n8n vs LangGraph
n8n added AI agent nodes to its workflow canvas, so teams now ask if that replaces a code framework. It does for a bounded step. It does not once the agent has to hold its own state, get evaluated and fail safely on its own.
The short answer
Use n8n when the agent is one reasoning step inside a business workflow that ops already owns. Use LangGraph when the agent is the product and needs its own state, evals and testing.
The agent node sits inside a normal workflow. A trigger fires, a few nodes run, the agent node makes one judgment call, then the workflow continues. The rest of the automation is nodes and connections you can see and hand to ops. That is the right shape for a support triage step, a lead-routing decision, or a document classifier bolted onto an existing pipeline.
LangGraph earns its setup cost once the agent stops being a step and becomes the thing you are shipping. A support agent has to remember the last ten turns. It has to retry a tool call that failed, then get scored against a test set before every release. That needs a state model and a way to checkpoint and resume it. A workflow canvas was not built to hold that. A graph with typed state was.
Side by side
Drawn from running both in production: n8n on Little Tree's meeting-to-action pipeline, LangGraph on Cruise Search AI's booking agent.
| Dimension | n8n | LangGraph |
|---|---|---|
| What it is | A visual workflow tool with an AI agent node and a code escape hatch | A Python and JavaScript library for building agents as a graph of nodes |
| Who owns the build | Ops or a technical generalist, reading the canvas | An engineer, reading and writing code |
| Where the agent sits | One node inside a larger workflow with triggers and integrations | The whole system. The graph is the agent |
| State across turns | Limited to what the workflow run carries; not built for long conversational memory | A typed state object, checkpointed after every node |
| Recovering from a failed step | Retry the node or the whole execution, depending on how it is wired | Resumes from the last checkpoint by default |
| Testing the agent's decisions | No built-in eval framework; you check outputs by hand or bolt one on | Works with LangSmith and other eval tooling built for graph-based agents |
| Connecting to other systems | Hundreds of pre-built integration nodes, the main reason teams pick it | You write the integration code or call it through a tool |
| Best-fit problem | An agent step embedded in an operational workflow ops already runs | An agent that is the product, with its own state and release process |
- A trigger firesAn email, a form, a schedule, a webhook
- Deterministic steps runNodes and connections you can read on the canvas
- A judgment call is neededThis is where the two approaches diverge
- n8n: one agent node decidesBounded input, one output, the workflow continues
- LangGraph: the agent is the systemIts own state, its own retries, its own eval suite
- Result lands where it needs toA CRM record, a ticket, a booking, a sent message
The crossover is not the model or the prompt. It is whether the agent has state and decisions worth testing on their own.
n8n
Where it wins
- The AI agent node drops a reasoning step into a workflow ops can already read and maintain, with no separate codebase to hand over.
- Hundreds of pre-built nodes mean the integrations around the agent, the CRM write, the Slack message, the calendar check, are often a few clicks, not custom code.
- Self-hosting is real and documented, so a client can run it on their own infrastructure instead of a vendor's cloud.
- A non-engineer can trace what happened in a run by opening the execution log on the canvas.
Where it hurts
- The agent node has no built-in state model for a long conversation or a multi-step plan the agent has to track itself.
- There is no equivalent to a checkpoint-and-resume graph. A failed run is retried or restarted, not resumed mid-reasoning.
- Evaluating whether the agent's decisions are getting better or worse over time is not something the tool gives you.
- The cloud tier's pricing scales with executions, which matters once an agent step runs on every inbound event.
LangGraph
Where it wins
- State is typed and checkpointed after every node, so a crashed run resumes where it left off instead of starting over.
- The graph makes every branch, retry and stopping point something you wrote and can read back months later.
- It pairs with LangSmith and similar tooling built specifically for scoring an agent's decisions across a test set.
- Fits a process that must pause for a human approval, or follow an order that cannot be skipped.
Where it hurts
- There is no visual canvas. Every node, edge and integration is code, so a non-engineer cannot trace or edit a run.
- Connecting to a CRM, an inbox or a calendar means writing that integration, not picking it from a list.
- It takes longer to get a first version running than dropping an agent node into an existing workflow.
- A team new to graph-based state needs ramp-up time before it can extend the graph safely.
How to choose
- Choose n8n if the agent makes one bounded decision inside a workflow that already exists, and ops needs to see and maintain it.
- Choose LangGraph if the agent has to remember prior turns, retry its own tool calls, or get scored on decision quality before every release.
- Choose n8n if the integrations around the decision, not the decision itself, are most of the build.
- Choose LangGraph if a failed run has to resume from where it stopped, not start over.
- Choose neither yet if nobody can describe what the agent should do when it is wrong. That gap breaks either tool the same way.
- Move from n8n to LangGraph when the agent node's output starts driving decisions nobody is checking by hand.
Questions teams ask before committing
01Can I use n8n and LangGraph together?
Yes, and it is a common pattern. n8n can call a LangGraph agent as an HTTP step inside a larger workflow. The workflow keeps its integrations and ops visibility. The hard reasoning happens in a properly stateful graph. The agent does not have to live entirely in one tool.
02Is n8n's AI agent node good enough for production?
For a bounded decision inside a workflow, yes. n8n runs Little Tree's meeting-to-action pipeline in production today. It is not built for an agent that needs to hold state across a long conversation or resume mid-reasoning after a crash.
03Do I need to know how to code to use LangGraph?
Yes. LangGraph is a Python and JavaScript library with no visual canvas. Every node, edge and state transition is written as code, which is the tradeoff for the control and checkpointing it gives you.
04What is the actual crossover point between the two?
State and evaluation. The agent may need to remember what happened three turns ago, or retry a failed tool call from where it stopped. It may need scoring against a test set before every release. Past that point, a workflow canvas stops being enough and a graph-based framework earns its setup cost.
05Does n8n replace the need for a framework like LangGraph?
No. It replaces the need for one in the specific case where the agent is a single bounded step. When the agent becomes the product rather than a step inside a workflow, the two tools solve different problems. They stop competing for the same one.
Related
- how we run n8n in production →Little Tree's meeting-to-action pipeline, and where the cloud tier gets expensive.
- how we run LangGraph in production →Checkpointers, resume behaviour, and when a plain state machine is the better build.
- AI agents vs workflows →The decision that comes before picking a tool: does this even need to be an agent?
- AI agent frameworks, ranked →Where LangGraph sits against the rest of the code-first field.
