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name: autogen description: >- Build conversational multi-agent systems with Microsoft AutoGen. AssistantAgent, UserProxyAgent, GroupChat, code execution, nested chats, cancellation tokens, tool integration, and MCP support. Use when building conversation-driven multi-agent systems or comparing agent frameworks. Do not use this skill for unrelated requests; route to the nearest named specialist. license: MIT metadata: author: Magnus Hedemark version: 1.2.0 source: https://microsoft.github.io/autogen

AutoGen Expert Skill

This skill targets the AutoGen AgentChat 0.7.5 API on Python 3.10+. AutoGen (by Microsoft Research) is a framework for conversational multi-agent AI. Unlike LangGraph's explicit graph topology or CrewAI's role-based crews, AutoGen uses agent-to-agent conversations as the orchestration primitive. Agents communicate through structured chat, with built-in patterns for group chat routing, human input, and code execution.

Core Paradigm

from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.ui import Console
from autogen_ext.models.openai import OpenAIChatCompletionClient

model_client = OpenAIChatCompletionClient(model="gpt-4o-mini")

assistant = AssistantAgent(
    name="assistant",
    system_message="You are a helpful assistant.",
    model_client=model_client,
)

Role split in AgentChat 0.7.5: UserProxyAgent represents a human and obtains replies through input_func. CodeExecutorAgent executes code blocks using a CodeExecutor. They are separate agents; neither accepts the legacy human_input_mode option.

Core Principles

1. Conversations are the orchestration primitive. Agents send messages, receive replies, and the conversation structure determines the workflow.
2. Keep human input and code execution separate. Use UserProxyAgent for a human participant; use CodeExecutorAgent with a CodeExecutor for code.
3. GroupChat routes between agents. RoundRobinGroupChat cycles fixed-order. SelectorGroupChat uses an LLM to pick the next speaker.
4. Nested chats delegate work. An agent can spawn a sub-conversation between specialist agents and return the result.
5. Docker is the safe code execution mode. Local code execution (LocalCommandLineCodeExecutor) runs LLM-generated code on your machine — use Docker in production.
6. Cancellation tokens stop runaway agents. Always pass CancellationToken for long-running tasks.

Where to Start

You already have...	Start here
Nothing — exploring AutoGen	Create an assistant chat or a team with a human UserProxyAgent
Agents that need to coordinate	Build a GroupChat with multiple agents
Agents that need code execution	Configure Docker code executor
A complex multi-step task	Use nested chats for sub-tasks

Quick Reference

Task	Approach	Reference
Human-in-the-loop chat	AssistantAgent + UserProxyAgent	references/agent-types.md
Multi-agent group	GroupChat with RoundRobinGroupChat	references/group-chat.md
Code execution	DockerCommandLineCodeExecutor	references/code-execution.md
Tool integration	register_function() or @tool	references/tool-integration.md
Nested chat	AgentTool or a team run from a tool	references/conversation-patterns.md
Cancellation	CancellationToken	references/conversation-patterns.md
MCP tools	McpWorkbench	references/tool-integration.md

Framework Routing Guide

Scenario	Reach for	Why
Conversation-driven multi-agent	AutoGen	Native agent-to-agent chat as orchestration
Role-based multi-agent teams	CrewAI	Role/Goal/Backstory is the native abstraction
State-machine multi-agent	LangGraph	Graph topology, subgraphs, human-in-the-loop
Chain/agent composition	LangChain	LCEL pipe operator for general chains

Reference Files

Reference	Load when	File
Agent Types	AssistantAgent, UserProxyAgent	references/agent-types.md
Conversation Patterns	Send/receive, nested chats, cancellation	references/conversation-patterns.md
Group Chat	RoundRobin, Selector, MagenticOne	references/group-chat.md
Code Execution	Docker, local, cancellation tokens	references/code-execution.md
Tool Integration	register_function, @tool, MCP integration	references/tool-integration.md
v0.4 Migration	v0.2->v0.4 migration, AgentTool, streaming, termination	references/v04-migration.md
Validation Audit	Research validation of all API claims	references/validation-audit.md
FAQ & Troubleshooting	Common errors and fixes	references/faq-and-troubleshooting.md

Install the exact package versions used by the templates with python -m pip install -r requirements.txt from this skill directory. The code-execution example uses Docker and requires a working Docker daemon.

Templates

Template	When to use	File
Two-Agent Chat	Assistant + human input	templates/two-agent-chat.py
Group Chat	Multi-agent team with speaker routing	templates/group-chat.py
Code Execution Agent	Assistant + Docker-backed code executor	templates/code-execution.py

Troubleshooting

Symptom	Likely cause	Fix	Reference
Agent loops forever	No team termination condition	Add a TerminationCondition such as MaxMessageTermination	references/conversation-patterns.md
Code execution fails	Docker not running	Start Docker or use LocalCommandLineCodeExecutor	references/code-execution.md
Nested chat never returns	Cancellation token not passed	Pass CancellationToken with timeout	references/conversation-patterns.md
v0.2 code doesn't work	v0.4 API changed	Follow migration guide	references/faq-and-troubleshooting.md
GroupChat speaker selection loops	SelectorGroupChat with no clear next	Use RoundRobinGroupChat for fixed order	references/group-chat.md
UserProxyAgent waits for input	It is a human participant and its input_func is waiting	Supply an appropriate input function or use an automated agent	references/agent-types.md

When NOT to Use AutoGen

• Simple single-agent task — overkill, use direct API call
• Need fine-grained graph control — use LangGraph
• Need role-based teams with fixed processes — use CrewAI
• Need chain composition — use LangChain LCEL