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flowiseai/flowise

🔥 Hot★ 52,918TypeScriptAudience · vibe coderComplexity · 3/5ActiveLicenseSetup · moderate

TLDR

Visual drag-and-drop builder for AI applications and chatbots. Connect AI models, databases, and tools without coding.

Mindmap

mindmap
  root((Flowise))
    What it does
      Drag-and-drop AI builder
      Connect models and tools
      No coding required
    Use cases
      Document Q&A systems
      Customer support chatbots
      AI automation workflows
    Deployment
      Self-hosted servers
      Docker containers
      Flowise Cloud
    Tech stack
      TypeScript backend
      React frontend
      Node.js runtime
    Key features
      Visual canvas editor
      Component library
      Memory and storage

Things people build with this

USE CASE 1

Build a document Q&A chatbot by connecting a PDF loader, text splitter, vector database, and chat node without writing code.

USE CASE 2

Create a customer support agent that searches your knowledge base and responds to customer inquiries automatically.

USE CASE 3

Prototype multi-step AI workflows that combine web search, data retrieval, and language models in minutes.

Tech stack

TypeScriptNode.jsReactDockerpnpm

Getting it running

Difficulty · moderate Time to first run · 30min

Requires Docker and pnpm; multi-service setup (frontend + backend) needed to see the builder UI working.

Open-source software available under the Apache 2.0 license, allowing free use, modification, and distribution for any purpose including commercial use.

In plain English

Flowise is an open-source tool that lets you build AI-powered applications and autonomous agents through a visual, drag-and-drop interface instead of writing code. The problem it solves is the complexity of wiring together AI models, data sources, memory systems, and external tools, tasks that normally require significant programming knowledge and familiarity with AI frameworks. In Flowise, you work on a canvas where each component, an AI language model, a database connection, a web search tool, a memory store, is a visual node. You connect these nodes together with lines to define the flow of information, and Flowise runs the resulting pipeline as a working application. For example, you could drag in a PDF file node, connect it to a text-splitting node, feed that into a vector database node for storage, and then connect a chatbot node that queries the database and answers questions about your PDF, all without writing a line of code. The system integrates with a wide range of AI services and tools through its components layer, and can be deployed as a self-hosted server on your own infrastructure (AWS, Azure, DigitalOcean, and others are all supported) or used through Flowise Cloud, the hosted version. You would use Flowise when you want to prototype or build AI workflows, chatbots, or agents quickly, especially if you are not a developer or want to move faster than building from scratch. It is popular for building document Q&A systems, customer support chatbots, and multi-step AI automation pipelines. The tech stack is TypeScript and Node.js on the backend, React on the frontend, deployed as a monorepo managed with pnpm. It can also be run locally or in Docker containers.

Copy-paste prompts

Prompt 1
How do I set up a Flowise instance to build a chatbot that answers questions about my company's documentation?
Prompt 2
Show me how to connect a PDF file to a vector database in Flowise and create a Q&A interface.
Prompt 3
What components do I need to chain together in Flowise to build an autonomous agent that can search the web and summarize results?
Prompt 4
How do I deploy Flowise on AWS or Docker so my team can use it to build AI workflows?
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Generated 2026-05-18 · Model: sonnet-4-6 · Verify against the repo before relying on details.