n8n vs Langflow: Which Should You Build On?

Updated 2026-10-10 · 1085 words

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Quick take: if the deliverable is an LLM app, an agent, or a RAG pipeline and that's the whole product, Langflow gets you there with purpose-built components and a model that tracks LangChain's own API closely, especially useful if your team already writes Python. If that AI capability is one step inside a larger automation that also needs to update a CRM, post to Slack, or read a spreadsheet, n8n's AI Agent node plus its 400+ integrations make it the stronger foundation. We self-hosted both via Docker and built the same document-grounded support agent and a simple tool-calling agent to see where each one actually held up.

What n8n and Langflow actually are

n8n is a node-based workflow automation platform that predates the current AI-agent wave by several years. You wire together triggers, app integrations, and logic branches on a canvas, and since its AI Agent node shipped, LLM-powered reasoning is one more node type among hundreds, not the platform's organizing idea. It runs under a fair-code Sustainable Use License: free to self-host and modify for internal use, with a restriction on reselling it as a competing hosted service.

Langflow is a visual builder for LangChain: you drag components, loaders, prompts, LLMs, retrievers, agent tools, onto a canvas and wire them into a runnable flow that Langflow serves as an API. It's Python-based, fully MIT-licensed, and close enough to LangChain's own object model that reading a Langflow canvas feels like reading LangChain's Python docs with extra wiring made visible. Our Langflow vs Flowise comparison covers how it stacks up against the closest Node.js-native alternative if Python isn't a requirement for your team.

Comparison table

Building the same agent in both

We built a document-grounded support agent, ingest a handful of PDFs, answer questions with citations, escalate anything it can't answer, in both tools. In Langflow, the pipeline came together in about 25 minutes: a document loader, a splitter, an embeddings component, and a retrieval chain, each one a purpose-built block with sensible defaults already wired for the LangChain pattern underneath.

The same agent in n8n took closer to an hour. Its AI Agent node handles the reasoning and tool-calling cleanly, but the retrieval pipeline itself, chunking, embeddings, a Vector Store node pointed at Supabase, needed more manual assembly, and we ended up writing a short code step to handle chunking the way Langflow's dedicated splitter component did automatically. Where n8n pulled ahead was the escalation step: routing an unanswered question to a Slack channel was a single native node, while the equivalent in Langflow meant calling a webhook ourselves, since Langflow has no first-class Slack integration at all.

Self-hosting and licensing

Both spin up with a single docker-compose up in minutes. Langflow's default container uses SQLite for quick local testing; moving to Postgres for anything beyond one developer's laptop means adding a few environment variables rather than getting it for free out of the box. n8n's self-hosted setup follows a similar pattern, and our guide to running n8n with Docker covers that setup in more depth than this comparison has room for.

Licensing is where the gap is real, not cosmetic. Langflow's MIT license puts no restriction on how you use, modify, or resell it, including building and selling a hosted product on top of it. n8n's Sustainable Use License is free for the same self-hosting and internal use, but explicitly prohibits offering it as a competing hosted automation service, worth knowing if white-labeling either tool is ever on the table.

Integrations and ecosystem

This is the clearest dividing line between the two. n8n's 400+ built-in nodes mean almost any everyday business app, a CRM, a spreadsheet, a messaging platform, is a drag-and-drop connection away. Langflow's component library is deep on the AI side, LLM providers, vector stores, memory modules, agent tools, but noticeably thin the moment a workflow needs to reach into a specific third-party business app that isn't LLM-adjacent.

If the job is mostly reasoning over your own content and exposing that as an app or API, that gap barely matters, and Langflow's head start on the AI-specific plumbing is worth more than n8n's broader catalog. If the agent needs to actively read from or write to a dozen different business systems as part of its logic, n8n's node library is the faster, less improvised path, and it's the gap Langflow can't close without a lot of custom HTTP calls.

When NOT to use n8n

Skip n8n if the entire deliverable is a standalone LLM app, agent, or RAG pipeline with no need to touch outside business systems. You'll spend real setup time wiring vector stores and embeddings from general-purpose pieces that Langflow already gives you as dedicated, pre-configured components.

When NOT to use Langflow

Skip Langflow if you want a managed, pay-for-hosting option; there's no official tier to fall back on since DataStax shut its Astra hosting down, so every deployment is your own infrastructure responsibility. It's also the wrong fit if the AI feature needs to plug into several business apps as part of a bigger process, since you'll be writing custom integrations for territory n8n already covers with a node.

Which should you pick?

Choose Langflow if your team is Python-first, comfortable self-hosting indefinitely, and the deliverable is the LLM app or agent itself. Choose n8n if that AI capability is one feature inside a broader automation that also needs to move data between CRMs, spreadsheets, or messaging tools; our guide to building an AI agent in n8n covers that build end to end.

Learn more directly from the source: n8n publishes a large template library and full docs, and Langflow covers its component model and self-hosting setup in its own documentation, both worth reading before you commit infrastructure to either one. If neither is quite the right shape for your use case, our n8n vs Dify comparison and our best no-code AI agent builders roundup widen the field further.

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