n8n is a powerful workflow automation platform that lets you build complex AI agents using a visual node-based interface. It's the perfect low-code solution for creating intelligent automation without deep programming knowledge.
Drag-and-drop interface for creating complex AI workflows
Connect to any API, database, or service you need
Built-in support for OpenAI, Claude, Gemini, and local LLMs
Run on your infrastructure for complete data control
GPT-4, GPT-3.5, DALL-E
Claude 3 Opus, Sonnet, Haiku
Gemini Pro, Vision
Local LLMs (Llama, Mistral)
Advanced agent chains
Pinecone, Weaviate, Qdrant
Interactive conversations
PDF, Word, CSV processing
Add a "Chat Trigger" node to start conversations with your agent
Connect a "Window Buffer Memory" node to maintain conversation context
Add an "OpenAI Chat Model" node and set your API key and model (gpt-4)
Add tool nodes: Calculator, Web Search, Database Query
Use "AI Agent" node to orchestrate the model and tools
Define the agent's role, personality, and constraints in the system prompt
Test in n8n's chat interface, then deploy as webhook or embed in website
Build a Retrieval-Augmented Generation system for intelligent document Q&A
Workflow Components:
1. Document Ingestion Pipeline
- PDF/Word/CSV loaders
- Text splitter (chunk size: 1000, overlap: 200)
- OpenAI Embeddings
- Pinecone Vector Store
2. Query Pipeline
- Chat Trigger → User Question
- Vector similarity search (top_k: 5)
- Context assembly
- OpenAI Chat with retrieved context
- Streaming response
3. Memory Management
- Conversation buffer (last 10 messages)
- Document reference tracking
- Source citation in responses
Intelligently route requests to different AI models based on task type
Router Logic:
- Creative Writing → Claude 3 Opus
- Code Generation → GPT-4
- Quick Responses → GPT-3.5 Turbo
- Image Analysis → Gemini Vision
- Local/Private Data → Ollama (Llama 3)
Implementation:
1. Classify intent with lightweight model
2. Route to appropriate specialist model
3. Post-process and format response
4. Track usage and costs per model
Multiple specialized agents working together on complex tasks
Agent Roles:
- Research Agent: Web search, data gathering
- Analysis Agent: Data processing, insights
- Writer Agent: Content generation
- Reviewer Agent: Quality check, improvements
Orchestration:
1. Task decomposition by Manager Agent
2. Parallel execution where possible
3. Inter-agent message passing
4. Result aggregation and synthesis
5. Human-in-the-loop approval gates
| Tool Category | n8n Nodes | Use Cases |
|---|---|---|
| Databases | PostgreSQL, MySQL, MongoDB, Redis | Data retrieval, updates, analytics |
| Communication | Slack, Discord, Email, SMS | Notifications, alerts, responses |
| Documents | Google Docs, Notion, Confluence | Content creation, knowledge base |
| APIs | HTTP Request, GraphQL, Webhook | External service integration |
| Analytics | Google Analytics, Mixpanel | Data analysis, reporting |
| Development | GitHub, GitLab, Jira | Code management, issue tracking |
# Docker Compose Setup
version: '3.8'
services:
n8n:
image: n8nio/n8n
ports:
- "5678:5678"
environment:
- N8N_BASIC_AUTH_ACTIVE=true
- N8N_BASIC_AUTH_USER=admin
- N8N_BASIC_AUTH_PASSWORD=secretpassword
- N8N_HOST=n8n.yourdomain.com
- N8N_PORT=5678
- N8N_PROTOCOL=https
- NODE_ENV=production
- WEBHOOK_URL=https://n8n.yourdomain.com/
volumes:
- n8n_data:/home/node/.n8n
- ./custom-nodes:/home/node/.n8n/custom
postgres:
image: postgres:15
environment:
- POSTGRES_USER=n8n
- POSTGRES_PASSWORD=n8n
- POSTGRES_DB=n8n
volumes:
- postgres_data:/var/lib/postgresql/data
volumes:
n8n_data:
postgres_data:
| Feature | n8n | Zapier | Make.com |
|---|---|---|---|
| Pricing Model | Free self-hosted / Fair-code | Task-based pricing | Operation-based |
| AI Capabilities | Extensive, multiple providers | Limited, mainly OpenAI | Growing, good selection |
| Customization | Fully customizable | Limited | Moderate |
| Self-Hosting | ✅ Yes | ❌ No | ❌ No |
| Learning Curve | Moderate | Easy | Moderate |