working together. A customer service system might need agents for billing, technical support, and sales, each with different tools and knowledge. Swarm provides a minimal framework for coordinating these handoffs.

The key insight: multi-agent systems don’t need complex infrastructure. Swarm demonstrates that agent coordination can be as simple as function calls and handoffs. One agent can transfer control to another by returning a reference to it, making the flow explicit and debuggable.

For AI engineers, Swarm serves as a reference implementation for multi-agent patterns. Even if you don’t use Swarm directly (OpenAI explicitly marks it as experimental, not for production), studying its design teaches transferable concepts for building your own agent systems.

How It Works

Swarm orchestrates agents through two key concepts:

1. Agents Each agent is defined with instructions and tools. Instructions are system prompts that establish the agent’s role and behavior. Tools are functions the agent can call.

2. Handoffs An agent can transfer control to another agent by returning a handoff function. When Agent A’s tool returns Agent B, the system continues execution with Agent B.

3. Routines Sequences of steps encoded in natural language instructions. Instead of hard-coding flows, agents follow instructions that describe what to do, including when to hand off.

4. Context Variables Shared state that persists across agent transitions. Variables can be read and updated by any agent in the conversation.

Implementation Basics

Working with Swarm patterns:

Agent Definition Define agents with clear, focused roles. Each agent should have a specific purpose and the tools needed to fulfill it. Avoid agents that try to do everything.

Handoff Design Map out when agents should transfer control. Common patterns: triage agent routes to specialists, specialists escalate to humans, parallel agents collaborate on subtasks.

State Management Use context variables for information that needs to persist across handoffs, user ID, conversation history, intermediate results.

Testing Flows Multi-agent systems are harder to test than single agents. Test individual agents first, then integration test common handoff paths.

Error Handling Plan for failed handoffs and agent errors. Implement fallback agents or escalation to human operators for edge cases.

Production Considerations Swarm is experimental. For production multi-agent systems, consider more mature options like LangGraph, CrewAI, or building custom orchestration. Use Swarm to prototype and validate agent designs.

The patterns Swarm demonstrates (specialized agents, explicit handoffs, shared context) apply regardless of which framework you ultimately choose for production.