n8n vs LangGraph: Which to Choose?

n8n and LangGraph are both workflow tools, but they solve different problems: n8n is a general-purpose automation platform for connecting apps and services, while LangGraph is a specialized library for building stateful, agentic AI applications. This article explains the core differences, when to use each, and how to decide based on your project’s needs.

Background: Where Each Tool Comes From

n8n is a fair-code workflow automation tool launched in 2019. It lets users connect over 400 apps (Google Sheets, Slack, databases, APIs) using a visual editor, with options for self-hosting or cloud. It’s designed for IT, operations, and business teams to automate repetitive tasks without writing code from scratch.

LangGraph is an open-source Python library released by the LangChain team in 2024. It’s built specifically for creating AI agents—systems that use large language models (LLMs) to reason, call tools, and maintain state across multiple steps. It’s aimed at developers building complex conversational or decision-making applications.

How They Work: Core Architecture

n8n uses a node-based editor where each node performs an action (e.g., fetch data, send email, run code). Workflows are event-driven: a trigger starts the flow, and data passes through nodes in a linear or branched path. n8n handles retries, error handling, and scheduling natively.

LangGraph models workflows as a graph with nodes (functions) and edges (transitions). The key difference is that LangGraph supports cycles—you can loop back to a previous step based on conditions, which is essential for agents that need to call tools, observe results, and decide next actions. It also maintains a state object that persists across steps, allowing the agent to remember context.

Key Differences at a Glance

Primary use case: n8n = API orchestration and business automation; LangGraph = AI agent development.
Interface: n8n has a visual drag-and-drop UI; LangGraph is code-first (Python).
State management: n8n passes data along a linear path; LangGraph has explicit state that can be mutated and read at any point.
Looping and branching: n8n supports loops but they’re less flexible; LangGraph allows arbitrary cycles and conditional edges.
Hosting: n8n can be self-hosted or cloud; LangGraph runs in your own Python environment.
Integration with LLMs: n8n has AI nodes but they’re for single calls; LangGraph is built for multi-step agent reasoning.

When to Use n8n

Choose n8n when your task is about moving data between services or automating a business process. For example:

Syncing new CRM leads to a marketing email list
Posting new blog content to social media channels
Sending a Slack notification when a form is submitted
Polling an API and writing results to a database

n8n shines when you need many integrations out-of-the-box, want non-developers to maintain workflows, or need quick deployment. It also has a built-in queue mode for high-volume jobs.

When to Use LangGraph

LangGraph is the right choice when your application requires an AI agent that can reason and act over multiple steps. Examples:

A customer support bot that can look up order status, escalate issues, and remember conversation context
A research assistant that searches the web, reads documents, and synthesizes answers
A coding agent that edits files, runs tests, and iterates based on errors

LangGraph gives you fine-grained control over the agent’s loop, including human-in-the-loop checkpoints, time travel (rewind to a previous state), and the ability to persist state across sessions.

Can You Combine Them?

Yes, and many teams do. A common pattern is to use n8n for the surrounding automation (triggering a job, sending notifications, storing results) and call a LangGraph agent as a node when deep AI reasoning is needed. For instance, an n8n workflow might listen for an email, extract the request, invoke a LangGraph agent to draft a response, then send the reply via n8n.

This hybrid approach lets you leverage n8n’s integrations and LangGraph’s agent capabilities without forcing either tool to do what it’s not designed for.

Cost and Complexity

n8n has a free self-hosted community edition, with paid cloud plans starting at a monthly fee. The visual editor reduces developer time but can become cluttered for very complex workflows.

LangGraph is free and open-source (MIT license), but you pay with development effort—you need to write Python code and manage your own infrastructure. You’ll also incur LLM API costs when running agents.

Complexity-wise, n8n is easier for non-programmers; LangGraph requires solid Python skills and an understanding of agent architectures.

Common Pitfalls and Misconceptions

Misconception: “n8n can build AI agents.” While n8n has AI nodes that can call an LLM, they are stateless—they don’t maintain context across multiple calls. For a true agent that loops and uses tools, you’d need to build that logic in code or use LangGraph.

Misconception: “LangGraph is just for chatbots.” It’s broader—any stateful, multi-step AI process, including data extraction pipelines or automated reasoning systems.

Pitfall in n8n: Trying to implement complex conditional loops can lead to spaghetti diagrams. If you find yourself adding many branches, consider moving that logic to a code node or a separate service.

Pitfall in LangGraph: Over-engineering the graph. Start with a simple linear flow and add cycles only when needed. Also, remember to handle errors gracefully—an agent that throws an exception can crash the whole graph.

How to Choose: A Practical Checklist

What’s the core task? If it’s connecting apps and automating data flow → n8n. If it’s building an AI agent that reasons → LangGraph.
Who will maintain it? Non-developers → n8n. Developers → either, but LangGraph is code-only.
Do you need persistent state? If yes (e.g., multi-turn conversations) → LangGraph. If no → n8n.
Do you need loops with conditions? If yes, and they’re complex → LangGraph. If simple, n8n’s loop nodes may suffice.
What’s your hosting preference? n8n offers self-hosted or cloud; LangGraph requires your own Python environment.

Frequently Asked Questions

Can n8n call a LangGraph agent?

Yes. You can expose a LangGraph agent as an API endpoint (using FastAPI or similar) and call it from an n8n HTTP Request node. This is a common integration pattern.

Is LangGraph only for Python?

Currently, LangGraph is primarily a Python library. There is also a JavaScript version (LangGraph.js), but it’s less mature. If you’re a JavaScript developer, you can still use it, but expect more limited examples.

Does n8n have AI capabilities?

Yes, n8n has AI nodes that can send prompts to LLMs and parse responses. However, these are single-turn calls—they don’t maintain a conversation history or loop. For simple AI tasks like classification or summarization, n8n is sufficient.

Which is better for a small business?

For most small businesses, n8n is the better fit because it’s easier to use, has pre-built integrations, and doesn’t require a developer to maintain. LangGraph would only be necessary if you’re building a custom AI product.

Can I use LangGraph without LangChain?

Yes, LangGraph is a separate library, but it’s designed to work well with LangChain. You can use it with plain LLM API calls if you prefer, but you’ll lose some convenience.

What are the learning curves?

n8n has a shallow learning curve—you can build a simple workflow in minutes. LangGraph has a steeper curve because it requires understanding of graph theory, state management, and agent design patterns.

Method: the standard formula for this calculation, rounded as each result states. See how the calculators work or report an error.

More cards in this drawer

The whole Numbers and Powers drawer →