AutoGen

Open-source Python framework for building multi-agent LLM applications that collaborate to solve tasks.

4.5 (4)

审阅者 Daniel Nikulshyn·更新 2026年5月

概览

AutoGen is an open-source programming framework developed by Microsoft Research for creating agentic AI systems. It lets developers define multiple LLM-powered agents that can converse, reason, call tools, execute code, and coordinate with each other to complete complex workflows. The framework supports flexible conversation patterns, customizable agent roles, and integration with human input, external APIs, and code execution environments. It is commonly used to prototype research assistants, automated coding workflows, data analysis pipelines, and other task-oriented agent systems. AutoGen is distributed as a Python library with active community development, documentation, and examples, making it a practical foundation for experimenting with and deploying multi-agent applications.

主要功能

Multi-agent conversation orchestration
Customizable agent roles and personas
Code execution and tool calling
Human-in-the-loop support
Compatible with major LLM providers
Extensible Python API

优点 & 缺点

优点

Free and open source
Flexible multi-agent conversation patterns
Supports tool use and code execution
Backed by Microsoft Research with active community

缺点

Requires Python and LLM API knowledge
Documentation can lag behind rapid updates
Running multi-agent loops may incur high token costs
No built-in GUI for non-developers

评测

4.5

4 个评分的平均值。

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Gunnar Eriksson

Skeptical, then convinced

I went in skeptical — most tools in this space overpromise. It actually delivers on code execution and tool calling, and free and open source caught me off guard. Requires Python and LLM API knowledge is why this isn't a perfect score, still, I'd recommend giving it a real trial.

Omar Haddad

Solid for our team

We rolled this out across the team last quarter and flexible multi-agent conversation patterns. Compatible with major LLM providers fits neatly into how we already work, and multi-agent conversation orchestration removed a step we used to do by hand. but it has held up under daily use.

Jamal Carter

Compared a few options

Evaluated this against two competitors. Where it wins: human-in-the-loop support and backed by Microsoft Research with active community. On balance the feature set — especially compatible with major LLM providers — justifies the 5 stars for our use case.

Linda Petersen

Skeptical, then convinced

I went in skeptical — most tools in this space overpromise. It actually delivers on compatible with major LLM providers, and flexible multi-agent conversation patterns caught me off guard. No built-in GUI for non-developers is why this isn't a perfect score, still, I'd recommend giving it a real trial.