Build an Agent by dragging and dropping nodes

Flowise AI: An open source visual LLM workflow orchestration tool that supports the construction of chat robots.
Drag-and-drop UI allows you to quickly build Chatbot + RAG (Retrieval Enhanced Generation)+ tool calls.

What project is Flowise?

core positioning

Flowise is a open source、visualization、Low code/no code Platform that helps users build AI agents, chat bots, and various large language model (LLM)-based workflows by dragging and dropping nodes

Project highlights and functions

Visualize the construction operation process: Through a complete drag-and-drop interface, connect different modules (such as prompt templates, models, vector databases, tool interfaces, etc.) to easily build an AI workflow
Based on LangChain: Using LangChain (especially LangChain.js) to execute processes in the background makes building complex logic visual and modular
Supports multiple deployment methods: Can be built through NPM, Docker, or by yourself to support on-premises deployment and cloud deployment (such as AWS, GCP, Render, Railway, etc.).
wide application range: Suitable for building chat assistants, document Q & A, RAG (Retrieval-Augmented Generation) processes, multi-agent systems, etc.
business-friendly：
- multi-agent coordination: Support multi-agent collaboration, parallel tasks, complex process structures (such as loops, routing, layering, etc.)
- Human-computer interaction (HITL): Support “human-in-the-loop” processes, such as having people review agent decisions, approve/reject tool calls
- Observability and process tracking: Provide complete execution tracking and support monitoring tools such as Prometheus and OpenTelemetry (
- process validation: Automatically check process configuration to reduce configuration errors

Project architecture and installation method

Warehouse structure (Monorepo)

The Flowise warehouse contains multiple modules, and the main structure is as follows:

server: Node.js backend, providing API interfaces;
ui: Front-end interface built using React;
components: Integrate third-party nodes and functional modules;
api-documentation: Automatically generated Swagger interface document

Installation and Usage Guide

Two common methods:

1. Local installation and operation:

npm install -g flowise
npx flowise start

then visit http://localhost:3000

2. Using Docker:

Using Docker Compose:
- replication .env.example for .env, execute after configuration docker compose up -d;
- Open in browser http://localhost:3000
Run with Docker single image:
- docker build -t flowise .
- docker run -d -p 3000:3000 flowise
- The visit is also in http://localhost:3000

developer mode

If you are conducting development and debugging, you can run it in turn:

pnpm install
pnpm build
pnpm start

The development model supports hot overloading through pnpm dev You can enable real-time changes on the front end (and back end)

Ecology and expansion

Flowise SDK – Python: Provide Python SDK (flowise Package), you can call the Flowise chat process through the API, supporting streaming and non-streaming responses
Rich integrated support: Support GitHub document loader (can load public or private repo content, configurable recursion, concurrency, filtering, etc.)
community feedback: The developer mentioned on Reddit that this tool demonstrated that it is an interesting open source project “drag drop UI to build your customized LLM flow using LangchainJS”

Summary overview

aspects	content description
essence	An open source low/no-code platform for visualizing the building AI process.
construction way	Drag and drop nodes to access LLM, RAG, tools and other components.
basic technology	Based on LangChain (especially the JavaScript version).
deployment options	Local installation, Docker container, local/cloud deployment.
enterprise characteristics	Multi-agent collaboration, man-machine review, process monitoring and verification, etc.
extended	API, Python SDK, widely integrated plug-ins, document loaders, etc.

Warehouse:https://github.com/FlowiseAI/Flowise

Oil tubing: