Flowise: Build LangChain Flows Visually Without Writing Code

What Is Flowise?

Flowise is an open-source tool that brings LangChain to a visual drag-and-drop interface. You build LLM pipelines — RAG systems, conversational agents, document QA bots — by connecting nodes on a canvas instead of writing Python or JavaScript.

The output is a live API endpoint and embeddable chat widget, ready to integrate into any application.

Core Interface

The canvas has nodes for every major LangChain component:

• LLMs — OpenAI, Anthropic, Bedrock, Ollama (local), Groq
• Memory — Buffer Memory, Redis Memory, Zep
• Retrievers — Pinecone, Chroma, Weaviate, Qdrant, FAISS
• Document Loaders — PDF, URL, Notion, GitHub, CSV
• Tools — web search (SerpAPI, Tavily), code interpreter, custom function calls
• Chains — Conversational Retrieval QA, SQL Database, OpenAPI

You drag nodes onto the canvas, connect their inputs and outputs, configure credentials in the node settings, and click Save.

Pre-Built Templates

Flowise ships with templates for common patterns:

• Conversational RAG — upload PDFs, ask questions with chat history
• SQL Agent — connect to a database, query it with natural language
• Document QA — multi-document Q&A with source citations
• Web Scraping Agent — browse URLs and extract structured data

Templates give you a working flow in under two minutes that you can then customize.

Document Upsert API

For dynamic document ingestion, Flowise exposes an Upsert API:

curl -X POST http://localhost:3000/api/v1/vector/upsert/{chatflowId}   -H "Content-Type: multipart/form-data"   -F "files=@document.pdf"   -F "pineconeIndex=my-index"

This lets you build workflows where new documents are automatically indexed when uploaded.

Calling Your Flow as an API

Every saved flow gets an API endpoint:

const response = await fetch("http://localhost:3000/api/v1/prediction/YOUR_FLOW_ID", {
  method: "POST",
  headers: { "Content-Type": "application/json" },
  body: JSON.stringify({ question: "What is the refund policy?" }),
});
const { text } = await response.json();

Embedded Chat Widget

Flowise generates a script tag you paste into any webpage to embed a chat widget that connects to your flow:

<script type="module">
  import Chatbot from "https://cdn.jsdelivr.net/npm/flowise-embed/dist/web.js"
  Chatbot.init({
    chatflowid: "YOUR_FLOW_ID",
    apiHost: "http://localhost:3000",
  })
</script>

Self-Hosting with Docker

docker run -d   --name flowise   -p 3000:3000   -v ~/.flowise:/root/.flowise   flowiseai/flowise

Flowise vs Dify vs Langflow

Flowise is the simplest of the three — closest to "LangChain with a GUI." Dify has better built-in RAG pipeline management and more sophisticated dataset handling. Langflow is the most technically similar to Flowise but has a different node library. For teams already familiar with LangChain who want a visual interface, Flowise has the most direct mapping between the canvas and LangChain concepts.

Resources

• Flowise GitHub
• Official docs
• Flowise Cloud