Automation

Flowise AI: Build Custom LLM Workflows with No-Code Visual Pipelines (2026 Guide)

Flowise AI: Build Custom LLM Workflows with No-Code Visual Pipelines (2026 Guide)

Server and Network Infrastructure Photo by Brett Sayles on Unsplash

Building AI-powered applications used to require deep knowledge of LangChain, vector databases, prompt engineering, and Python. Flowise changes that with a visual, drag-and-drop interface for building LLM workflows β€” everything from simple chatbots to complex multi-agent systems with RAG (Retrieval-Augmented Generation), memory, and tool use.

In 2026, Flowise has become the open-source platform of choice for developers and technical teams who want to build powerful AI applications without writing everything from scratch. This guide covers what Flowise can do, how to set it up, and the most valuable workflows you can build with it.

What is Flowise?

Flowise is an open-source, low-code tool for building LLM applications using a visual node-based interface. It’s built on top of LangChain and LlamaIndex, abstracting the complex plumbing of AI pipelines into draggable nodes that you connect visually.

Key facts:

  • Open source (Apache 2.0 license) β€” free to self-host
  • Cloud option β€” Flowise Cloud for managed hosting
  • Built on LangChain.js + LlamaIndex
  • Outputs β€” REST API, embedded chat widget, Slack/Discord bots
  • GitHub stars β€” 30,000+ (one of the fastest-growing AI repos)

Who uses Flowise:

  • Developers building AI features for SaaS products
  • Agencies building AI chatbots for clients
  • Enterprises building internal knowledge bases
  • Researchers prototyping LLM systems

Installation

# Option 1: npm
npm install -g flowise
npx flowise start

# Option 2: Docker
docker pull flowiseai/flowise
docker run -d --name flowise \
  -p 3000:3000 \
  -v ~/.flowise:/root/.flowise \
  flowiseai/flowise

# Option 3: Docker Compose (with PostgreSQL)
# See flowise.ai for docker-compose.yml

Flowise runs at http://localhost:3000. The visual editor opens immediately β€” no configuration required to start experimenting.

Flowise Cloud

For teams who don’t want to manage infrastructure, Flowise Cloud provides:

  • Managed hosting on AWS
  • Automatic updates
  • Built-in monitoring and logs
  • Starting at $35/month

Core Concepts

Nodes

Everything in Flowise is a node β€” a configurable block that does one thing:

  • LLMs β€” GPT-4o, Claude 3.7, Gemini, local models via Ollama
  • Vector Stores β€” Pinecone, Chroma, Weaviate, Qdrant, pgvector
  • Document Loaders β€” PDF, Word, CSV, websites, Notion, GitHub
  • Text Splitters β€” How to chunk documents for embedding
  • Memory β€” Conversation memory, buffer window, summary memory
  • Tools β€” Web search, calculators, code execution, custom APIs
  • Chains β€” Pre-built patterns like Q&A chain, SQL chain
  • Agents β€” ReAct, OpenAI Functions, Conversational agents

Flows

A Flow is a connected graph of nodes. You create a flow by:

  1. Dragging nodes from the palette
  2. Connecting outputs to inputs (drag the colored dots)
  3. Configuring each node (click to edit)
  4. Saving and deploying as an API

Chatflows vs. Agentflows

  • Chatflow β€” Linear pipeline: input β†’ processing β†’ output
  • Agentflow β€” Autonomous agent with tools, memory, and decision-making

Essential Workflows

1. Document Q&A Chatbot (RAG)

The most common Flowise use case: upload documents, ask questions about them.

Nodes needed:

PDF Loader β†’ Text Splitter β†’ OpenAI Embeddings β†’ Chroma Vector Store
                                                          ↓
                                          Conversational Retrieval QA Chain
                                                          ↓
                                                      ChatOpenAI (GPT-4o)

Setup:

  1. Drag PDF File loader β†’ configure with your PDF
  2. Add Recursive Text Splitter β†’ chunk size 1000, overlap 200
  3. Add OpenAI Embeddings β†’ connect your API key
  4. Add Chroma vector store β†’ In-Memory for testing, persistent for production
  5. Add Conversational Retrieval QA Chain β†’ connects loader + vector store + LLM
  6. Add ChatOpenAI β†’ model: gpt-4o, temperature: 0.1
  7. Connect everything β†’ Save β†’ Test in chat window

Result: A chatbot that accurately answers questions from your documents, with conversation memory.

Data Visualization and Technology Photo by Luke Chesser on Unsplash

2. AI Customer Support Agent

Build an agent that can search your knowledge base AND call external APIs.

Nodes:

Vector Store (product docs) β†’ Retriever Tool
Web Search Tool              β†’ 
Custom API Tool (check order status) β†’ OpenAI Agent β†’ ChatOpenAI
Conversation Memory          β†’

Configuration highlights:

  • System prompt: β€œYou are a helpful customer support agent for [Company]. Always check the knowledge base before answering. If the customer asks about order status, use the order API tool.”
  • Memory: Buffer Window Memory (last 10 messages)
  • Tools: Retriever Tool + Web Search + Custom HTTP API

3. SQL Database Agent

Query databases in natural language.

MySQL/PostgreSQL Connection β†’ SQL Database Chain β†’ ChatOpenAI

Users ask: β€œHow many orders were placed last week from California?” The agent writes and executes SQL, returns the answer in plain English.

4. Multi-Document Research Assistant

Combine multiple data sources into one intelligent assistant.

Notion Pages Loader  β†˜
GitHub Repo Loader   β†’  OpenAI Embeddings β†’ Pinecone β†’ Conversational Agent
Web Scraper          β†—
Confluence Loader    β†—

This is the foundation of an internal knowledge base assistant.

5. Automated Content Pipeline

Use agents to generate and refine content:

User Input β†’ Planner Agent β†’ Writer Agent β†’ Editor Agent β†’ Output

Each β€œagent” node has a different system prompt (planner, writer, editor) and they pass their output to the next stage.

Connecting to Your Apps

REST API

Every Flowise chatflow automatically generates an API endpoint:

curl -X POST http://localhost:3000/api/v1/prediction/{chatflowId} \
  -H "Content-Type: application/json" \
  -d '{"question": "What is our refund policy?"}'

Embedded Chat Widget

Add a chat bubble to any website:

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

Integration with n8n/Make

Use Flowise’s HTTP API as a node in n8n or Make workflows β€” perfect for triggering AI analysis when new data arrives.

Advanced Features

Custom Tools

Write custom JavaScript tools that your agent can call:

// Tool: Get Customer Details
const customTool = {
  name: "getCustomerDetails",
  description: "Get customer information by email address",
  schema: z.object({ email: z.string() }),
  func: async ({ email }) => {
    const response = await fetch(`https://api.yourapp.com/customers/${email}`);
    return JSON.stringify(await response.json());
  }
}

Local LLMs with Ollama

For privacy-sensitive data, connect local models:

  • Add ChatOllama node
  • Model: llama3.2, mistral, phi4
  • Base URL: http://localhost:11434
  • Zero data leaves your infrastructure

Human-in-the-Loop

Add approval steps where humans review before agent actions:

  • Flowise supports interrupt/resume patterns
  • Useful for high-stakes automations (sending emails, making payments)

Flowise vs. Alternatives

Feature Flowise LangFlow Dify n8n AI
Open source βœ… βœ… βœ… βœ…
Visual builder βœ… βœ… βœ… βœ…
RAG support βœ…βœ… βœ… βœ… ⚠️
Agent support βœ… βœ… βœ… ⚠️
LangChain native βœ… βœ… ❌ ❌
Multi-agent βœ… ⚠️ βœ… ❌
Embeddable chat βœ… ❌ βœ… ❌
Self-host ease βœ…βœ… βœ… βœ… βœ…
Community Large Large Growing Large

Pricing

Option Cost
Self-hosted Free (infrastructure costs only)
Flowise Cloud Starter $35/month
Flowise Cloud Pro $100/month
Enterprise Custom pricing

The self-hosted version is completely free and production-ready. Many teams run it on a $10/month VPS or on AWS/GCP.

Getting Started: Your First RAG Chatbot in 30 Minutes

  1. Install Flowise (npx flowise start)
  2. Open http://localhost:3000
  3. Click Add New β†’ Create a new Chatflow
  4. Drag these nodes: PDF Loader, Text Splitter, OpenAI Embeddings, Chroma, Conversational Retrieval QA, ChatOpenAI
  5. Connect them in order
  6. Configure OpenAI API key in each node that needs it
  7. Upload a PDF in the PDF Loader
  8. Click Save β†’ Chat button
  9. Ask questions about your document

That’s it. You’ve built a production-capable RAG chatbot with no code.

Tips for Production Deployments

  1. Use persistent vector stores β€” Chroma (local disk), Pinecone, or Weaviate for real deployments
  2. Set rate limits β€” Configure API key authentication to prevent abuse
  3. Monitor with logs β€” Flowise Cloud has built-in logging; self-hosted can use Langfuse
  4. Version your flows β€” Export flows as JSON and store in Git
  5. Use environment variables β€” Never hardcode API keys; use Flowise’s credential manager
  6. Test with different chunking strategies β€” Chunk size significantly impacts RAG quality

Conclusion

Flowise democratizes the building of sophisticated AI applications. What previously required weeks of LangChain development can now be built in hours with the visual editor, tested immediately, and deployed as an API or chat widget.

For technical teams who want control, customization, and the ability to run on their own infrastructure, Flowise is the clear choice over SaaS alternatives. The open-source nature means you’re never locked in, the community is large and active, and new nodes and features ship frequently.

Whether you’re building a customer support bot, an internal knowledge base, or a complex multi-agent research system, Flowise provides the building blocks β€” and in 2026, it’s more capable than ever.

What kind of AI workflow are you building with Flowise? Share your use case in the comments!

Tags: #flowise #flowise ai #llm workflow #no-code ai #langchain #rag #ai agents #chatbot builder #open source ai