The shortlist
Multiple specialized agents beating one generalist is the promise; coordination is the hard part. These frameworks use different workflow abstractions; none is a measured overall winner here.
These are documentation and catalogue-based suggestions, not hands-on benchmarks. Check the linked product sources, supported languages, regional availability, permissions and current costs. Use the same evaluation worksheet for each candidate.
59k stars · Open source · Python
A Python framework organized around agents, crews and flows. Prototype your own coordination and failure paths instead of assuming it is the quickest or easiest framework.
Capability source ↗— You need fine-grained low-level control of agent state machines.
14k stars · Open source · Python
Microsoft's framework for agent and multi-agent workflows in Python and .NET. Evaluate the current APIs and migration guidance against the AutoGen patterns your application actually uses.
Capability source ↗— You work outside .NET or Python — those are the supported languages.
43k stars · Open source · Python
Evaluate the LangGraph ecosystem when explicit state, checkpoints and human approval steps are central to your design. Distinguish the orchestration framework from hosted deployment products before comparing costs.
Capability source ↗— You want a quick no-code start — expect real engineering investment.
If you came here for AutoGen: its repository is now in maintenance mode — no new features, community managed — and Microsoft points new users at Agent Framework above, with a published migration guide. Check the published migration guidance and compatibility before changing an existing application. Beyond that, multi-agent systems multiply cost and failure modes with every added agent, and most tasks are still better served by one good agent with good tools. Reach for a crew when the task genuinely decomposes, and cap iterations so a disagreement between agents can't run your bill in a loop.
