CrewAI vs AutoGen
Both build teams of agents, but one thinks in roles and tasks while the other thinks in conversations. Which fits your project?
CrewAI and AutoGen are both multi-agent frameworks, but they picture collaboration differently. CrewAI organizes agents by role and task. AutoGen, from Microsoft, organizes them as a conversation where agents message each other to reach an answer.
The mental model
In CrewAI you assign roles: this agent is the researcher, that one is the writer. You define tasks and the crew works through them. In AutoGen you set up agents that talk, and the work emerges from their back-and-forth. AutoGen's conversation model is powerful for open-ended, exploratory problems; CrewAI's role model is clearer for structured workflows with a defined output.
Maturity and stability
AutoGen reached 1.0 GA in 2026 with a redesigned event-driven core. That is a strong, stable foundation, but the rewrite means many older tutorials no longer match the current API, so check the date on anything you follow. CrewAI has stayed more API-stable and has heavy investment in docs and templates for common business workflows.
Language support
CrewAI is Python-only. AutoGen supports Python and .NET from one team, which matters if part of your stack lives in C#. If you are a pure Python shop, both are fine; if you need .NET, AutoGen pulls ahead.
Best fit
Pick AutoGen for research-style problems, agent dialogues, and .NET environments. Pick CrewAI when you have a structured workflow with clear roles and want the fastest path to a working crew with good documentation. Both handle human-in-the-loop and tool use; the deciding factor is usually whether your problem feels like a conversation or a set of assigned jobs.