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Flowise

by FlowiseAI

Drag-and-drop builder for LLM agents and chains.

Agents & Automation Free, open-source; paid Flowise Cloud

What is Flowise?

Flowise is an open-source, low-code tool for visually building LLM chains and agents by dragging and connecting nodes, aimed at people who want LangChain-style capability without writing the underlying code by hand.

Key features

  • Drag-and-drop canvas for building LLM chains and agents
  • Pre-built nodes for common patterns like RAG and tool-calling
  • Self-hostable, or available as a managed cloud service
  • API and embeddable chat widget generated from any flow
  • Marketplace of ready-made flow templates

How to get started

  1. Self-host Flowise via Docker, or sign up for Flowise Cloud
  2. Start a new flow and drag in the nodes you need
  3. Connect nodes to define how data and prompts move through the chain
  4. Deploy as an API endpoint or embeddable chat widget

Use cases

  • Building a RAG chatbot without writing LangChain code directly
  • Prototyping agent workflows visually before a custom build
  • Teams wanting a self-hosted, low-code AI app builder
  • Embedding a custom chat widget into an existing website

Flowise vs. alternatives

ToolHow it compares
FlowiseDrag-and-drop builder for LLM agents and chains.
Difymore complete app-building platform overall
LangGraphcode-based control instead of visual flows

FAQ

Is Flowise free to use?

Flowise's plans: Free, open-source; paid Flowise Cloud. Pricing and free-tier limits change over time, so check the official site above for the latest details.

What is Flowise used for?

Drag-and-drop builder for LLM agents and chains.

Who is Flowise best for?

Flowise is a good fit for building a RAG chatbot without writing LangChain code directly, or prototyping agent workflows visually before a custom build.

What are the alternatives to Flowise?

Commonly compared alternatives include Dify and LangGraph, see the comparison above for how they differ.

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Last reviewed: 2026-08 · Reviewed by AIKetra editors · How we evaluate tools