Wrong question first: these aren’t competing for the same job. Langflow builds AI flows — chains of LLM calls, retrieval, agents. n8n automates everything else — APIs, webhooks, databases, the operational glue between SaaS tools. Pick based on what you’re actually connecting, not which one has more stars on GitHub this month.

What each one is actually for

Langflow is a visual builder for LLM applications, built on top of LangChain concepts. Its nodes are things like prompt templates, vector stores, retrievers, memory, and agent loops. It exists to let you compose a RAG pipeline or an agent without hand-writing LangChain’s Python glue every time.

n8n is a general-purpose workflow automation tool. Its nodes are things like “HTTP Request,” “Postgres,” “Slack,” “Google Sheets,” and a few hundred other app integrations. It exists to move data between systems and trigger actions based on events — a form submission creates a CRM record, a webhook fires an approval flow, a schedule pulls a report and emails it.

Where the overlap actually is

Both tools can call an LLM API. Both have some flavor of “if/else” branching. That’s about where the overlap ends. n8n added AI nodes (OpenAI, Anthropic, vector store integrations) because customers wanted to bolt one LLM call onto an existing business automation — “summarize this support ticket, then create a Zendesk ticket.” That’s a fundamentally different job than building a multi-step reasoning agent with tool use and long-term memory, which is Langflow’s actual design center.

Trying to build a serious RAG pipeline in n8n means chaining generic HTTP Request nodes and Code nodes to reimplement what Langflow gives you as first-class nodes: chunking strategies, embedding model swaps, retriever configuration. It’s possible. It’s also reinventing a wheel Langflow already built well.

Trying to build a “when a GitHub issue is created, post to Slack and update a Notion database” flow in Langflow means fighting a tool that has no native GitHub, Slack, or Notion nodes, because that’s not what it was built to do.

Decision table

Your task	Use
RAG pipeline over your own documents	Langflow
Multi-step agent with tool calls	Langflow
Sync data between SaaS apps	n8n
Webhook-triggered business process	n8n
Scheduled report generation	n8n
Chatbot with retrieval + memory	Langflow
“Summarize X, then do Y in another system”	n8n calling an LLM node, or n8n calling a Langflow flow as a step

The setup that actually works: both, together

The strongest pattern isn’t picking one — it’s using n8n as the orchestration layer that triggers business logic, and Langflow as the AI reasoning layer it calls into for the parts that need it. Langflow exposes a flow as an API endpoint; n8n calls it with an HTTP Request node like any other API, then takes the response and does something operational with it — write to a database, notify a channel, update a ticket.

This division keeps each tool doing what it’s actually good at. You’re not fighting n8n to build a vector retriever, and you’re not fighting Langflow to build a Slack integration.

What tips the decision if you can only run one

If your automations rarely touch an LLM — mostly moving data between tools and reacting to events — run n8n and use its built-in AI nodes for the occasional LLM call. If your core product is an AI feature — a support bot, a document Q&A tool, an agent doing research — run Langflow, and if you need occasional non-AI automation around it, n8n’s free tier or a lighter tool covers that without requiring you to run two heavyweight self-hosted apps for one project. Most teams doing serious work in both categories end up running both, because neither one is a good substitute for the other’s core job.

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