n8n vs LangGraph: Which Fits Your Team?
This page may contain affiliate links. If you buy through them we may earn a commission at no extra cost to you.
Verdict: if your team writes code and needs precise, explicit control over how an agent reasons, retries, and loops through a multi-step task, LangGraph is built for exactly that. If you want a working AI agent live today, wired into Slack, a CRM, or a spreadsheet without writing integration code, n8n's visual canvas gets there faster. We looked at where each one's control over agent behavior actually comes from, since that's the real difference between them, not just "no-code vs. code."
What n8n and LangGraph actually are
n8n is a node-based workflow automation platform. Its AI Agent node, added as the category matured, lets a workflow reason through a task using an LLM and call other nodes as tools, all assembled visually alongside n8n's 400+ app integrations, logic branches, and triggers. Agent behavior is one capability inside a much broader automation tool.
LangGraph is an open-source library from the LangChain team for building agents as an explicit graph: nodes represent steps, edges represent the possible transitions between them, and a shared state object carries data through the whole run. Unlike a simple linear chain, LangGraph's graphs support cycles, letting an agent loop back and retry or re-reason, and human-in-the-loop interrupts that pause execution for approval before continuing. It's a developer tool first: you write the graph in Python or JavaScript, there's no visual canvas.
Comparison table
| n8n | LangGraph | |
|---|---|---|
| Interface | Visual node canvas | Code (Python/JavaScript library) |
| Core design | General automation platform with an AI Agent node | Purpose-built framework for stateful agent graphs |
| Control over agent state/loops | Limited to what n8n's nodes expose | Explicit, fine-grained, built into the framework |
| App integrations | 400+ pre-built nodes | None built in; you write the integration code |
| Self-hostable | Yes, free | Yes, the library is free and open source |
| Hosted option | n8n Cloud, from ~$24/month | LangGraph Platform, usage-based pricing |
| Best for | Non-developers, fast time-to-working-agent | Engineering teams needing precise agent control |
Where the real difference shows up
The gap isn't "visual vs. code" in the abstract, it's what each one makes easy. In n8n, wiring an agent to five different business apps is five drag-and-drop nodes; building the same integrations from scratch in LangGraph is five pieces of connector code someone on your team has to write and maintain. That's a decisive advantage for a team without engineering resources, or one that just wants an agent handling a real task this week.
The gap runs the other way the moment an agent needs genuine control over its own reasoning loop: retry a step with adjusted parameters if the first attempt fails, pause for a human to approve a risky action before continuing, or branch based on intermediate state that isn't just a simple if/else. LangGraph's explicit graph and state object are built for exactly that kind of control, and expressing the same logic in n8n means composing it from general-purpose conditional and loop nodes that weren't designed around agent state specifically. It's possible, but you're working against the tool's design rather than with it.
Pricing reality check
Both are free to start. n8n's Community Edition self-hosts for free indefinitely under its Sustainable Use License, with n8n Cloud available from around $24 a month if you'd rather not manage a server. LangGraph the library costs nothing either; it's open source and you can run agents built with it on your own infrastructure at no licensing cost. Where cost enters for LangGraph is LangGraph Platform, the LangChain team's managed deployment and observability layer, which bills by usage if you want hosted infrastructure and monitoring rather than running the graph yourself. Neither pricing model obviously wins; they diverge on whether you're paying with money (a managed platform) or engineering time (self-hosting either one).
When NOT to use n8n
Skip n8n if your agent genuinely needs the kind of fine-grained control LangGraph is built for: explicit retry logic tied to state, multi-branch reasoning that isn't a simple conditional, or a human-approval checkpoint mid-run that has to pause and resume cleanly. You can approximate some of this in n8n, but you'll spend real time working around a canvas that wasn't designed for it.
When NOT to use LangGraph
Skip LangGraph if nobody on your team writes Python or JavaScript, or if you need an agent connected to a dozen business apps without building each integration by hand. Its control comes at the cost of n8n's drag-and-drop simplicity and pre-built connector library, and that tradeoff only pays off once you actually need the control.
Which should you pick
Choose LangGraph if you have engineering resources and the agent's value comes from precise, explicit control over multi-step reasoning, retries, and human checkpoints. Choose n8n if you want a working agent today, wired into the apps your business already runs on, without writing integration code from scratch. If you're choosing between code-first agent frameworks specifically rather than against a no-code platform, our CrewAI vs AutoGen comparison covers that narrower matchup. And if n8n's AI Agent node is the direction you're headed, our guide to building an AI agent in n8n walks through a real build, while our n8n vs OpenAI AgentKit comparison covers the other major visual-vs-code-adjacent matchup in this category.
Read the source directly before committing either way: n8n publishes its node documentation and template library, and LangGraph's own documentation covers the framework's graph and state APIs in the technical depth this comparison doesn't need to replicate.
Frequently asked questions
Do I need to know how to code to use LangGraph?
Yes. LangGraph is a Python and JavaScript library, not a visual builder, and you define the graph of nodes, edges, and state transitions in code. There's no drag-and-drop canvas; it's built for developers who want explicit control over how an agent moves through a multi-step task, not a no-code audience.
Can n8n build the same kind of multi-step agent LangGraph can?
To a point. n8n's AI Agent node handles tool-calling and reasoning over several steps, and you can chain conditional logic and loops using its own nodes. What it doesn't give you is LangGraph's explicit state object and graph structure, which matters most for agents that need precise control over retries, branching based on intermediate state, or a human-approval step mid-run. For simpler agent workflows, n8n gets there with far less setup.
Is LangGraph free?
The library itself is free and open source under the MIT license, so you can build and run agents with it at no cost on your own infrastructure. LangGraph Platform, the managed deployment and monitoring layer built by the LangChain team, is a separate paid product billed by usage if you'd rather not host and operate the graph yourself.
Which is better for a non-technical team building its first AI agent?
n8n, by a wide margin. Its visual canvas, pre-built AI Agent node, and 400+ app integrations let someone without a software background assemble a working agent. LangGraph assumes comfort writing and debugging Python or JavaScript; it's the stronger tool once you have that skill on the team, not before.