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title n8n AI Tutorial
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n8n AI Tutorial: Workflow Automation with AI

Build powerful AI-powered automations with n8n's visual workflow builder.

⚡ AI Meets Automation

GitHub

Why This Track Matters

n8n AI is increasingly relevant for developers working with modern AI/ML infrastructure. Build powerful AI-powered automations with n8n's visual workflow builder, and this track helps you understand the architecture, key patterns, and production considerations.

This track focuses on:

  • Build AI Workflows visually with n8n
  • Connect LLM Providers (OpenAI, Anthropic, Ollama)
  • Process Documents with AI extraction
  • Create AI Agents with tool access

🎯 What is n8n?

n8nView Repo is a fair-code workflow automation platform that lets you connect anything to everything. With its AI capabilities, you can build intelligent automations that leverage LLMs, process documents, and make smart decisions.

Key Features

Feature Description
Visual Builder Drag-and-drop workflow creation
AI Nodes OpenAI, Anthropic, local models
400+ Integrations Connect any service
Self-Hostable Full control over your data
Code When Needed JavaScript/Python in workflows
Agents Build AI agents with tools

Mental Model

flowchart LR
    A[Trigger] --> B[n8n Workflow]
    
    B --> C[Data Processing]
    C --> D[AI Node]
    D --> E[LLM Processing]
    
    E --> F[Decision]
    F --> G[Action 1]
    F --> H[Action 2]
    
    G --> I[Slack]
    H --> J[Database]
    H --> K[Email]
    
    classDef trigger fill:#e1f5fe,stroke:#01579b
    classDef process fill:#f3e5f5,stroke:#4a148c
    classDef ai fill:#fff3e0,stroke:#ef6c00
    classDef output fill:#e8f5e8,stroke:#1b5e20
    
    class A trigger
    class B,C process
    class D,E,F ai
    class G,H,I,J,K output
Loading

Current Snapshot (auto-updated)

  • repository: n8n-io/n8n
  • stars: about 180k
  • latest release: stable (published 2026-03-18)

Chapter Guide

  1. Chapter 1: Getting Started - Installation and first workflow
  2. Chapter 2: AI Nodes - Using OpenAI, Anthropic, and local models
  3. Chapter 3: Document AI - Processing PDFs, images, and text
  4. Chapter 4: AI Agents - Building autonomous agents
  5. Chapter 5: RAG Workflows - Retrieval-augmented generation
  6. Chapter 6: Smart Decisions - AI-powered routing and logic
  7. Chapter 7: Custom AI Tools - Extending agent capabilities
  8. Chapter 8: Production Deployment - Scaling and monitoring

What You Will Learn

  • Build AI Workflows visually with n8n
  • Connect LLM Providers (OpenAI, Anthropic, Ollama)
  • Process Documents with AI extraction
  • Create AI Agents with tool access
  • Implement RAG for knowledge-based AI
  • Make Smart Decisions based on AI analysis
  • Deploy Production automations

Prerequisites

  • Docker (recommended) or Node.js 18+
  • API keys for AI providers
  • Basic workflow automation understanding

Quick Start

Docker

docker run -it --rm \
  --name n8n \
  -p 5678:5678 \
  -v n8n_data:/home/node/.n8n \
  n8nio/n8n

# Open http://localhost:5678

npm

npm install n8n -g
n8n start

Your First AI Workflow

  1. Add Trigger - Webhook, Schedule, or Manual
  2. Add AI Node - OpenAI, Anthropic, etc.
  3. Configure Prompt - Define your AI task
  4. Add Output - Slack, Email, Database
  5. Activate - Run your workflow

AI Nodes Available

Node Capability
OpenAI GPT-4, GPT-3.5, embeddings
Anthropic Claude 3.5, Claude 3
Ollama Local LLM inference
AI Agent Autonomous agents with tools
Vector Store RAG with Pinecone, Qdrant
Document Loader PDF, web, text processing

Example: Smart Email Responder

{
  "nodes": [
    {
      "type": "gmail-trigger",
      "parameters": {"event": "newEmail"}
    },
    {
      "type": "openai",
      "parameters": {
        "model": "gpt-4o",
        "prompt": "Classify this email and draft a response:\n\n{{$json.text}}"
      }
    },
    {
      "type": "if",
      "parameters": {
        "conditions": [{"value1": "{{$json.classification}}", "value2": "urgent"}]
      }
    },
    {
      "type": "gmail",
      "parameters": {"operation": "sendReply"}
    }
  ]
}

AI Agent Example

Trigger: Customer Support Ticket

AI Agent:
  - Tool: Search Knowledge Base
  - Tool: Check Order Status
  - Tool: Create Support Ticket
  
Logic: Agent decides which tools to use based on customer query

Output: 
  - Reply to customer
  - Update CRM
  - Notify team if escalation needed

RAG Workflow

1. Document Loader → Process PDF
2. Text Splitter → Chunk document
3. Embeddings → OpenAI embeddings
4. Vector Store → Store in Pinecone
5. On Query:
   - Search vector store
   - Pass context to LLM
   - Generate response

Integrations + AI

Integration AI Use Case
Slack AI-powered responses
Gmail Smart email categorization
Notion AI content generation
Airtable Data enrichment
GitHub PR review automation
Stripe Fraud detection

Learning Path

🟢 Beginner Track

  1. Chapters 1-3: Setup and basic AI nodes
  2. Build simple AI automations

🟡 Intermediate Track

  1. Chapters 4-6: Agents, RAG, and decisions
  2. Create intelligent workflows

🔴 Advanced Track

  1. Chapters 7-8: Custom tools and production
  2. Scale enterprise AI automation

Ready to automate with AI? Let's begin with Chapter 1: Getting Started!

Generated for Awesome Code Docs

Related Tutorials

Navigation & Backlinks

Full Chapter Map

  1. Chapter 1: Getting Started with n8n AI
  2. Chapter 2: AI Nodes and LLM Integration
  3. Chapter 3: Document AI and Content Processing
  4. Chapter 4: Building AI Agents with Tools
  5. Chapter 5: Retrieval-Augmented Generation (RAG)
  6. Chapter 6: AI-Powered Decision Making and Routing
  7. Chapter 7: Building Custom AI Tools and Integrations
  8. Chapter 8: Production Deployment and Scaling

Source References

Generated by AI Codebase Knowledge Builder