Flowise

Overview

Flowise is an open-source drag-and-drop UI builder for LangChain applications. It allows users to visually construct LLM flows and chains without writing code, making AI application development accessible to non-technical users. Built on top of LangChain, it provides instant visual feedback and easy deployment, with a focus on simplicity and rapid prototyping.

With over 32,000 GitHub stars, Flowise has become the go-to tool for developers and non-developers alike who want to experiment with LangChain without getting bogged down in code. Its visual interface makes it easy to understand how different components connect and interact.

Features

• Intuitive Drag-and-Drop Builder: Visual canvas where you drag nodes and connect them with wires. No coding required for basic flows.
• 100+ Pre-built Nodes: Comprehensive library of LangChain components including LLMs, prompts, chains, agents, document loaders, embeddings, vector stores, and more.
• Real-time Flow Visualization: See your flow as you build it. Each node shows its configuration and output preview.
• API Endpoint Generation: One-click deployment of your flow as a REST API with automatic documentation.
• Chat UI Embedding: Generate embeddable chat UI widgets for your website.
• Custom Node Development: Create custom nodes using TypeScript for specialized functionality.
• Multi-model Support: Connect to OpenAI, Anthropic, Google, Cohere, HuggingFace, Ollama, and more.

Installation

# Global installation
npm install -g flowise

# Start Flowise
npx flowise start

# Access at http://localhost:3000

# With Docker
docker run -d -p 3000:3000 flowise/flowise

Quick Start

# 1. Install and start
npm install -g flowise
npx flowise start

# 2. Open http://localhost:3000

# 3. Create a simple chat flow:
#    - Drag "OpenAI Chat Model" node
#    - Drag "Prompt Template" node
#    - Drag "Conversation Chat History" node
#    - Connect nodes together
#    - Click "Test" to try your flow
#    - Click "API" to get the endpoint

# 4. Embed in your website:
#    - Click "Embed" button
#    - Copy the script tag

Core Concepts

Nodes

Building blocks representing LangChain components. Each node has input and output ports for connecting to other nodes.

Connections

Wires that connect node outputs to inputs. Data flows through connections, with each node transforming the data.

Flow

A complete chain of connected nodes that represents your LLM application. Flows can be tested, saved, and deployed.

API

Each flow can be deployed as a REST API endpoint. The API accepts requests and returns responses based on your flow logic.

Examples

Example 1: Simple Q&A Chatbot

1. Drag "OpenAI Chat Model" node
2. Drag "Prompt Template" node
3. Drag "Conversation Chat History" node
4. Connect: Prompt → Chat Model
5. Connect: Chat History → Chat Model
6. Configure prompt: "You are a helpful assistant. {history} Human: {input} Assistant:"
7. Test in the preview panel

Example 2: Document Q&A with RAG

1. Drag "PDF Loader" node (upload a PDF)
2. Drag "Character Text Splitter" node
3. Drag "OpenAI Embeddings" node
4. Drag "Chroma Vector Store" node
5. Drag "Retrieval QA Chain" node
6. Connect: PDF → Splitter → Embeddings → Vector Store → Retrieval QA
7. Test with questions about your document

Example 3: Multi-step Agent

1. Drag "OpenAI Functions Agent" node
2. Add tool nodes: "Calculator", "Search", "Wikipedia"
3. Connect tools to agent
4. Add "Conversation Memory" for context
5. Test complex queries requiring multiple tools

Pros

• ✅ Truly no-code approach to LangChain
• ✅ Instant visual feedback on flow construction
• ✅ Easy API deployment with one click
• ✅ Large library of pre-built components
• ✅ Great for prototyping and demos
• ✅ Embeddable chat UI for websites
• ✅ Active community with many tutorials

Cons

• ❌ Limited to LangChain ecosystem
• ❌ Less control for complex custom logic
• ❌ Not suitable for production-scale deployments
• ❌ Visual complexity grows with flow size
• ❌ Custom nodes require TypeScript knowledge
• ❌ No built-in RAG pipeline management

Use Cases

Use Case	Why Flowise
Rapid Prototyping	Visual drag-and-drop for quick LangChain prototypes
Non-technical Teams	Enable non-developers to build AI flows
Embeddable Chat UI	Generate chat widgets for websites
API Generation	One-click REST API from any flow
Education & Demos	Visual representation of AI concepts

Comparison with Alternatives

Feature	Flowise	LangFlow	Dify	n8n
Paradigm	Visual (LangChain)	Visual (LangChain)	Visual + Code	Visual workflow
LangChain Native	✅ Yes	✅ Yes	⚠️ Partial	⚠️ Via LangChain node
Code Export	❌ No	✅ Yes	⚠️ Limited	❌ No
Chat Widget	✅ Yes	❌ No	✅ Yes	❌ No
Self-hostable	✅ Yes	✅ Yes	✅ Yes	✅ Yes
Learning Curve	Low	Low	Low-Medium	Medium
Best for	Quick LangChain prototypes	LangChain with code export	Full LLMOps	Automation + AI

Best Practices

1. Start with templates — Use built-in templates to understand node patterns
2. Test incrementally — Run individual nodes before full flow execution
3. Use API endpoints early — Deploy flows as APIs for testing
4. Organize with grouping — Group related nodes for readability
5. Document flows — Add notes to explain complex sections
6. Version control — Save flow versions for rollback

Troubleshooting

Issue	Solution
Node not connecting	Check input/output types match
API returns error	Verify API key and model configuration
Flow runs slowly	Reduce context size, use caching
Embed widget fails	Check script tag placement and CORS
Memory issues	Clear browser cache, reduce flow complexity

Resources

• Official Documentation
• GitHub Repository
• Flowise Cloud
• YouTube Tutorials
• Discord Community