AutoGen
Build LLM-based multi-agent systems with patterns like GroupChat. Microsoft's original AutoGen repo is now in maintenance mode, merged into the new Microsoft Agent Framework, with the AG2 community fork continuing active development.
The verdict on AutoGen: Developers building multi-agent systems that need iterative back-and-forth reasoning between agents AutoGen's real selling point, beyond the multi-agent patterns, is the human proxy: the ability to build supervised workflows where a human stays in the loop without writing custom interrupt logic from scratch. Pricing: Free and open source (in maintenance mode, see AG2 fork). Last reviewed: August 2026.
Best For
Developers building multi-agent systems that need iterative back-and-forth reasoning between agents
Standout Feature
The human proxy pattern makes it straightforward to build supervised, not fully autonomous, multi-agent workflows
TL;DR
Strong for complex research and coding tasks, now in maintenance mode, check whether the AG2 fork better fits new projects.
Alternatives
Overview
AutoGen is Microsoft Research's open-source framework for building multi-agent AI applications where agents can converse with each other, use tools, execute code, and collaborate to solve problems that require multiple rounds of reasoning. The ConversableAgent pattern lets you define human proxies, coding agents, and assistant agents that hand tasks back and forth until a problem is solved, or escalate to a human when they're stuck. Used by researchers and developers building complex AI pipelines, automated research workflows, and coding agents that write, test, and iterate on code autonomously.
Our Take
That makes it genuinely useful for research and coding tasks that need iterative back-and-forth between agents. The maintenance mode status is the honest caveat here: the original Microsoft repo has merged into the new Microsoft Agent Framework, and the AG2 community fork is where active development continues. For new projects, check whether AG2 or a more actively maintained framework better fits your roadmap before committing.
Key Features
- ConversableAgent pattern
- Human-in-the-loop support
- Code execution sandbox
- Group chat between agents
- Tool use and function calling
- Flexible model backend
- • Best for complex research and coding tasks that need iterative agent collaboration
- • Human proxy pattern makes it easy to build supervised autonomy workflows
- • Microsoft backing means strong long-term development
- • Higher complexity than simpler agent frameworks for basic tasks
- • Python-only with a steeper learning curve than visual tools
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