EnterpriseTypeScript

Flowise

by FlowiseAIUpdated Jun 15, 2025

Open-source drag-and-drop tool for building LLM flows and AI agents with a visual node-based interface. Built on top of LangChain.js, Flowise makes it easy to prototype and deploy chatbots, RAG systems, and agent workflows without writing code. Each node represents a LangChain component that can be configured and connected visually.

Architecture Overview

Flowise is a Node.js/TypeScript application with a React frontend using the ReactFlow library for the visual canvas. The backend wraps LangChain.js components as configurable nodes. When a flow is executed, the system resolves the node graph, instantiates LangChain components with their configured parameters, and runs the resulting chain or agent. Flows are persisted in a database and can be exposed as REST APIs or embedded as chat widgets.

When to Use Flowise

Visual LLM workflow prototyping and building
Chatbot creation without coding
RAG application building with visual configuration
No-code agent prototyping for business users
Rapid experimentation with different LLM architectures

Strengths & Weaknesses

Strengths

Intuitive visual interface for non-developers
LangChain.js integration provides access to a rich component library
Easy deployment as APIs or embeddable chat widgets
Self-hostable with simple Docker setup
Active community with frequent updates

Weaknesses

Limited for complex agent orchestration beyond visual nodes
UI-dependent workflow definition can be hard to version control
Performance may lag behind code-first approaches for complex flows
Debugging visual flows is less intuitive than debugging code

Quick Start

typescript

// Flowise is a visual builder. Use the API to interact with deployed flows:
const response = await fetch("http://localhost:3000/api/v1/prediction/your-chatflow-id", {
  method: "POST",
  headers: { "Content-Type": "application/json" },
  body: JSON.stringify({
    question: "What is an AI agent?",
    overrideConfig: {
      temperature: 0.7,
    },
  }),
});

const data = await response.json();
console.log(data.text);

// Flowise also supports streaming
const streamResponse = await fetch(
  "http://localhost:3000/api/v1/prediction/your-chatflow-id",
  {
    method: "POST",
    headers: { "Content-Type": "application/json" },
    body: JSON.stringify({
      question: "Explain multi-agent systems",
      streaming: true,
    }),
  }
);

const reader = streamResponse.body?.getReader();
const decoder = new TextDecoder();
while (reader) {
  const { done, value } = await reader.read();
  if (done) break;
  process.stdout.write(decoder.decode(value));
}

Features at a Glance

Developer	FlowiseAI
Language	TypeScript
License	Apache-2.0
GitHub Stars	35k+
MCP Support	No
Multi-Agent	No

Notable Users

FlowiseAIMonday.comZapier community

Resources

Official Documentation

https://docs.flowiseai.com/

GitHub Repository

https://github.com/FlowiseAI/Flowise

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