Everyone compares the big three. Fine. But the comparison gets honest when you add a fourth, because a fourth forces you to stop repeating consensus and start evaluating. Here is the four-way split, no trophies.
LangGraph is the control pick: graph-based workflows, explicit state, Python-first. Best when you need to see and steer exactly what the agent does. Worst when you want to move fast and could not care less about the internals. CrewAI is the team pick: role-based agents that collaborate, an API designed for humans instead of graph theorists. Best for getting a multi-agent demo running this week. Worst when you need exotic control flow. I have recommended both, in different rooms, for different reasons.
AutoGen is the veteran pick: conversational agent patterns, deep history, plenty of prior art to learn from. Best when your problem genuinely maps to agents talking. Worst when you want the newest abstractions. And then OpenAgents, the wild card almost nobody includes. It deserves the same evaluation as the big three. Fame is not a technical argument. Run it through the same filter and let the data speak. Consensus is lazy, and lazy picks cost months.
That filter: recent maintenance, real contributors, a license you can live with, a model layer that is not welded to one provider. Apply it evenly. The data usually settles the debate faster than any opinion piece, including this one. Our AI agent framework comparison database search scores all of these on GitHub activity, license, and capabilities across 548 tracked frameworks, so the whole LangGraph vs CrewAI vs AutoGen vs OpenAgents verdict takes minutes at localaiagents.fyi.
And if even that feels like homework: framework selection consulting SaaS exists for exactly this. A few hours with someone who has shipped agents beats a month of README archaeology, and I say that as someone who has done the archaeology. Or skip both and have the agent built: I build custom AI agents scoped to your actual workflow at localaiagents.fyi.