LangGraph vs AutoGen
AutoGen let agents talk their way to an answer and was fast to demo. Microsoft has since put it in maintenance mode. LangGraph is the one still getting the checkpoint and control-flow work a production system needs.
The short answer
Choose LangGraph for a production build; treat AutoGen as a framework Microsoft has stopped actively developing.
AutoGen organised multi-agent work as a conversation: agents pass messages back and forth until one produces a final answer. It was quick to set up and easy to explain, which is why it spread fast after Microsoft released it.
Microsoft put AutoGen into maintenance mode in October 2025. That means bug and security fixes only, no new features. New production work now goes through Microsoft Agent Framework instead. It shipped 1.0 in April 2026 and merges AutoGen with Semantic Kernel into one supported SDK. LangGraph never had that interruption. It has kept its typed state and checkpointed graph model the whole time. That is what a system needs to resume cleanly after a failure.
Side by side
Positioning drawn from each project's own documentation and Microsoft's own migration guidance, checked in August 2026.
| Dimension | AutoGen | LangGraph |
|---|---|---|
| Current status | Maintenance mode since October 2025: fixes only, no new features | Actively developed, no comparable interruption |
| Where Microsoft sends new production work | Microsoft Agent Framework, its merger of AutoGen and Semantic Kernel | Not a Microsoft product, so this question does not apply |
| How it models a run | A conversation between agents passing messages | A graph of nodes and edges you define explicitly |
| State across steps | Conversation history; durable memory needs extra setup | A typed state object, checkpointed after every node |
| Recovering from a failure mid-run | App-managed. You design the save and reload yourself | Resumes from the last checkpoint by default |
| Best-fit problem | An existing AutoGen 0.2 deployment staying on the community fork, AG2 | A new build, or one that needs resumable, auditable control flow |
| Wrong fit | Any new production system started today | A same-day demo with no state worth persisting |
- Task arrivesBoth frameworks can start the same run
- AutoGen: agents converseMessages pass back and forth until one settles the task
- LangGraph: enters the graphA named node handles this step, state is typed
- A step failsWhere the two diverge
- AutoGen: your app decidesRecovery is code you write yourself
- LangGraph: resumes from checkpointState from before the failure was already saved
AutoGen never built a built-in answer to this. LangGraph was designed around it.
AutoGen
Where it wins
- Conversation-style setup reads naturally, so a first version is easy to explain to a non-technical stakeholder.
- Getting two or three agents talking to each other is fast, which suited early prototypes and research demos.
- The community fork, AG2, keeps API compatibility for teams already running AutoGen 0.2 who are not ready to migrate.
- Message-passing between agents is built in, so early experiments do not need custom orchestration code.
Where it hurts
- Microsoft put the project into maintenance mode in October 2025: security and bug fixes only, no new capability.
- New Microsoft production guidance points to Microsoft Agent Framework instead, which is a different codebase to adopt.
- Recovering a failed run is not built in. Your application has to save and reload state itself.
- Tracing why a conversation went one direction over another gets harder as the number of agents grows.
LangGraph
Where it wins
- Checkpointing is built in, so a crashed run resumes from its last saved state instead of starting over.
- The graph makes control flow explicit. Every branch, retry and stopping point is something you wrote and can read back.
- State is typed and updated node by node, which keeps a long-running process debuggable months after launch.
- Fits a process that must pause for a human approval or follow a strict order that cannot be skipped.
Where it hurts
- There is no ready-made cast of conversing agents. You design the graph, which takes longer to get a first version running.
- A same-day internal script gets more structure than it needs when there is no state worth saving.
- The graph-based model takes longer to learn than assigning agents a role and letting them talk.
- A team new to graph-based thinking needs ramp-up time before it can extend the graph safely.
How to choose
- Choose LangGraph for any system you are starting now. AutoGen is not the framework Microsoft is putting new engineering into.
- Choose LangGraph if a failed step has to resume where it left off, not restart from the beginning.
- Stay on AutoGen, through the AG2 fork, only if you already have an AutoGen 0.2 system in production and a migration is not planned yet.
- If you are already committed to the Microsoft stack, evaluate Microsoft Agent Framework directly rather than starting a new build on AutoGen.
- Choose neither yet if nobody has written down what the agent should do when a step fails. That gap breaks any framework the same way.
- Move off AutoGen once a prototype needs to survive real users. The first unhandled failure in a long run is usually where that shows.
A conversational agent built on LangGraph, in production
Questions engineers ask before committing
01Is AutoGen dead?
No, but it is no longer where Microsoft puts new engineering. AutoGen entered maintenance mode in October 2025. That means bug and security fixes only. New production work goes through Microsoft Agent Framework instead. It merges AutoGen and Semantic Kernel, and it shipped its 1.0 release in April 2026.
02What is AG2, and is it different from AutoGen?
AG2 is a community fork of AutoGen 0.2. It is kept alive for teams that already have an AutoGen deployment and are not ready to migrate. It keeps API compatibility with the version it forked from. New AutoGen-style features are not being built there.
03Should I migrate an existing AutoGen system to Microsoft Agent Framework or to LangGraph?
The rest of your stack decides this. Deep in Azure and Semantic Kernel already? Microsoft Agent Framework is the path Microsoft built for that migration. Want checkpointed, resumable graphs with no recent framework split? LangGraph is the more established choice.
04Why does checkpointing matter this much?
A production agent will fail mid-run eventually, from a timeout, a bad response or a dependency going down. Without a checkpoint, the only recovery is starting the task over, which can repeat side effects like a sent email or a booked order. A checkpoint means the system resumes from the last known good state instead.
05Do we need a multi-agent framework at all?
Not for a first version. A single, well-prompted agent handling one job directly is often clearer to build and debug than a crew of conversing agents or a graph. Reach for either framework once the task genuinely splits into distinct roles or distinct states worth tracking on their own.
Related
- AI agent frameworks, ranked →Where LangGraph sits against the rest of the field.
- CrewAI vs LangGraph →The other pairwise comparison in the same framework decision.
- how we run LangGraph in production →Checkpointers, resume behaviour and what a state model actually buys you.
- single agent vs multi-agent →The decision that comes before picking a framework.
