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Peer Tool Comparison

n8n vs LangGraph

LangGraph is an MIT-licensed framework for building durable, stateful agents as a graph of nodes in code. n8n is a visual workflow builder with an AI Agent node. Both draw graphs; only one of them makes you write it.

Deploy n8n $7/mo on InstaPods

n8n vs LangGraph: The Verdict

LangGraph exists for agents that are hard: long-running, resumable, human-in-the-loop, with state that has to survive a crash halfway through. If that describes your problem, it is the better tool and n8n will fight you. If it does not - if the agent runs for thirty seconds, calls three tools and finishes - LangGraph is a framework you now maintain for control you are not using. n8n gives you the graph on a canvas, plus the scheduler, the webhook endpoints, the credential store and 400+ integrations, for $7/mo on a server someone else patches. The honest split: LangGraph when durability and control of the loop are the requirement; n8n when integration breadth and someone-else-can-edit-it are the requirement. Note also that LangGraph is free to import but its managed runtime is now sold as LangSmith Deployment, metered in compute units rather than a flat fee, which makes its cost harder to predict than a $7 pod.

Last updated: 2026-09-02

Quick Comparison

Feature comparison: n8n vs LangGraph
Feature LangGraph n8n
What It Is Agent orchestration framework (library) Self-hosted application with a UI
License MIT (40,923 GitHub stars) Sustainable Use License (199,633 stars)
How You Build Define a graph in Python or JS Drag nodes on a canvas
Durable State Across Restarts Yes - the core feature Execution resumes from stored data, not arbitrary checkpoints
Human-in-the-Loop First-class (interrupt and resume) Via Wait node and approval steps
Managed Runtime LangSmith Deployment, metered in compute units Any server - $7/mo on InstaPods
Cost Model Per seat + metered LCU/LSU usage Flat monthly, unmetered executions
Integrations Whatever you write 400+ built-in nodes
Non-Engineers Can Edit No Yes
Self-Hostable Free Yes - the framework is MIT Yes - community edition

What LangGraph Leaves You to Build

Trade-offs to weigh before committing.

The Runtime Is a Separate Purchase

The LangGraph framework is MIT and free. Running it as a managed service means LangSmith Deployment, which is metered: runtime compute at 0.045 LCU per vCPU-hour, runtime memory at 0.006 LCU per GiB-hour, database compute at 0.177 LSU per vCPU-hour, with LCUs at $1.50 and LSUs at $1.00. Predicting next month's bill takes a spreadsheet.

Every Integration Is Yours

LangGraph orchestrates. It does not ship a Slack node, a Google Sheets node or an HTTP credential store. Anything your agent touches, you write and maintain.

Deep Control You May Not Need

Checkpointing, interrupts, custom reducers and state channels are powerful and they are also concepts your team now has to hold. For an agent that calls three tools and returns, that is overhead against no benefit.

Self-Hosting the Platform Is Enterprise-Only

The framework self-hosts freely. The LangSmith platform around it - traces, deployment, evaluation - is cloud-only on Developer and Plus. Self-hosted and hybrid appear only on Enterprise, at custom pricing.

What n8n Hands You Instead

What you get when you pick this stack.

A Flat Number You Can Predict

n8n on InstaPods is $7/mo on the Build plan - 2 vCPU, 2 GB RAM, 25 GB SSD - with executions unmetered. The bill does not move when the agent has a busy week.

The Graph Is Already Visual

LangGraph's core idea is that an agent is a graph. n8n shows you the graph and lets you edit it without a deploy, which is the same idea arrived at from the other direction.

Integrations, Credentials and Scheduling Included

400+ nodes, encrypted credential storage, cron and webhook triggers, and execution history are all part of n8n rather than things you assemble.

It Can Call LangGraph

If one part of the job genuinely needs durable checkpointing, host that as a service and call it from n8n over HTTP or MCP. Choosing n8n for orchestration does not rule LangGraph out.

Real Cost Comparison

One production agent, running on a schedule and via webhook, for a small team.

LangGraph
  • LangGraph framework: free (MIT)
  • LangSmith Plus for deployment and traces: $39/seat/mo
  • Metered runtime: LCU at $1.50, LSU at $1.00, billed per second
  • Integrations and credential handling: you build them
  • 1 free Serverless (Small) deployment included on Plus
$39/seat/mo plus usage you have to model
n8n on InstaPods
  • InstaPods Build plan: $7/mo (2 vCPU, 2 GB, 25 GB)
  • n8n community edition: free, executions unmetered
  • Managed PostgreSQL on the same pod: included
  • Execution history and credentials: included
  • Daily backups, 5 kept: included
$7/mo flat, unlimited seats

Frequently Asked Questions

Is LangGraph better than n8n for AI agents?

For agents that run a long time, need to survive a restart mid-run, or pause for human approval and resume days later, yes - that is what LangGraph was built for and n8n approximates it. For agents that call a few tools and finish, n8n gets you there faster and hands you 400+ integrations, a scheduler and a credential store you would otherwise write.

Is LangGraph free?

The framework is MIT-licensed and free to self-host. The managed platform around it is not. LangChain now sells that as LangSmith Deployment rather than a separate LangGraph Platform product: Developer $0 per seat, Plus $39 per seat per month, Enterprise custom, with runtime metered in LangChain Compute Units at $1.50 and Storage Units at $1.00.

Can n8n do stateful agents?

Partly. n8n stores execution data and has memory sub-nodes - Postgres and Redis chat memory run as one-click services on the same InstaPods pod, and MongoDB is available as an n8n option if you host the database elsewhere - so conversation state persists. What it does not give you is LangGraph's arbitrary checkpointing, where you resume a run from any node after a crash. If that is your requirement, LangGraph is the right tool.

Can I run LangGraph and n8n together?

Yes. Host the LangGraph agent as a service and call it from n8n with the HTTP Request node, or expose it as an MCP server and attach it to an n8n AI Agent via the MCP Client Tool sub-node. n8n handles triggers, scheduling and integrations; LangGraph handles the durable part.

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