The short answer
Pick LangGraph when you need to control exactly how an agent moves between steps and retries. Pick CrewAI when you're modeling a team of specialist agents with clear roles and want that structure to stay readable. Pick Microsoft Agent Framework when your org already lives in the Microsoft/Azure stack and needs one supported SDK across Python, C#, and Java. Pick n8n when the people building and maintaining the workflow aren't primarily software engineers, or when the agent needs to sit next to 400 other business integrations you already run.
None of these are mutually exclusive. A common production pattern we see in the Automations Cookbook recipes is n8n orchestrating the business trigger (a webhook, a form, a schedule) and calling out to a LangGraph or CrewAI service for the part that actually needs multi-step reasoning.
What each one is actually for
LangGraph — state machines for agents
LangGraph models an agent as an explicit graph: nodes are steps, edges are transitions, and state is a typed object that flows between them. That gives you two things frameworks with more implicit control loops don't: you can pause, inspect, and resume execution at any node, and you can enforce that certain transitions never happen. Teams building on LangGraph tend to be doing so because they've already hit the wall with a simpler "just call the LLM in a loop" approach and needed real control over branching and retries.
CrewAI — role-based multi-agent teams
CrewAI's core abstraction is the crew: a set of agents, each with a role, a goal, and a backstory, working through a sequence of tasks. It reads closer to an org chart than a state machine, which makes it the easier framework to hand to a teammate who isn't deep in the codebase — the config genuinely describes what the system does. CrewAI has grown fast enough to become one of the most-starred agent frameworks on GitHub, and it now ships an enterprise tier aimed at teams that outgrew the open-source crew definition files.
Microsoft Agent Framework — the AutoGen successor
If you built on AutoGen, this is where that investment goes now. Microsoft merged AutoGen with Semantic Kernel into a single Microsoft Agent Framework in October 2025, and AutoGen itself is in maintenance mode — new projects shouldn't start on it. The pitch for Agent Framework is consistency: the same agent orchestration concepts across Python, C#, and Java SDKs, wired into Azure AI Foundry and the rest of the Microsoft enterprise stack. It's the pragmatic default if procurement, security review, and long-term support already point you at Microsoft.
n8n — the no-code layer that talks to all of them
n8n isn't trying to be an agent-reasoning framework — it's trying to be the workflow layer that triggers, sequences, and connects everything else, including calls out to LangGraph or CrewAI services. Its advantage is breadth: hundreds of pre-built integrations (Slack, HubSpot, Google Sheets, Stripe) that would otherwise be custom code in a pure-code agent stack. For teams where the actual agent logic is simple (classify, summarize, route) but the surrounding business process touches a dozen SaaS tools, n8n is usually the faster path to production — see the AI Workflow Automation guide for patterns.
How to actually decide
Ask who maintains this after you ship it. If it's an ops or RevOps person, not an engineer, n8n's visual workflow wins even if it's less flexible. If it's an engineering team that needs fine-grained control over agent state and retries, LangGraph earns its complexity. If the mental model is "a team of specialists with different jobs," CrewAI keeps that structure honest as the system grows. If your organization already has Microsoft/Azure procurement, security, and support relationships in place, Agent Framework removes a category of approval friction the others don't.
FAQ
Q: Is AutoGen still worth learning in 2026?
A: Only if you're maintaining an existing AutoGen system. Microsoft has moved new development to Agent Framework, and AutoGen is in maintenance mode.
Q: Can I use n8n and LangGraph together?
A: Yes — it's a common pattern. n8n handles triggers, integrations, and the surrounding business workflow; a LangGraph service handles the part of the task that needs stateful multi-step reasoning, called from an n8n HTTP Request node.
Q: Which of these is easiest for a non-engineer to maintain?
A: n8n, by a wide margin — its visual editor is built for that. CrewAI's role/task config is the next easiest to read without a deep coding background.
Originally published on Automations Cookbook.
