Magnus Hedemark
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Each README is written for a human audience, explaining: - What the skill does (not what format it follows) - What benefit the user gets from installing it - Quick setup and usage patterns - When to load/trigger the skill - What scripts, references, and templates it ships data-scientist already had a README — left unchanged. 48 READMEs added across all skill and bundle directories.
1.6 KiB
AutoGen — Conversational Multi-Agent AI (Microsoft Research)
An expert-level skill for building conversational multi-agent systems with Microsoft's AutoGen framework. Unlike graph-based or role-based orchestration, AutoGen uses agent-to-agent conversations as the orchestration primitive.
Why Install This Skill
When your agent loads this skill, it becomes an AutoGen expert who can:
- Design agent topologies — AssistantAgent, UserProxyAgent, GroupChat configurations
- Build group chat systems — RoundRobinGroupChat and SelectorGroupChat patterns
- Implement nested chats — agent-to-agent delegation for sub-tasks
- Configure code execution — Docker-safe code execution for LLM-generated code
- Handle production concerns — cancellation tokens, termination conditions, error recovery
What You Get
| Directory | Purpose |
|---|---|
SKILL.md |
Quick-start guide, core paradigm explanation, and pattern selection |
references/ |
Deep dives into agent types, group chat, nested chats, code execution, tool integration, and MCP support |
Triggers
Load this skill when working with AutoGen, building multi-agent chat systems, or comparing agent frameworks. Use when you need conversation-driven agent orchestration.
Framework Comparison
AutoGen differs from other frameworks in the portfolio: it's conversation-driven (vs LangGraph's graph topology), uses autonomous agent-to-agent messaging (vs CrewAI's explicit role-based crews), and has built-in group chat routing (vs PydanticAI's direct delegation).
Requirements
Python 3.8+ with autogen-agentchat and autogen-ext packages.