n8n vs LangGraph: Which Should You Use for AI Agents?

Short answer: Use n8n when the hard part of your project is connecting systems: triggers, SaaS apps, databases, retries and a visual run history that non-developers can follow. Use LangGraph when the hard part is the agent's reasoning: stateful loops, branching on model output, multi-agent hand-offs, long-running tasks and human approval mid-run, all in Python or TypeScript code. They are not really rivals. A common production pattern is n8n as the plumbing (trigger, data prep, delivery) calling a LangGraph agent over HTTP for the "think hard" step.

New to the vocabulary? Start with What is agentic AI?. Comparing code frameworks only? See LangGraph vs CrewAI vs AutoGen.

What each tool is

n8n describes itself as a fair-code platform to build and deploy AI agents and workflows, combining a visual canvas with custom code, run self-hosted or in n8n's cloud (n8n on GitHub). You build workflows from nodes: a trigger (webhook, schedule, app event), app nodes, code nodes (JavaScript or Python) and AI nodes such as the AI Agent with a model, memory and tools attached. Its docs cover connecting providers such as OpenAI, Anthropic and Google, adding tools and memory, and connecting agents to MCP servers (n8n docs: Integrate AI).

LangGraph is "a low-level orchestration framework and runtime for long-running, stateful agents" (LangGraph overview). You define your agent as a graph in code: nodes are functions that read and update a shared state, and edges (often conditional) decide what runs next. It focuses on durable execution, streaming, human-in-the-loop and memory, and does not prescribe prompts or agent architecture.

Side-by-side comparison

A fair note on sources: n8n publishes its own n8n vs LangGraph comparison. It is useful for facts about n8n's features, but it is written by one of the two vendors, so we've used it only for n8n's side and checked LangGraph's claims against LangGraph's own docs.

When to choose n8n

• Your agent's value is mostly integration: read a form, look up a CRM, call a model, update a sheet, post to a chat tool.
• You need triggers out of the box: webhooks, schedules, app events.
• Non-developers must be able to see and fix a failed run.
• You want to prototype in hours, then decide what deserves real code.
• You want to self-host for data control and your use is internal. Check the Sustainable Use License terms if you plan to resell n8n-based services.

When to choose LangGraph

• The agent must loop: plan, call tools, check its own output and retry.
• You need multi-agent designs with explicit hand-offs.
• Runs are long or must survive failures and resume where they stopped.
• A human must approve or edit the agent's state partway through.
• Your team wants agent logic in the codebase, with unit tests, code review and CI like any other service.

Using both together

You don't have to pick one. A widely used split:

1. n8n receives the event (webhook, schedule, new ticket), cleans and enriches the data.
2. n8n calls a LangGraph agent exposed as an HTTP endpoint and waits for a structured response.
3. LangGraph does the multi-step reasoning, with state, limits and human approval where needed.
4. n8n routes the result to email, CRM, Slack or a database, and handles retries and alerts.

The seam is a clean HTTP API, so you can replace either side later. A sign it's time to move logic out of n8n: you are rebuilding state management with chains of IF and loop nodes, or your agent needs memory that persists between runs. A sign you over-engineered: a LangGraph service that only calls one model and writes one row.

Where MCP fits: both sides can use the Model Context Protocol. n8n documents connecting agents to MCP servers, and a LangGraph agent can call tools exposed by MCP servers. If you build one MCP server for your company's data, both can use it. See our MCP server guide.

Which should you learn first?

Whatever you start with, prove it with a project: see 10 agentic AI projects for beginners. Projects 1 and 2 suit n8n; 8 to 10 suit LangGraph.

Common mistakes

• Choosing a tool from a comparison table instead of from your bottleneck (integration vs reasoning)
• Building complex agent loops in a visual canvas until nobody can debug them
• Writing a code framework service for a simple "trigger, call model, save result" flow
• No step limits or budgets on agent loops in either tool
• Giving the agent write access to production systems during development
• Skipping evaluation: test both tools' outputs on a fixed set of real examples

Learn both, hands-on

Datamarcos's Applied Agentic AI Certification teaches n8n, LangChain, LangGraph, CrewAI and MCP in an 8-week, live online, mentor-led format, with a hands-on project in every module and an integrated multi-agent capstone. No prior AI experience is required; you should be open to learning basic Python. For a deeper engineering track, see Generative AI & Agentic AI Engineer. If you mainly want to use AI tools in everyday work, see GenAI at Work. Live online, so you can join from anywhere. There is no placement guarantee.

For teams, Datamarcos offers corporate training and has trained teams at 200+ organizations.

CTA: Talk to a counsellor about which tool and program fit your goals.