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LangChain / LangGraph vs n8n
A plain-English comparison to help you choose between them.
LangChain is the framework you code and n8n the engine you draw, and the maintenance burden each carries is what should actually decide between them. Pick LangChain when the system justifies being real software: LangGraph's stateful graphs with checkpointing, durability and human-in-the-loop control, the largest integration ecosystem in the space, and tracing that makes agent behaviour debuggable. Pick n8n when flows should be drawn and owned operationally: visual workflows with first-class AI steps, real code where nodes run out, and self-hosting that keeps data residency and unit cost under your control.
Side by side
- Summary
LangChain is the broadest framework for building LLM applications, and LangGraph is its production heart: stateful, controllable agents expressed as graphs, with the checkpointing, human-in-the-loop and durability that real deployments demand.
- Best for
- Complex stateful agents built as graphs
- Checkpointing, durability and human-in-the-loop
- One of the largest integration ecosystems in the space
- Cost
- Free
- Ease
- Openness
- Runs privately (self-hostable)
- Data
- Data goes wherever your code sends it; the framework imposes no posture of its own.
- Summary
n8n is the engineer's automation platform: open source and self-hostable, with execution-based pricing that keeps high-volume workflows economical, visual nodes that accept real code when logic demands it, and first-class support for AI and agentic flows.
- Best for
- Self-hosted automation with data residency
- Execution-based pricing at high volume
- Visual flows with real code where needed
- Cost
- Freemium (Free tier + paid plans)
- Ease
- Openness
- Runs privately (self-hostable)
- Data
- Execution-based pricing (whole workflow = one execution) is usually cheaper than per-task, but AI Workflow Builder credits (50 Starter / 150 Pro) and any LLM API calls you wire in are metered separately, and Business overage is roughly €4,000 per 300,000 extra executions. The sticker price is not the whole bill. Self-hosting shifts cost to your own infrastructure/maintenance.
Common questions
What does each cost to maintain over a year?
LangChain's bill is engineering attention: the abstractions move, and keeping up with the framework's evolution is a standing cost of building on it, on top of owning your own runtime. n8n's bill is operations: someone runs the server, applies updates, guards uptime and secures a platform holding credentials to every connected system. Choose the burden your team can actually carry.
Which handles AI-heavy automation better?
For most automation that includes AI, n8n suffices and ships faster: agentic flows are first-class, and the visual estate stays comprehensible to the operators who own it. LangChain earns its complexity when state, control and durability are actual requirements: long-running agents, human-supervised checkpoints, behaviour that must be traced and evaluated. Below that threshold, its own reputation concedes the boilerplate tax.
Do the economics differ at scale?
Both start free and open source, so the sticker is not the story. n8n's hosted plans price by executions, and self-hosting keeps high-volume workflows economical with hardware as the cost. LangChain systems pay in model usage and engineering time, with the platform tooling around tracing and evaluation as the optional commercial layer. At scale, both reward owning your infrastructure; they differ in who does the owning.
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Tool facts last checked July 2026