We are evaluating frameworks for a massive internal automation project. While CrewAI is great for simple roles, AutoGen seems to handle conversational group chats and code execution much better. Given the recent merger into the Microsoft Agent Framework, is it now the "safe" bet for long-term enterprise scalability? We need something that won't be obsolete by next year.
3 answers
In my experience leading cloud migrations, AutoGen is definitely the more "enterprise-ready" choice if you are already in the Azure ecosystem. The biggest draw isn't just the multi-agent conversation logic, but the built-in code executor which can run in a secure Docker sandbox. This is critical for Business Analysis where you need an agent to actually write and run Python to analyze a CSV file rather than just guessing. Since it has merged with Semantic Kernel, you now get production SLAs and C# support, which is a massive win for traditional IT shops. It feels less like an experimental library and more like a core piece of the Microsoft stack.
That sounds robust, but doesn't the "group chat" model in AutoGen lead to higher token costs compared to a direct state graph?
For startups, it might be overkill, but for a global firm, the security and multi-language support in AutoGen make it the standard.
I agree with Bradley. The cross-language support for .NET and Python is something no other framework is doing well right now.
You've hit on a key point, Austin. In a raw group chat, every agent sees every message, which can bloat the context window quickly. However, with the new "GroupChatManager" configurations, you can limit which agents see what. It requires a bit more fine-tuning than a simple linear chain, but the flexibility it gives for agents to "debate" a solution usually results in much higher quality output for complex logic problems, which justifies the cost for many of our enterprise clients.