n8n is one of the most powerful open-source workflow automation platforms available, combining a drag-and-drop visual builder with custom JavaScript or Python nodes. It ships with 400+ pre-built integrations and native support for LLM chains, RAG pipelines, and AI agents. With nearly 194,000 GitHub stars it is the dominant self-hostable alternative to Zapier and Make.
n8n is a workflow automation platform you can run on your own server or use through its hosted cloud. You build automations on a node canvas: a trigger node starts a run, and each node after it moves data, reshapes it, or calls a service. The project ships several hundred prebuilt integration nodes covering common SaaS APIs, databases, queues and plain HTTP endpoints. When no node fits, you drop in a code node and write JavaScript or Python against the data passing through, so you are never stuck waiting for an integration to exist. It also includes AI-native pieces, which means model calls, retrieval steps and agent loops live on the same canvas as the rest of your logic instead of in a separate product. Every run leaves an execution record you can open node by node to see the exact payload at each step, which is what makes debugging a broken automation tractable. The code is published under a fair-code license, and Docker self-hosting is the usual route for teams that want credentials and customer data staying on infrastructure they control.
Reach for n8n when an automation has outgrown a linear if-this-then-that rule and needs branching, loops, retries, or a code step in the middle. It fits the case where you are gluing together an internal stack: pull rows from a database, enrich them with an API call, run them past a model, write the result back and post a notification. It is also the obvious pick when the data cannot leave your network, because self-hosting is a first-class path rather than an enterprise upsell. Developers who want version-controlled, inspectable automation logic get more out of it than someone who only needs two apps connected. If your team is non-technical and the workflows stay simple, a hosted point-and-click service will involve less operational work.
Langflow is also a visual builder, but its canvas is aimed at composing LLM chains and agent graphs rather than at operational plumbing across hundreds of business apps, so Langflow suits prototyping a model pipeline while n8n suits running the whole process around it. Trigger.dev comes at the same problem from the other side: you write background jobs as code in your own repository and get retries, queues and observability, which fits teams who would rather review a pull request than a canvas. Maxun is narrower again, focused on turning websites into structured data without code, and pairs with n8n as a source rather than competing with it. Choose n8n when the breadth of connectors and the ability to self-host matter most.
Compare with: Langflow, Trigger.dev, Maxun
The catalog records n8n as freemium. In practice that means the source is open under a fair-code license and self-hosting is free of a vendor charge, while the company sells a managed cloud offering and paid tiers for heavier or enterprise use. If you self-host, your real costs are the server or container platform you run it on, storage for execution history, and whatever the third-party APIs and model providers in your workflows charge. Check the current license terms before embedding it in a commercial product, since fair-code is not the same as a standard open-source license.
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Use a Set node early in a workflow to normalize/rename fields from different upstream sources — makes every downstream expression easier to write and debug.
Use pinned data to iterate on a single node without re-triggering the whole workflow (and any real API calls) each time.
Reference $json and $node["NodeName"].json explicitly in expressions rather than relying on auto-mapping — more reliable across workflow versions.
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