AutoGen vs LangGraph

AutoGen vs LangGraph is now a migration decision: on 27 October 2026, Microsoft's AutoGen README says the project is archived and no longer maintained. Choose LangGraph for explicit control over a stateful workflow; choose Microsoft Agent Framework when continuity with Microsoft's agent stack matters most. An existing AutoGen prototype is useful migration material, but it is a poor foundation for new production work. Current AutoGen status.

AutoGen vs LangGraph: which should you choose?

Choose LangGraph for a new system that must track work, stop for approval, and recover predictably. LangGraph, the orchestration runtime built by LangChain, lets you express the application as steps connected through explicit state. That suits a support workflow where a draft, review decision and approval must remain distinguishable after the worker restarts.

Choose Microsoft Agent Framework for an AutoGen migration tied to Microsoft's ecosystem, especially an existing .NET application. Microsoft's successor combines agent abstractions with workflows. Staying in that family still requires changes to orchestration and state handling; it is not a package rename. Microsoft's framework overview.

Keep an existing AutoGen prototype only as a bounded reference while you migrate. Preserve the prompts, tool contracts and examples that make it valuable. Avoid expanding its responsibilities while relying on an upstream maintenance commitment that no longer exists.

The decision turns on three criteria: who maintains the foundation, where application state lives, and how a paused job resumes. These are documentation-based judgments, not speed or model-quality benchmark results. The vendor pages and documentation cited here were verified on 27 October 2026.

The implementation details behind those rows come from the AutoGen team guide, AutoGen state guide, LangGraph Graph API, and LangGraph persistence docs.

AutoGen's current status: archived, beyond maintenance mode

AutoGen, Microsoft's framework for collaborating AI agents, now carries the README warning “Project archived and no longer maintained”. The same notice says Microsoft is no longer supplying bug fixes, security fixes, features or support, and encourages existing users to migrate. Its recommendation for newcomers is explicit: “New users should start with Microsoft Agent Framework.” Microsoft's AutoGen README.

Older Microsoft statements explain why a builder may remember a different policy. The 1 October 2025 launch post said: “Both projects will remain supported but most investment is now focused on Microsoft Agent Framework.” That was the launch position for AutoGen and Semantic Kernel, Microsoft's earlier agent and AI application framework. It does not override the current AutoGen warning. Microsoft's introduction post.

Microsoft's 3 June 2026 BUILD update also states: “MAF reached 1.0 GA on April 2, 2026”. GA means general availability. The successor should therefore not be described as merely the original preview announced at launch. Microsoft's BUILD update.

Winner for a fresh choice between AutoGen and LangGraph: LangGraph. AutoGen's support status settles that part of the decision before its conversation API enters the discussion. Microsoft Agent Framework remains a separate, current migration candidate.

Conversation model versus graph with state

AutoGen AgentChat makes the conversation the convenient organizing unit; LangGraph makes application state and transitions explicit. Both can coordinate agents. The useful distinction is what you must specify when something goes wrong or a person needs to intervene.

AutoGen: agents take turns and inspect messages

AutoGen AgentChat is the higher-level team API over AutoGen's event-driven Core runtime. With RoundRobinGroupChat, participants speak in order and broadcast responses to the group. A drafter can propose an answer and a reviewer can challenge it. SelectorGroupChat instead uses a model to choose the next speaker. AutoGen team patterns.

That is an intuitive way to express a conversation you already understand. The work becomes less obvious when the transcript must also act as an operational record. A sentence saying “approved” might refer to the draft's factual accuracy, a customer's consent, or a human authorization. Your application needs to distinguish those meanings before it takes an external action.

AutoGen's wall is the gap between a useful conversation and a resumable business process, now compounded by its archived status. More prompts do not define which saved artifact is authoritative or which action has already happened.

It would also be wrong to say AutoGen cannot express graphs. Its experimental GraphFlow supports sequencing, branches, parallel work and loops. A subtler limit matters more: its execution graph does not decide which messages an agent sees. Messages broadcast by default, and message filtering is separate. An edge saying “reviewer acts next” is not a data-access boundary. GraphFlow documentation.

Winner for understanding an existing conversational prototype: AutoGen AgentChat. Its team abstraction can explain the prototype concisely. That is a reason to understand the code before migrating, not a reason to begin a new deployment on it.

LangGraph: the application owns a working record

LangGraph is a low-level runtime for stateful workflows. A node performs work, an edge selects the next step, and state is the structured working record passed through the application. Nodes can contain model calls or ordinary functions. LangGraph overview.

For a support reply, state might hold the ticket identifier, approved source text, current draft, review findings and human decision. A review node updates the findings. A conditional edge sends a failed review back for revision or advances an acceptable draft toward approval. The fields and policy are your application design.

A reducer is the rule for combining an update with an existing state field. You might replace the current draft while accumulating review notes. Getting that choice wrong can leave stale findings attached to a revised answer. State updates and reducers.

LangGraph's wall is the design work it makes explicit. You own the schema, routing rules and failure behavior. A graph can also contain model-driven decisions and loops; drawing it does not make generated text repeatable or correct.

Winner for controlling a workflow's business state: LangGraph. The payoff is a record you can inspect directly instead of inferring progress from the agents' conversation.

LangGraph vs LangChain: choose the right layer

LangChain is the higher-level framework for models, tools and agent loops; LangGraph supplies orchestration underneath. You can use LangGraph without adopting LangChain's components. How the stack fits together.

For an AutoGen migration, first decide whether your application needs a custom workflow. A small assistant that calls a tool and returns an answer may not need that additional structure. The LangChain vs LangGraph comparison covers that choice in detail.

The same two-agent task in each framework

Use a drafter and reviewer to compare coordination, then keep sending outside both agents. Consider a support reply based on a fixed customer ticket and an approved knowledge excerpt. The drafter proposes an answer. The reviewer checks each claim against the excerpt and requests changes or accepts the draft. A human authorizes sending separately.

This is an illustrative design, not an executed benchmark. Both implementations receive the same inputs, prompts and review criteria. Neither agent receives a sending tool. That keeps the question focused: how does the framework represent the work and its stopping points?

These designs use the documented AutoGen reflection-team pattern, feedback between runs, LangGraph routing, and interrupts.

What you configure in AutoGen

Create the drafter and reviewer with distinct instructions, then place them in RoundRobinGroupChat. The official tutorial uses TextMentionTermination to stop on a word in an agent response. For this application, treat that stop as the end of automated review, never as authorization to send. Inspect the result and preserve the draft that the reviewer evaluated.

Give the application a bounded revision policy. If the reviewer keeps finding unsupported claims, return the case to a person with the findings attached. Do not turn an unresolved conversation into an endless attempt to produce a success token.

The additional engineering is outside the dialogue: record which ticket is being processed, save the review outcome, persist the team at a safe stopping point, and associate later human input with the right draft. The state tutorial provides serialization primitives, not your application's complete recovery policy.

What you configure in LangGraph

Define application fields such as ticket_id, source_text, draft, review, approved and send_receipt. Connect drafting to review. Route a failed review to revision while the revision policy allows it; otherwise stop for manual handling. An acceptable review advances to the human approval step.

A useful implementation rule is to invalidate the previous approval whenever the draft changes. Otherwise a person can approve one text while the application later sends another. This is your business invariant, a condition the application must always enforce, rather than a framework feature.

Sending belongs in a separate ordinary function after approval. The function records the external system's receipt against the ticket and the approved draft. An agent's review result and a person's authorization remain different fields.

Winner for this task once delayed approval and recovery matter: LangGraph. AutoGen describes the exchange neatly; the graph more directly expresses the operational record and the gates around it. If the task ends with an unsent draft and is easy to restart, the migration has fewer workflow requirements to solve.

Checkpoints and human interrupts: what recovery really means

Saving conversation state is useful, but it is not the same contract as resuming a workflow. AutoGen supports save_state() and load_state() on agents and teams. Your application can persist that state to a file or database. Saying AutoGen has “no memory” would misdescribe the available API. AutoGen state management.

LangGraph uses a checkpointer, a component that saves a thread's graph state. A thread identifies a continuing piece of work. Its separate store holds application data that should be available across threads. Use the former to continue the current ticket and the latter, if needed, for shared durable information. Persistence concepts.

The storage choice matters. InMemorySaver and MemorySaver keep checkpoints in RAM; those checkpoints disappear when the process restarts. A prototype demonstrating an interrupt with in-memory storage has not demonstrated restart recovery. Use persistent storage for that requirement. Persistence troubleshooting.

For delayed review, LangGraph's interrupt() pauses execution for external input. Resume with Command(resume=...) and the same thread_id used for the paused run. The identifier connects the arriving decision to the saved work. LangGraph interrupt behavior.

AutoGen's UserProxyAgent can request human feedback during a run, but its documentation warns that the waiting team is blocked in a state that cannot be saved or resumed. For a person who may return later, the documented alternative is to end the run, save state, and continue with feedback in a later run. AutoGen human feedback.

For the support example, protect dispatch with an application-owned request identifier and a stored send receipt. Put the send operation after approval and design it to tolerate repeated requests. The position of an approval gate does not by itself make a separate email service transactional.

Winner for durable, delayed approval: LangGraph, provided you configure durable storage and design repeated execution deliberately. Neither a checkpoint nor a chat transcript is proof that an external action happened exactly once.

Migrate to LangGraph or Microsoft Agent Framework?

Choose the destination by the control you need to own, then compare the rewrite against your current application. Moving away from AutoGen does not automatically mean choosing LangGraph.

LangGraph vs AutoGen for an existing prototype

Choose LangGraph when the prototype's pain is ambiguous state: you cannot tell which artifact is current, where a failed job should continue, or whether a human approved the current version. Translate those uncertainties into explicit fields and transitions before porting the prompts.

The useful change is making those requirements inspectable. Recreating the old transcript inside a graph while leaving every important decision buried in messages gives you a new dependency without much operational improvement.

If the prototype's agent roles do not add distinct tools, context or review responsibilities, simplify them during migration. The multi agent systems guide explains when a single agent is the better design.

Microsoft Agent Framework for ecosystem continuity

Microsoft Agent Framework is Microsoft's direct successor to AutoGen and Semantic Kernel. It offers agents and workflows that connect agents with ordinary functions. It is the stronger starting candidate when your application and operating team already depend on Microsoft's agent stack or .NET. That is an integration judgment, not a claim that LangGraph cannot serve the same business process. Agent Framework overview.

The current guide maps AssistantAgent to Agent, explains conversation continuity through AgentSession, and replaces team coordination with workflow patterns. Those workflows provide typed data flow, human input requests and checkpointing. Tool-call iteration behavior also differs, so replay your tool sequences during migration. AutoGen migration guide.

The named migration gap: custom distributed runtimes. The guide currently describes Agent Framework composition as single-process and distributed execution as planned. If your AutoGen prototype uses its distributed Core runtime, do not assume an agent-level port preserves the deployment topology. Validate that requirement separately against the actual runtime and hosting option you intend to use. This is a limitation stated in the migration guide, not a blanket statement about every Microsoft hosting service.

Winner for Microsoft successor continuity: Microsoft Agent Framework. Its wall is compatibility: familiar agent concepts do not guarantee identical message routing, session behavior or execution topology. LangGraph wins when the reason for leaving is a deliberate redesign around explicit application state.

What switching costs, and who should not rush it

The migration cost is behavior reconstruction, state conversion and operational validation. Prompts and tool implementations may be reusable, but their scheduling, context and persistence rules need examination. Budget the work around a complete business operation rather than a count of renamed imports.

Start with the support reply. It has an observable input, a draft artifact, a review decision, a human gate and a final receipt. That makes it a better migration slice than moving an entire agent collection and hoping the final answer looks familiar.

1. 1

   Capture the existing behavior

   Save representative inputs, approved source material, prompts, tool schemas, outputs and failure cases. Record model and dependency versions. Include a case that the reviewer rejects and one that requires human clarification. These become acceptance examples for the new implementation.

2. 2

   Export application records independently

   Preserve ticket identifiers, drafts, review findings, human decisions and external-action receipts in an application-owned format. Keep transcripts for context, but do not treat a framework's serialized state blob as a portable business record. Write an explicit mapping for any data you need to continue processing.

3. 3

   Rebuild the smallest complete workflow

   In LangGraph, define the state schema and routes. In Microsoft Agent Framework, map agents, sessions and workflow executors using the current guide. Hold the task inputs and evaluation criteria steady so you can distinguish orchestration changes from changes to the model or prompt.

4. 4

   Exercise the failure boundaries

   Stop the worker while a job waits for approval. Resume it after restart, reject a draft, repeat a resume request, and simulate a failed external action. Inspect the saved application record and final receipt. A plausible final paragraph is insufficient evidence that the workflow recovered correctly.

5. 5

   Choose how work already in flight finishes

   Decide whether pending jobs finish under the old implementation or receive an explicit state conversion. Preserve identifiers and approval-to-draft associations. Do not silently send an old checkpoint into an incompatible new workflow.

LangGraph has its own migration constraints once adopted. Its Graph API documentation warns against renaming or removing nodes needed by interrupted threads. Treat workflow definitions and saved state as a compatibility boundary during future deployments too. Graph migration constraints.

Do not rush a broad rewrite of an isolated research prototype whose useful output is already captured and that will be retired. Extract the lessons and migrate only the continuing use case. For an active AutoGen application, a controlled transition is preferable to an unvalidated replacement, but the archived status is a reason to schedule that transition, not to keep expanding the old dependency.

For a broader shortlist, the AI agent frameworks comparison covers other starting points. If CrewAI is the remaining candidate, use LangGraph vs CrewAI for that specific decision.

Your next move: take one recurring job, write down the records that must survive a restart, and define who can authorize the final action. Choose the framework that expresses those requirements most clearly, then validate that complete slice before moving the rest.

Frequently asked questions

What are the key differences between AutoGen and LangChain?

AutoGen AgentChat organizes collaborating agents around teams and messages. LangChain supplies model, tool and agent abstractions, while LangGraph is its underlying orchestration runtime. For a custom stateful workflow, LangGraph is the more direct comparison. AutoGen's archived status also changes the choice for new work.

Is it worth learning LangChain in 2026?

Yes, when its model and tool abstractions fit your application. Learn LangGraph directly when you need to specify the workflow's state and transitions. You do not need to adopt every layer of the stack to migrate an AutoGen prototype.

Is CrewAI better than LangGraph?

There is no universal winner. If explicit state, recovery boundaries and custom approval logic decide the application, LangGraph is the candidate to evaluate first. Compare CrewAI against the same complete task before choosing; framework preference alone does not establish task quality.

What does AutoGen mean?

In this comparison, AutoGen means Microsoft's multi-agent application framework at microsoft/autogen. The implementation discussed is its AgentChat team API, with its underlying Core runtime and experimental GraphFlow distinguished where relevant.

Is AutoGen deprecated?

Microsoft's current README uses the stronger status “archived” and says the project is no longer maintained. It directs new users to Microsoft Agent Framework and encourages existing users to migrate. Use that current wording rather than assuming an ongoing maintenance phase.

Is AutoGen the same as Microsoft Agent framework?

No. Microsoft Agent Framework is the successor, with a migration guide covering changed agent, session, tool and workflow APIs. Moving to it requires validating the application's behavior; it is not simply a new name for the installed AutoGen package.

Which agentic AI framework is considered the best?

For this reader, choose LangGraph when explicit application state and workflow control are decisive. Choose Microsoft Agent Framework when Microsoft's successor path and your existing ecosystem are decisive. A framework cannot establish whether the underlying task needs multiple agents.

What are the key differences between AutoGen and Semantic Kernel?

Microsoft's introduction post describes AutoGen's contribution as experimental multi-agent orchestration and Semantic Kernel's as an enterprise application SDK foundation. Microsoft Agent Framework brings those lines together. For a new AutoGen migration, evaluate the current successor instead of treating that historical split as the only choice.

What is the best multi-agent framework?

The best fit is the one that makes your required behavior clear and maintainable. For the support workflow here, LangGraph is the recommendation because review, human authorization and sending must be separate states. Microsoft Agent Framework is the alternative when successor continuity matters more.

Use the AI Business Workflow Audit Checklist to define the job, its approval boundary and its operating owner before migrating.