AutoGen Alternatives: Where to Go Now It's in Maintenance Mode

If you're looking for AutoGen alternatives, the reason is probably the banner on its own repository: AutoGen states it is now in maintenance mode, will not receive new features or enhancements, and is community managed going forward.

That's the maintainers' own wording, not a third-party rumour, and it makes this an unusually clear decision. It also doesn't mean anything is broken — existing code keeps working.

Where to go depends on why you're moving:

• You want the official path → Microsoft Agent Framework, which Microsoft calls the direct successor
• You want out of the Microsoft ecosystem → CrewAI or LangGraph
• You want less framework, not a different one → direct API calls with tool use
• You want no framework at all → a hosted platform where someone else runs the agent

Quick comparison

The official path: Microsoft Agent Framework

Per the Agent Framework documentation, it is "the direct successor, created by the same teams" to both AutoGen and Semantic Kernel, and "the next generation of both." It combines AutoGen's agent abstractions with Semantic Kernel's enterprise features — session-based state management, type safety, middleware, telemetry — and adds graph-based workflows for explicit multi-agent orchestration. There's an official migration guide from AutoGen.

Choose it if you're on Azure or .NET, you want the lowest-friction migration, or you need the enterprise plumbing. It supports .NET, Python, and Go, though the Go implementation is in public preview with some capabilities not yet available.

Think twice if you were using AutoGen precisely because it was a lightweight research-friendly library. Agent Framework is aimed at production systems, and that shows in its surface area.

Leaving the Microsoft ecosystem

CrewAI is the closest philosophical match. Where AutoGen framed multi-agent work as agents conversing, CrewAI frames it as a team: agents with roles, goals, and tools that collaborate and delegate. Its documentation separates Crews (teams of collaborating agents) from Flows (structure, state, and control flow), and the recommended pattern is a Flow that calls a Crew for the steps that genuinely need one. It's independent of LangChain, contrary to a common assumption.

LangGraph is the choice if what you actually wanted from AutoGen was control. Per its documentation it's a low-level orchestration framework and runtime for long-running stateful agents, offering durable execution that resumes after failure, state and memory management, human-in-the-loop inspection, and graphs that mix deterministic steps with LLM-driven ones. You can use it without LangChain.

LlamaIndex is worth considering if your agents mostly work over your own documents — it builds agents and event-driven workflows on top of a mature data and retrieval layer. LangChain vs LlamaIndex covers that distinction.

Using less framework, not a different one

A migration is a good moment to ask whether you needed the framework.

Microsoft's own Agent Framework documentation offers the most useful advice in the category: if you can write a function to handle the task, do that instead of using an AI agent. It also draws the line between agents and workflows plainly — use an agent when the task is open-ended and needs autonomous tool use; use a workflow when the process has well-defined steps and you want explicit control over execution order.

Plenty of AutoGen projects were multi-agent because the framework made it easy, not because the work decomposed into agents. If yours calls two tools in a predictable order, direct API calls with tool use will be shorter, faster, and dramatically easier to debug. Our guide to building agents covers when the extra machinery earns its place.

Not writing the runtime at all

If the appeal was multi-agent capability rather than owning the code, a hosted platform removes the whole maintenance question.

Dify is a source-available platform for building AI applications — agents, agentic workflows, chatbots, and RAG — available as managed cloud or self-hosted via its Community Edition. Its licence is a modified Apache 2.0 that forbids running a multi-tenant service without written authorisation. n8n approaches it from workflow automation, with LangChain nodes and local model support. Dify vs n8n compares them.

The trade is control for convenience, and for teams without a Python engineer to spare it's frequently the right one.

How urgent is this?

Not very, and it's worth saying because "maintenance mode" reads more alarming than it is.

AutoGen still runs. Community maintenance continues. Nothing breaks on a schedule. The genuine costs are that new capabilities land elsewhere, and that hiring or onboarding onto a framework its maintainers have moved off gets harder over time.

A sensible posture: don't start new work on AutoGen, and migrate existing work when you're touching it anyway rather than as a standalone project.

The step before any of them

Every option here except the hosted platforms assumes you're a developer who'll write, run, and host the agent — environment, keys, deployment, and the patience to debug a multi-agent loop.

The more common stall for everyone else is seeing an agent setup someone shared that does exactly the job, and never getting it running.

Taku is built for that: an AI-native desktop workspace where you mirror an AI app or workflow that already works, run it on your own machine, and remix it into your own. Not an agent framework, and not competing with these — the step where people stall before a framework is any use. Taku is in Beta, and the Mac app is available now, and the free app library shows what mirroring looks like.

FAQ

Is AutoGen deprecated?

Its repository says maintenance mode: no new features or enhancements, community managed. That's short of deprecated — existing code works — but new projects should start elsewhere.

What is the official AutoGen replacement?

Microsoft Agent Framework, described by Microsoft as the direct successor to both AutoGen and Semantic Kernel, created by the same teams, with an official migration guide.

Do I have to migrate immediately?

No. Nothing stops working on a date. The practical advice is to avoid starting new work on it and migrate existing code when you're already in that area.

What's closest to AutoGen's multi-agent conversation model?

Agent Framework inherits its abstractions most directly. CrewAI is the closest independent equivalent, using a team metaphor rather than a conversation one.

Can I move to something that isn't a framework?

Often yes, and a migration is the right time to ask. If your workflow has well-defined steps, a workflow or plain functions with tool calls will be simpler than any agent framework.

What about Semantic Kernel?

Same story — Agent Framework is the successor to both, per Microsoft's documentation, and both lineages fed into it.