AI & agents
n8n is a visual workflow-automation tool that connects apps with nodes and can call AI. LangChain and LangGraph are code frameworks for building the AI agents themselves.
A source-available workflow automation platform with a visual node editor and hundreds of integrations.
Triggers, app nodes and AI nodes are wired on a canvas and run on n8n's engine; it suits business automations built by technical and non-technical people.
A code framework with standard interfaces to models, tools and agent loops.
You write the agent in Python or TypeScript with create_agent, tools and middleware, and ship it inside your own service.
A code-level orchestration runtime for stateful, long-running agents.
Graphs of nodes give durable execution, human approvals and precise control over agent steps.
| Aspect | n8n | LangChain | LangGraph |
|---|---|---|---|
| Interface | Visual canvas | Code | Code |
| Main job | Automating workflows between apps | Building agents and LLM features | Orchestrating complex agents |
| Integrations | Built-in app nodes | Model and tool integrations | Uses LangChain or your own code |
| Hosting | n8n cloud or self-hosted | Your service | Your service or LangSmith deployment |
| Best for | Ops and business automations | Developers adding AI features | Production agents needing control |
n8n is a workflow automation platform where you connect triggers, apps and AI steps on a visual canvas and it runs them for you. LangChain is a code library for building LLM applications and agents inside your own software. One automates processes across tools; the other is how developers build AI features.
LangGraph is a code-level runtime for stateful, long-running agents with checkpoints, interrupts for human approval and precise control over each step. n8n is a visual automation tool whose workflows are designed and run on its own engine. LangGraph is for building complex agents; n8n is for automating business workflows quickly.
n8n targets technical operators, automation engineers and developers who want to wire systems together quickly, with code nodes when needed. LangChain and LangGraph target software developers writing Python or TypeScript who need full control over agent logic, testing and deployment.
Yes. n8n has AI nodes, including an AI Agent node with tools, memory and model connections, plus vector store and chain nodes. They suit agents embedded in automations, such as triaging emails or answering from documents. For highly custom orchestration, code frameworks give more control.
When the job is mainly integration and automation: reacting to webhooks, syncing CRMs, sending messages, scheduling reports, with an LLM step or two in the middle, and when non-developers need to read or edit the flow. Built-in app connectors save a lot of code.
When the agent itself is the product: complex branching and loops, many tools, durable execution, human approvals at precise points, custom state, rigorous evaluation in code, and deployment inside your own services with full version control and testing.
n8n ships hundreds of ready nodes for SaaS apps, databases and protocols, with credentials management built in. LangChain integrates models, vector stores, document loaders and tools; connecting to business apps usually means writing tools yourself or using MCP servers.
n8n workflows are JSON stored in n8n; teams export them to Git or use source control features for environments. LangChain and LangGraph are ordinary code, reviewed in pull requests, unit-tested and deployed through CI like the rest of the application.
n8n has built-in triggers: webhooks, schedules, app events, form submissions and more, which start workflows automatically. LangChain and LangGraph have no triggers; your application calls the agent from an API route, queue worker or scheduler.
n8n passes items between nodes during an execution and offers memory nodes for chat agents. LangGraph keeps explicit, typed state with reducers and persists it with checkpointers per thread, so runs can pause, resume and keep memory across sessions.
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