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AgentChat is AutoGen’s high-level API for building multi-agent applications. It provides intuitive defaults and abstractions that make it easy to get started while still offering flexibility for advanced use cases.

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What is AgentChat?

AgentChat builds on top of the Core API to provide:
  • Pre-built agent types - AssistantAgent, CodeExecutorAgent, UserProxyAgent, and more
  • Team patterns - RoundRobin, Selector, Swarm, and GraphFlow orchestration
  • Simplified tool integration - Easy-to-use tool and MCP server support
  • Model flexibility - Support for all major LLM providers
  • Memory management - Built-in state and memory handling

Agents

Learn about AssistantAgent, CodeExecutorAgent, and custom agents

Teams

Explore multi-agent orchestration patterns

Tools

Integrate tools and MCP servers with your agents

Models

Connect to OpenAI, Anthropic, Azure, and more

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When to use AgentChat

Use AgentChat when you want to:
  • Rapid prototyping - Get a working multi-agent system in minutes
  • Common patterns - Use established orchestration patterns (round-robin, selector, swarm)
  • Simplified development - Focus on your application logic, not infrastructure
  • Tool integration - Easily connect agents to external tools and APIs
For advanced use cases requiring full control over message routing and agent lifecycle, consider using the Core API directly.

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Quick example

import asyncio
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

async def main() -> None:
    # Create model client
    model_client = OpenAIChatCompletionClient(model="gpt-4o")
    
    # Create assistant agent
    agent = AssistantAgent(
        "assistant",
        model_client=model_client,
        system_message="You are a helpful assistant."
    )
    
    # Run a task
    result = await agent.run(task="What is 2+2?")
    print(result.messages)

asyncio.run(main())

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Core concepts

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Agents

Agents are the building blocks of your application. Each agent has:
  • A name and description
  • A model client for LLM interactions
  • Optional tools for external capabilities
  • A system message defining its behavior

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Teams

Teams orchestrate multiple agents working together:
  • RoundRobinGroupChat - Agents take turns in sequence
  • SelectorGroupChat - An LLM selects the next speaker
  • Swarm - Agents hand off tasks dynamically
  • GraphFlow - Custom graph-based orchestration

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Messages

Agents communicate through typed messages:
  • TextMessage - Plain text communication
  • ToolCallSummaryMessage - Results from tool execution
  • HandoffMessage - Task transfers between agents
  • StructuredMessage - Typed structured data

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Tools

Tools extend agent capabilities:
  • Function tools - Python functions as tools
  • MCP servers - Model Context Protocol servers
  • Code execution - Safe code execution in containers
  • Custom tools - Build your own tool implementations

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Architecture

AgentChat sits on top of the Core layer: 
┌─────────────────────────────────────┐
│         Your Application            │
├─────────────────────────────────────┤
│  AgentChat (High-level API)        │
│  - Agents, Teams, Tools             │
├─────────────────────────────────────┤
│  Core (Event-driven runtime)       │
│  - Actor model, Message passing     │
├─────────────────────────────────────┤
│  Extensions (Model clients, etc)   │
└─────────────────────────────────────┘

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Next steps

Quickstart

Build your first agent in 5 minutes

Agents

Explore different agent types

Teams

Learn about multi-agent patterns

Examples

See AgentChat in action