n8n vs Flowise vs Langflow: which visual tool for your AI workflows?

Search for "flowise vs n8n" or "langflow vs n8n" and you'll mostly find screenshots that look alike: three canvases where you connect boxes, three self-hostable projects, three public repositories. The resemblance ends quickly, though. Flowise and Langflow are specialized LLM application builders, born around the LangChain ecosystem; n8n is a general-purpose automation platform that has built AI into its engine. Comparing them feature by feature without seeing that difference in nature is like comparing a prototyping workshop to a production line. Here's the honest comparison, criterion by criterion, plus the combined pattern that often means you don't have to choose at all.

Three visual canvases, two different jobs

Flowise and Langflow start from the same observation: assembling an LLM application — a chatbot, a RAG pipeline, an agent with tools — in code involves repetitive plumbing. Both offer a visual editor where you wire together components from the LangChain universe: models, prompts, parsers, retrievers, vector databases, memory. You drag, connect, test in a built-in chat, then expose the result through an API or an embeddable widget. The scope is deliberate: the LLM application, from prompt to answer.

n8n comes from the other shore. It's first and foremost a general automation tool — the one we pit against its SaaS rivals in n8n vs Make vs Zapier — with hundreds of business integrations: CRMs, messaging, e-commerce, databases, spreadsheets, calendars. AI arrived as a native layer: an AI Agent node built on LangChain, interchangeable chat model nodes, vector stores, conversation memory building blocks. The scope is the mirror image: the end-to-end business process, in which the LLM call is just one step.

That difference in origin explains almost everything that follows.

Functional scope: where n8n pulls ahead

To automate a complete process, four families of features matter as much as the quality of the LLM chain itself.

Triggers. n8n starts a workflow on a webhook, a cron schedule, an incoming email, a dropped file, a CRM event, a Slack or Telegram message — the list goes on. Flowise and Langflow are essentially designed to be called: a request hits their API or chat interface, the chain runs, the answer goes back. As soon as the need is "when X happens somewhere in my systems, do Y", n8n's execution model is the right one.

SaaS integrations. Fetching a HubSpot deal, enriching a Google Sheets row, creating an invoice, posting to a channel: in n8n, each of these is a ready-made node with managed authentication. In Flowise or Langflow, these actions go through custom tools, hand-written HTTP calls, or code — doable, but the very plumbing these tools set out to eliminate on the LLM side reappears on the business side.

Error handling. A production pipeline fails eventually: API downtime, quota exceeded, malformed response. n8n offers per-node configurable retries, error branches, dedicated Error Workflows, and a detailed execution history where you can replay a failed run with its original data. It's one of the things you discover when getting started with n8n's AI nodes: the LLM call is treated like any other fallible step, not a magic box.

Queues and scaling. n8n ships a Redis-based queue mode with horizontal workers, covered in our queue mode guide — essential when hundreds of AI executions fire in parallel. Flowise and Langflow can be deployed as multiple instances behind a load balancer like any web application, but workflow-level load orchestration (priorities, resumption, backpressure) is not what they're built for.

The verdict on this criterion: for end-to-end automation, n8n's advantage is clear. For pure LLM app prototyping, it flips — which brings us to the next point.

LLM prototyping: where Flowise and Langflow shine

Assembling an experimental RAG in Flowise or Langflow takes a single session: plug in a document loader, a splitter, a vector database, a model, iterate on the prompt in the test chat, compare two retrievers by duplicating a branch. API exposure is instant, and so is the embeddable chat widget. For a data scientist or developer exploring LLM chain architectures — reranking, model routing, multi-tool agents — the density of LangChain components available visually exceeds what n8n exposes natively.

n8n can build a complete RAG, as our RAG with Supabase guide shows, and can orchestrate multiple cooperating agents. But its canvas remains that of an automation tool: exotic or very recent LLM components land more slowly than in projects whose sole reason for existing is LLM apps, and rapid iteration on conversational quality alone is slightly less fluid.

Licensing: open source "to varying degrees"

All three projects have public code, but not the same contract.

• n8n is distributed as fair-code under the Sustainable Use License: internal use and self-hosting are free, including for companies, but commercializing n8n itself as a service is prohibited. If you're automating your own processes, nothing changes; if you're reselling an automation platform, it does.
• Flowise and Langflow are published under more permissive open source licenses, in the tradition of LangChain-ecosystem projects. Exact terms can evolve with versions and each vendor's commercial offerings: if licensing is a decision criterion (commercial product, resale, compliance), check the text in force at the moment you choose rather than trusting a frozen comparison.

For the vast majority of teams self-hosting for their own use, this criterion is a practical tie.

Production maturity and learning curve

A useful frame for getting past the marketing: the study by Sahay, Indamutsa, Di Ruscio and Pierantonio, Supporting the understanding and comparison of low-code development platforms (Euromicro SEAA, 2020 — see it on Google Scholar), proposes an analysis grid for low-code platforms along dimensions such as interoperability, extensibility and deployment capabilities. Applied to our trio, it captures the difference well: on interoperability (connecting heterogeneous systems) and deployment (persisted executions, recovery, scaling), n8n ticks more boxes; on extensibility within the LLM domain (adding an exotic chain component), Flowise and Langflow are more direct.

Concretely, on the production side: n8n persists every execution with its intermediate data, lets you replay, alert, and audit who changed what. That's the tooling you expect from an engine that has been running business processes for years. Flowise and Langflow are improving (traces, chain observability, integrations with LLM monitoring tools), but their production track record is shorter and centered on the conversational application, not the process.

On the learning curve, the ranking depends on where you start. A developer who knows LangChain is productive in Flowise or Langflow within an hour: the concepts are familiar, just visual. An automation-minded profile will find n8n more natural — trigger, steps, branches — and pick up the LLM notions gradually. The LangChain vocabulary (chains, retrievers, output parsers) is the real entry step of Flowise and Langflow for a newcomer.

The combined pattern: prototype here, orchestrate there

The choice isn't always exclusive, and the most effective pattern we see in the field is hybrid:

1. Prototype the LLM chain in Flowise or Langflow: iterate fast on the prompt, the chunking, the retriever, with immediate conversational feedback.
2. Expose the validated chain through its API endpoint.
3. Call it from n8n with an HTTP Request node, inside a workflow that handles everything else: the business trigger, data preparation, retries, writing results to the CRM, notifications, logging.

Each tool then does what it was designed for. The simplifying variant is just as legitimate: when n8n's AI nodes cover the need — and for a document RAG or an agent with standard tools, they very often do — doing everything in n8n removes one component to deploy, back up and update. That's the approach behind our RAG Assistant Pack (€119): a complete production RAG, from document ingestion to the chat API, ready to import into n8n with no extra external dependency.

Comparison table

Criterion	n8n	Flowise	Langflow
Nature	General-purpose automation platform with built-in AI	Visual LLM application builder	Visual LLM application builder
Triggers (cron, webhooks, SaaS events)	Very rich	Limited (API/chat as input)	Limited (API/chat as input)
Native business integrations	Hundreds	Few (custom tools, HTTP)	Few (custom tools, HTTP)
LLM/LangChain components	Good (AI Agent, vector stores, memory)	Very rich	Very rich
Rapid LLM app prototyping	Decent	Excellent	Excellent
Error handling, retries, execution audit	Excellent	Basic	Basic
Orchestrated scaling (queue, workers)	Yes (Redis queue mode)	Standard instance replication	Standard instance replication
License	Fair-code (Sustainable Use License)	Permissive open source	Permissive open source
Learning curve	Medium (automation logic)	Low if you know LangChain	Low if you know LangChain

Which tool for which profile

• You automate business processes in which AI is one step (email triage, lead qualification, document generation, support): n8n, no hesitation. It's the only one of the three built for end to end.
• You're a developer or data scientist iterating on LLM architectures (comparing retrievers, testing multi-tool agents): Flowise or Langflow, for prototyping speed. Between the two, try each interface for an hour — the preference is largely ergonomic.
• You need to ship a RAG chatbot into an existing IT landscape: n8n alone if its nodes suffice, or the combined pattern if your LLM chain goes off the beaten path.
• You're leaving a SaaS tool and want automation and AI under one roof: n8n covers both, as our comparison with Make and Zapier details.

In the end, the right question isn't "which is best?" but "is my problem an LLM application, or a process that contains an LLM?". In the first case, Flowise and Langflow will save you days of prototyping. In the second — by far the more common one in small and mid-sized companies — n8n is the backbone, and the other two make excellent side workshops.