Comparing AutoGen and LangGraph for production-grade AI agents

All answers to this question.

Having used both in production, the choice really depends on your workflow complexity. LangGraph is exceptional if you need a very specific, directed acyclic graph (DAG) where you control every state transition. It feels more like a traditional workflow engine. AutoGen, on the other hand, excels in "emergent" behavior where agents need to chat back and forth dynamically to solve a problem. In an enterprise setting, AutoGen’s recent updates to support Docker sandboxing for code execution give it a slight edge in safety for tasks involving data analysis or script generation.

   Answered 2025-09-22 by Deborah Lewis

Does the recent maintenance mode announcement for the original AutoGen library change your recommendation for new enterprise projects starting now?

   Answered 2025-10-10 by Kevin Parker

• Kevin, it actually makes the recommendation stronger but changes the target. You shouldn't be building on the legacy AutoGen; you should be moving toward the unified Agent Framework. Microsoft isn't "killing" the tech; they are maturing it. The migration path is fairly straightforward, and the new modularity actually makes it easier to swap out LLM providers, which is a major requirement for most corporate compliance teams I work with.

     Commented 2025-10-20 by William Turner

For me, the conversation patterns in AutoGen are just more natural. It’s much easier to model a "manager and worker" relationship than it is in LangGraph.

   Answered 2025-11-02 by Karen Phillips

• Agreed, Karen. Thomas, if your support bot needs to escalate through multiple "specialist" agents, the AutoGen pattern will save you a lot of manual logic coding.

     Commented 2025-11-08 by Thomas Scott