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Open Source Free Self-Host Visual Builder RAG Pipelines

Flowise: Drag-and-Drop LLM App Builder Powered by LangChain

Flowise is an open-source, low-code tool for building LLM applications visually. Connect LangChain components in a drag-and-drop canvas to build chatbots, RAG pipelines, and agents — then deploy them as APIs in minutes.

Try Flowise ↗ GitHub ↗ Docs ↗
Flowise drag-and-drop LangChain flow builder and API embed

What is Flowise?

Flowise is an open-source, low-code LLM flow builder built on top of LangChain and LangChain.js. It provides a visual node-based canvas where you connect components — LLM models, vector stores, memory, document loaders, and tools — to build applications that would otherwise require hundreds of lines of LangChain code.

The result is a running API endpoint you can call from any application. Flowise is particularly popular because it makes LangChain's power accessible without deep Python/JavaScript knowledge, while still exposing the full ecosystem for those who want it.

Key features of Flowise

1

Visual LangChain flow builder

Every LangChain component — chains, retrievers, memory, parsers, tools — is available as a visual node. Drag, drop, and connect them to build complex pipelines without code.

2

Built-in vector store support

Connect to Pinecone, Weaviate, Chroma, Qdrant, Supabase, and more as vector stores. Upload documents, generate embeddings, and wire up retrieval in the same canvas.

3

API endpoint generation

Every Flowise flow is automatically exposed as a REST API. Call it from your web app, mobile app, or any backend with a simple POST request — no separate deployment steps.

4

Chatflow and Agentflow

Build simple conversation flows (Chatflow) for Q&A and RAG chatbots, or Agentflow for tool-using agents that can search the web, run code, and call APIs.

5

Embeddable chat widget

Generate a JavaScript snippet to embed a chat widget directly on any website — your Flowise flow becomes a polished chatbot interface without custom frontend work.

6

Self-hostable with Docker

Run Flowise entirely on your own infrastructure. Your data and API keys never leave your environment — critical for compliance-sensitive applications.

How Flowise works

  1. 1 Install Flowise: npx flowise start or via Docker
  2. 2 Open the UI at localhost:3000 and create a new Chatflow
  3. 3 Drag nodes onto the canvas — LLM, vector store, memory, document loader
  4. 4 Connect the nodes, configure API keys in each node's settings
  5. 5 Test in the built-in chat panel, then copy the API endpoint for your app

Real-life use cases of Flowise

Document Q&A chatbots

Build a chatbot over PDFs, websites, or databases in under 30 minutes without writing a line of LangChain.

Customer support bots

Embed a chatbot on your support page that answers from your knowledge base and escalates edge cases.

Internal tool agents

Build agents that search internal databases, call REST APIs, and summarise results for internal teams.

Pros and cons of Flowise

Pros

  • + Open-source, MIT licence — free to self-host
  • + Exposes the full LangChain ecosystem visually
  • + Auto-generates API endpoints
  • + Embeddable chat widget out of the box
  • + Large and active community

Cons

  • − Complex flows can become hard to read on the canvas
  • − Debugging is harder than in pure code
  • − Observability and analytics limited compared to Dify
  • − Some advanced LangChain features require custom nodes

Flowise pricing

Plan Price Details
Open-source Free Fully featured, self-host with npm or Docker
Cloud Starter $35/month Hosted with automatic updates and basic support
Cloud Pro $65/month More resources, custom domain, priority support

Alternatives to Flowise

  • Dify — more production-oriented with built-in observability and multi-model management
  • LangChain — pure code, more flexibility and power for experienced developers
  • n8n — broader automation platform with AI capabilities added on
  • Langflow — similar drag-and-drop LangChain builder, DataStax-backed

Tips for using Flowise

No authentication by default — set credentials before exposing the UI

A fresh Flowise installation is accessible to anyone who can reach its URL. Before exposing it to a network, set FLOWISE_USERNAME and FLOWISE_PASSWORD environment variables, or place it behind a reverse proxy with auth.

Export flows as JSON before upgrading

Flowise pins specific LangChain versions — upgrading can break existing flows. Before upgrading, export all flows as JSON backups and test in a separate environment.

Complex canvases: use API chaining instead of a single mega-flow

Flows with more than 25–30 nodes become difficult to navigate. Break your pipeline into multiple smaller chatflows and chain them via the Flowise API for better readability.

Use environment variable references, not hardcoded API keys

API keys set as plain text within a node may appear in flow export files. Always use environment variable references like $OPENAI_API_KEY in node settings.

Quick start: RAG chatbot in 5 minutes

  1. 1 Start Flowise and create a new Chatflow
  2. 2 Add: PDF File Loader → Text Splitter → OpenAI Embeddings → Chroma (in-memory)
  3. 3 Add: Retrieval QA Chain → connect to the vector store
  4. 4 Add: ChatOpenAI → connect to the chain
  5. 5 Test in the chat panel and copy the API endpoint

Flowise is the fastest way to go from "I want to use LangChain" to a working, deployed API — without reading the LangChain docs cover to cover first. Compare with Dify for a more complete platform with observability and Phidata for code-first agent building.

Flowise FAQ

Is Flowise free?
Yes. Flowise is open-source and free to self-host with no limits. Cloud plans start at $35/month for teams who want managed hosting. Self-hosting requires Node.js and runs on any server, VPS, or locally.
What is the relationship between Flowise and LangChain?
Flowise wraps LangChain components in a visual drag-and-drop interface. Every node in Flowise corresponds to a LangChain class or concept — chains, retrievers, embeddings, vector stores, memory, tools, and agents. You build LangChain pipelines without writing Python code.
Can Flowise build RAG pipelines?
Yes. RAG (Retrieval-Augmented Generation) pipelines are one of Flowise's primary use cases. You connect a Document Loader, a Text Splitter, an Embeddings model, a Vector Store, and a Retrieval QA Chain — all visually — and deploy as an API in minutes.
How does Flowise compare to Dify?
Flowise is closer to LangChain's mental model with a node-based canvas and is better for developers already familiar with LangChain. Dify has a more polished product feel with built-in observability, conversation history, and prompt versioning. Dify suits teams wanting a complete platform; Flowise suits developers who want visual LangChain.
What databases does Flowise support for vector storage?
Flowise supports Pinecone, Chroma, Qdrant, Weaviate, Milvus, Supabase pgvector, and local in-memory vector stores. You can swap vector stores without changing the rest of your pipeline, since all are interchangeable LangChain-compatible nodes.
What are the best alternatives to Flowise?
Dify is the most direct alternative with a more complete platform approach. LangFlow is another visual LangChain builder. For code-first development, LangChain Python and LlamaIndex are the standard choices. For fully managed RAG, Pinecone Assistant and Weaviate Generative Search offer simpler alternatives.