Best AI Agent Framework 2026: LangGraph vs CrewAI vs n8n

Pick on control model and who writes the workflow, not on star counts. Read down and stop at the first line that describes you.

An agent orchestration framework manages the control flow, state and coordination between multiple LLM calls so you do not have to hand-write the loop, the retries and the message passing yourself. It handles which agent runs next, what each one can see, what happens when a step fails, and how a long-running job resumes after a pause.

That is genuinely useful once you have several agents, branching logic, or state that has to survive a restart. It is overhead when you have one agent making one tool call — which is why the most common mistake in this category is adopting a framework before the problem needs one.

No framework wins across the board: LangGraph takes control, CrewAI takes speed, Microsoft Agent Framework takes the Azure stack, n8n takes accessibility. Star counts, licences and activity were read from the GitHub API on 28 August 2026; prices come from each vendor's own pricing page the same day.

LangGraph is the one to pick when the agent has to be inspectable, resumable and correct rather than merely impressive in a demo. It models the workflow as an explicit directed graph with shared state, which is more work up front and considerably less work when something goes wrong at three in the morning.

CrewAI wins on the distance between an idea and something running. Its abstractions match how people already describe multi-agent work out loud: a researcher agent hands off to a writer agent, each with a task, together forming a crew.

If your infrastructure is Microsoft-centric, this is the current answer, and it replaced AutoGen rather than sitting alongside it. Microsoft publishes a migration guide from AutoGen and describes Agent Framework as the enterprise-ready successor.

n8n is the answer when the person building the workflow does not write Python, or when the agent is one step inside a much larger business process. It is a general workflow automation platform with AI agent nodes, not an agent framework that grew outward.

Do not start new work on AutoGen, and be careful with any guide that still recommends it. The `microsoft/autogen` repository carries a maintenance-mode banner stating that AutoGen "will not receive new features or enhancements and is community managed going forward", and directing new users to Microsoft Agent Framework. Microsoft publishes a formal migration guide for existing users.

The repository still shows 60.7k stars, and that number is the source of a widespread error. Several comparisons attribute roughly 60k stars to AG2, the community fork, and present it as the thriving continuation. AG2 in fact has 4.9k stars and is Apache-2.0 licensed, not MIT. It is genuinely active — it was pushed the same day we checked, and carries far fewer open issues than the original — but it is a much smaller project than those write-ups imply.

There is also a licensing subtlety worth knowing: the AutoGen repository ships two licence files. `LICENSE-CODE` is MIT and covers the code, while `LICENSE` is Creative Commons Attribution 4.0 and covers non-code content. Repository metadata reports the Creative Commons one, which is why some tables show AutoGen with a content licence.

Every orchestration library here is free; you pay for managed hosting, tracing and governance, never for the agent logic itself. That means the real budget question is which managed layer you will end up needing, and it is usually smaller than your model token spend either way.

Three of these four are OSI-approved open source under MIT; n8n is not, and that distinction can matter more than any feature. LangGraph, CrewAI and Microsoft Agent Framework are all MIT-licensed, which means you can use, modify and redistribute them commercially with essentially no restriction beyond attribution.

n8n uses the Sustainable Use License. The Community Edition is free and the source is available, but the licence restricts reselling n8n itself, which is a real constraint if you intend to build a product whose core value is a hosted n8n. This is not a criticism of the licence — it is a deliberate and reasonable business model — but it is a legal question you should answer before architecture, not after. Read the licence text rather than the marketing page.

The AG2 fork is Apache-2.0, which is also OSI-approved but includes an explicit patent grant that MIT lacks. For most teams this makes no practical difference; for legal review at a large company it sometimes does.

Your control requirements and who writes the workflow decide this. Five profiles cover most readers.

An agent framework decides where your prompts, tool outputs and intermediate reasoning go. That makes the hosted-versus-self-hosted question a compliance question in three major markets, not just an operations preference.

If your use case is one agent calling one or two tools in a loop, skip all four and write it directly against your model provider's function-calling API. You will have fewer dependencies, a shorter stack trace when something breaks, and nothing to migrate when the framework changes its abstractions — which, as the AutoGen situation this year shows, they do.

The threshold worth waiting for is concrete: multiple agents that must coordinate, state that has to survive a process restart, or a human approval step in the middle of a run. Any one of those justifies a framework. None of them present means the framework is solving a problem you do not have, at the cost of a dependency you will have to track.