Flowise No-Code AI Workflow Builder Guide 2026

Build AI Applications Without Writing Code

Flowise is an open-source, drag-and-drop tool for building LLM-powered applications. It wraps LangChain and LlamaIndex into a visual interface where you connect nodes to create chatbots, RAG pipelines, autonomous agents, and API endpoints — all without writing a single line of code.

In 2026, Flowise has become a staple in the local AI stack. It works with every major LLM provider and runs fully on-premise, making it ideal for teams that need powerful AI workflows without cloud dependency.

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What You Can Build with Flowise

• Document Q&A chatbots — chat with PDFs, CSVs, web pages
• Autonomous agents — AI that uses tools like web search, calculators, code interpreters
• Customer support bots — with fallback and escalation logic
• API endpoints — expose any workflow as a REST API
• Multi-chain pipelines — chain multiple LLM calls with conditional logic
• Memory-enabled assistants — bots that remember conversation history

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Installation

npm (Recommended)

# Install globally
npm install -g flowise

# Start Flowise
npx flowise start

Access at http://localhost:3000.

Docker

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

Docker Compose with Persistent Storage

# docker-compose.yml
version: '3.8'
services:
  flowise:
    image: flowiseai/flowise:latest
    restart: always
    ports:
      - "3000:3000"
    volumes:
      - flowise_data:/root/.flowise
    environment:
      - PORT=3000
      - DATABASE_PATH=/root/.flowise
      - APIKEY_PATH=/root/.flowise
      - LOG_PATH=/root/.flowise/logs
      - SECRETKEY_PATH=/root/.flowise

volumes:
  flowise_data:

docker compose up -d

Environment Variables

Create a .env file in your Flowise directory:

# Authentication (highly recommended for production)
FLOWISE_USERNAME=admin
FLOWISE_PASSWORD=yourpassword

# Database (SQLite by default, PostgreSQL for production)
DATABASE_TYPE=postgres
DATABASE_HOST=localhost
DATABASE_PORT=5432
DATABASE_NAME=flowise
DATABASE_USER=flowise_user
DATABASE_PASSWORD=dbpassword

# Optional: set custom port
PORT=3000

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Core Concepts

Nodes

Every Flowise workflow is built from nodes. Key node categories:

Category	Examples
LLMs	ChatOpenAI, ChatOllama, ChatAnthropic, ChatMistralAI
Vector Stores	Chroma, Pinecone, Faiss, Weaviate, Qdrant
Document Loaders	PDF, CSV, Web Scraper, YouTube, GitHub
Text Splitters	Recursive Character, Token, Markdown
Memory	Buffer Memory, Redis Memory, Zep Memory
Chains	Conversational Retrieval QA, LLM Chain, SQL Chain
Agents	ReAct Agent, OpenAI Function Agent, AutoGPT
Tools	Calculator, Web Browser, SerpAPI, Custom Tool

Chatflows

A Chatflow is a visual pipeline that processes user input and returns output. It’s the main unit of work in Flowise.

Agentflows

Agentflows are more advanced — they use AI reasoning to decide which tools to call. This enables autonomous multi-step task completion.

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Building Your First Chatflow: PDF Q&A Bot

Here’s how to build a working RAG chatbot that answers questions from a PDF document.

Step 1: Create a New Chatflow

1. Click Add New → Chatflow
2. Give it a name: “PDF Q&A Bot”

Step 2: Add a PDF Loader

1. From the left panel, drag PDF File node onto the canvas
2. Upload your PDF in the node’s settings

Step 3: Add a Text Splitter

1. Drag Recursive Character Text Splitter onto the canvas
2. Set Chunk Size: 1000, Chunk Overlap: 200
3. Connect PDF File → Text Splitter

Step 4: Add a Vector Store

1. Drag Chroma (or Faiss for in-memory) onto the canvas
2. For Chroma, set the URL: http://localhost:8000 and collection name
3. Connect Text Splitter → Chroma (Document input)

Step 5: Add an Embedding Model

1. Drag Ollama Embeddings (or OpenAI Embeddings) onto the canvas
2. For Ollama: set Base URL to http://localhost:11434, model to nomic-embed-text
3. Connect Ollama Embeddings → Chroma (Embeddings input)

Step 6: Add the LLM

1. Drag ChatOllama (or ChatOpenAI) onto the canvas
2. For ChatOllama: Base URL http://localhost:11434, model llama3.2

Step 7: Add a QA Chain

1. Drag Conversational Retrieval QA Chain onto the canvas
2. Connect Chroma → Conversational Retrieval QA Chain (Vector Store input)
3. Connect ChatOllama → Conversational Retrieval QA Chain (Chat Model input)

Step 8: Test

Click Save then Chat to open the test interface. Ask a question about your PDF.

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Building an Autonomous Agent

Agents can use tools to complete tasks autonomously. Here’s a web search + calculator agent.

Required Nodes

1. ChatOpenAI or ChatOllama — the reasoning LLM
2. SerpAPI Tool — web search (requires SerpAPI key) or use Brave Search
3. Calculator Tool — math operations
4. OpenAI Function Agent (or ReAct Agent for local models)

Setup

1. Add the LLM node and configure it
2. Add the tool nodes (no connections needed — agents discover tools automatically)
3. Add the OpenAI Function Agent node
4. Connect the LLM to the agent’s Chat Model input
5. Connect both tools to the agent’s Allowed Tools input
6. Save and test

The agent will decide which tools to use based on your question. Ask it: “What is 15% of today’s Bitcoin price in USD?” and it will search for the price, then calculate.

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Exposing Workflows as APIs

Every Flowise chatflow has a built-in REST API endpoint. Once saved:

# Get the chatflow ID from the URL or API list
CHATFLOW_ID="your-chatflow-id-here"

curl -X POST http://localhost:3000/api/v1/prediction/${CHATFLOW_ID} \
  -H "Content-Type: application/json" \
  -d '{
    "question": "What are the key findings in the Q1 security report?"
  }'

With API key authentication enabled:

curl -X POST http://localhost:3000/api/v1/prediction/${CHATFLOW_ID} \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_FLOWISE_API_KEY" \
  -d '{
    "question": "Summarize the vulnerabilities found",
    "overrideConfig": {
      "temperature": 0.2
    }
  }'

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Adding Memory to Chatbots

By default, each message is stateless. Add a Buffer Memory node to retain conversation history:

1. Drag Buffer Memory onto the canvas
2. Set Memory Key: chat_history
3. Set Session ID: {sessionId} (use the built-in variable for per-user sessions)
4. Connect to the chain’s Memory input

For persistent memory across server restarts, use Redis Memory:

# Run Redis
docker run -d -p 6379:6379 redis:alpine

Then configure the Redis Memory node with redis://localhost:6379.

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Useful Integrations

Service	Flowise Node	Use Case
Slack	Webhook	Chatbot in Slack
n8n	HTTP Request	Trigger workflows
Zapier	Webhook	Automate document ingestion
Notion	Custom Tool	Read/write Notion pages
GitHub	GitHub Tool	Code review assistants

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Flowise vs. n8n vs. LangFlow

Tool	Coding Required	LLM Focus	Complexity
Flowise	None	Yes (LangChain native)	Low–Medium
LangFlow	None	Yes (similar to Flowise)	Low–Medium
n8n	Some (JS)	Via plugins	Medium–High
Dify	None	Yes	Low

Flowise wins when your primary goal is LLM-powered workflows. n8n is better for general automation that happens to include some AI steps.

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Final Thoughts

Flowise makes building production-grade AI pipelines accessible to non-developers and rapid for developers. The visual interface removes the boilerplate of LangChain while still giving you access to the full LangChain ecosystem underneath.

Start with the PDF Q&A chatflow, get comfortable with how nodes connect, then graduate to multi-tool agents and custom integrations. Flowise can scale from a personal knowledge assistant to a team-wide AI platform running in Docker.