The rule we apply: LangGraph for production. AutoGen for research and for problems whose natural model is a conversation, not a graph.
Where AutoGen wins
- Conversational coordination is the natural fit for some multi-agent problems.
- Code execution agents are first-class and easier to set up than in LangGraph.
- Research-shaped flexibility for one-off explorations.
Where LangGraph wins
- Determinism. The graph shape is verifiable; conversation flow is not.
- Cost predictability. Bounded by the graph; AutoGen's call count is bounded by conversation length, which is harder to cap.
- State checkpointing for failure recovery.
Cost comparison
Per-task cost on equivalent workloads is meaningfully higher for AutoGen because of conversational call counts. We have measured 20+ LLM calls per task in AutoGen on workloads where LangGraph used 3-5. The expressiveness has a price; budget for it.
Three scenarios, three decisions
- Research a multi-agent debate or negotiation: AutoGen.
- Ship an orchestrator-worker pattern in production: LangGraph.
- Migrate an AutoGen prototype to production at scale: Rewrite in LangGraph.
Read next
Oliver runs Digital Signet, a research and product studio that operates ~500 production sites with AI agents as the engineering layer. The Digital Signet portfolio is built using a continuous AI-agent build pipeline, one of the largest agent-operated publishing operations on the open web. The handbook draws directly from those deployments: real cost data, real failure modes, real recovery patterns.