CREWAI VS AUTOGEN: WHAT TO USE NOW

If you came here to choose between CrewAI and AutoGen, the choice has changed: AutoGen is in maintenance mode and Microsoft directs new users to Microsoft Agent Framework. The underlying question is still the right one, though, so this compares CrewAI with MAF — and the distinction that actually decides it is whether you can write the process down in advance.

What changed#

AutoGen's README carries a maintenance-mode badge: it "will not receive new features or enhancements and is community managed going forward", and new users are pointed at Microsoft Agent Framework. Microsoft describes MAF as the direct successor, built by the same teams, combining AutoGen's agent abstractions with Semantic Kernel's enterprise features and adding graph-based workflows.

So the old framing — declared coordination versus emergent conversation — has moved. MAF keeps the agent abstractions but makes orchestration explicit: sequential, concurrent, handoff and group-collaboration patterns, with checkpointing and human-in-the-loop. It is much closer to CrewAI in philosophy than AutoGen ever was.

The real difference now#

CrewAI is Python-only and opinionated: you declare agents with roles and the tasks they perform, and the framework's shape pushes you toward writing the process down. It is quick to get moving and the mental model fits how people already describe work.

MAF is broader and more infrastructural. It runs on .NET and Python (agent-framework on PyPI, Microsoft.Agents.AI on NuGet), ships OpenTelemetry observability, middleware, declarative YAML agents and a developer UI, and interoperates through MCP and A2A. That surface is the point if you are taking agents to production; it is overhead if you are testing an idea this afternoon.

A decision table#

• Python shop, process is known, you want to move today → CrewAI. Writing the steps down is not a limitation, it is documentation.
• .NET anywhere in the estate → MAF, and it is not close. CrewAI has no .NET story.
• You need durability, restartability or auditability → MAF. Checkpointing and built-in tracing are the features you would otherwise build yourself, badly, at the worst possible moment.
• Already on Semantic Kernel or AutoGen → MAF. Microsoft publishes migration guides from both; that consolidation is the whole point of the project.
• A wrong step is expensive → consider LangGraph too, where permitted transitions are written down and a human gate is just a node.

What about existing AutoGen systems?#

Maintenance mode means no new features and community management — not deletion. Working software does not stop working because a badge changed, and a running system with tests around it is not an emergency. What it does mean is that you should not start anything new there, and should not plan a roadmap that assumes upstream will meet you halfway.

The question underneath#

Before choosing either, be honest about whether you need multiple agents at all. Multi-agent architectures multiply your token spend, your latency and your failure surface, and a great many problems that look like a team of specialists are one competent agent with several tools.

The multi-agent case is strong when sub-tasks need genuinely different context, when you want isolation between a generator and its critic, or when parallelism buys real wall-clock time. It is weak when the honest reason is that a team of agents sounds more impressive. We put the case for restraint in Do You Need an Agent Framework at All?