Skip to main content
Multi-agent systems enable specialized agents to work together on complex tasks. Each agent focuses on a specific domain and coordinates through orchestration patterns.

​
Multi-Agent System Design Patterns

There are many ways to design multi‑agent systems, but we commonly see two broadly applicable patterns:
  • Manager (agents as tools): A central manager/orchestrator invokes specialized sub‑agents as tools and retains control of the conversation.
  • Handoffs: Peer agents hand off control to a specialized agent that takes over the conversation. This is decentralized.
This guide focuses on the Manager pattern (agents as tools), which is the most common and straightforward approach.

​
Installation

pip install langgraph langchain-openai

​
Manager Pattern (Agents as Tools)

In the Manager pattern, a central orchestrator invokes specialized agents as tools. The orchestrator retains control throughout the conversation and decides when to call each specialized agent.

​
Creating Specialized Agents

Each specialized agent focuses on a specific task: TODO: Framework-specific example coming soon.

​
Creating the Orchestrator

The orchestrator coordinates the workflow by delegating to specialized agents. It wraps specialized agents as tools: TODO: Framework-specific example coming soon.

​
Best Practices

  1. Clear Boundaries: Each agent should have a specific, focused responsibility
  2. Efficient Communication: Minimize overhead between agents using appropriate patterns
  3. Structured State: Use typed state classes when agents need to share data
TODO: Framework-specific best practices coming soon.

​
Next Steps