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AutoGen

Microsoft's layered Python and .NET framework for multi-agent LLM apps, now in maintenance mode.

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About

Microsoft Research's agent framework lets you compose LLM-backed agents that message each other, call tools, execute code and pull in a human when needed. Since the 0.4 rewrite it is layered: autogen-core is an event-driven, actor-style runtime that runs locally or across processes, in Python and .NET; autogen-agentchat adds ready-made assistant agents, group chats and teams; and autogen-ext supplies model clients for OpenAI, Azure OpenAI and other providers, MCP workbenches and code executors. AutoGen Studio offers a browser UI for assembling and testing teams without code, AgBench handles agent benchmarking, and Magentic-One is a reference team for web browsing, coding and file tasks. Install with pip on Python 3.10 or newer; no GPU is needed, since the model endpoint you configure does the heavy lifting. Code is MIT and documentation CC BY 4.0. Microsoft moved AutoGen into maintenance mode in October 2025 and points new projects to Microsoft Agent Framework, so only bug fixes and security patches land and support is community run. It still suits existing AutoGen systems and studying multi-agent patterns.

Should you use AutoGen?

Pick it when

Use AutoGen if you already maintain a 0.4-era multi-agent codebase or run conversational multi-agent experiments for research, where its actor-style core and AutoGen Studio prototyping are known quantities.

Look elsewhere when

AutoGen is in maintenance mode: no new features, community managed, and Microsoft points new users to Microsoft Agent Framework. New projects should start there, or with CrewAI or LangGraph to stay vendor-neutral.

Alternatives to AutoGen

  • Microsoft Agent Framework

    Official successor merging AutoGen and Semantic Kernel, adding graph workflows and .NET support, with migration guides from AutoGen.

  • CrewAI

    Simpler role-and-task model with sequential or hierarchical crews; less flexible agent-to-agent messaging but faster to get running.

  • LangGraph

    Explicit graph with persistence and checkpoints instead of free-form agent conversation; more predictable control flow, more code.

  • CAMEL

    Research-oriented role-playing framework that scales to very large agent simulations; better for studies, weaker production story.

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