magnus919_agent-skills/autogen/SKILL.md

name, description, license, metadata

name	description	license	metadata
autogen	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.	MIT	author version source Magnus Hedemark 1.1.0 https://microsoft.github.io/autogen

AutoGen Expert Skill

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 nested conversations, group chat with routing, 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,
)

⚠️ UserProxyAgent is NOT a human user. It is an automated proxy that can execute code. Despite the name, it runs autonomously unless human_input_mode is set to ALWAYS.

Core Principles

1. Conversations are the orchestration primitive. Agents send messages, receive replies, and the conversation structure determines the workflow.
2. UserProxyAgent is a code executor, not a human. Despite the name, it runs autonomously by default. Set human_input_mode="ALWAYS" for actual human-in-the-loop.
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 a two-agent chat (Assistant + UserProxy)
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
Two-agent 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	initiate_chat() from within 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

Templates

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

Troubleshooting

Symptom	Likely cause	Fix	Reference
Agent loops forever	No termination condition	Add is_termination_msg or max_turns	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 asking for input	human_input_mode="ALWAYS"	Set to "NEVER" for automated execution	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