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LangGraph vs. CrewAI vs. Microsoft Agent Framework vs. n8n

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The short answer



Pick when you're modeling a team of specialist agents with clear roles and want that structure to stay readable. Pick 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 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 .

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