Flowise

Drag-and-drop builder for LangChain style LLM flows and agents

AI Agents & AutomationFree planOverseasβ˜…β˜…β˜…β˜…β˜† 4.0

What is Flowise?

Flowise is an open-source tool that puts a visual canvas over the components commonly used to build LLM applications: prompt templates, model nodes, retrievers, vector stores, tools and agents. You drag them onto a canvas, connect them, and get a working chatflow or agentflow without writing the glue code. It runs locally or in Docker, supports many model providers, and exposes each flow through an API or an embeddable chat widget. For teams already thinking in LangChain terms, it is the quickest way to see a pipeline working end to end.

Last updated: 2026-09-20. This site only provides an index; for exact features, pricing, and licensing, see the official website.

Key features

  • Drag-and-drop canvas over common LLM building blocks
  • Chatflow and agentflow modes for different behaviours
  • Vector store and retriever nodes for document Q&A
  • Embeddable web chat widget and API endpoints per flow
  • Provider-agnostic model nodes including local models
  • Self-hosted via Docker or npm with an exportable flow format

Pros & cons

Strengths

  • Fastest way to prototype a retrieval or agent flow visually
  • Open source and self-hostable with no per-flow fees
  • Flows export as JSON, so they can be version controlled

Watch out for

  • Complex logic outgrows the canvas fairly quickly
  • Documentation lags behind the fast release pace
  • Production hardening is left largely to the user

Best for & use cases

prototyping rag apps, internal chat assistants, learning llm pipelines and demos

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FAQ

Is Flowise a programming tool?

It is code-optional. Most flows are assembled on the canvas, but understanding what the underlying components do - retrievers, embeddings, chains - is what separates a working flow from a confusing one.

Can I embed the result?

Yes. Each flow can be exposed as an API endpoint or embedded as a chat widget, which makes it practical for internal tools and quick customer-facing prototypes.

Is it ready for production?

For internal or moderate-traffic use, often yes. High-traffic deployments need attention to scaling, secrets, monitoring and error handling that the canvas does not provide on its own.