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n8n Guides
Follow a structured learning path from your first workflow to production‑grade automation. Each guide builds on the previous one, with plenty of links to deeper reference material.
Getting Started with n8n
1 Set up your n8n instance
Install Docker Engine and Compose, then spin up n8n with PostgreSQL and Nginx using our step‑by‑step deployment blueprint. You’ll have a secure, self‑hosted automation platform running in about 30 minutes.
📖 Docker Node Deployment: Port, Volume & Compose Configuration
2 Build your first workflow
Learn how to add trigger and action nodes, connect them on the canvas, test with Execute step, and activate your first automation. The Manual Trigger is the perfect starting point.
📖 How to Add Your First n8n Node: Canvas, Panel & Connection Guide
3 Explore the workflow ecosystem
Discover the five core trigger types, conditional branching with IF and Switch nodes, and how to import thousands of community templates to jump‑ start any project.
📖 Trigger Nodes: Types, Activation & Execution Modes
📖 IF & Switch Node: Condition Config & Branch Routing
📖 Best Node Templates: Top Community Picks & How to Import
Advanced n8n Guides
4 Secure your workflows
Move beyond the basics by enabling Basic Auth, configuring HTTPS via reverse proxy, blocking risky nodes, and adding HMAC signature verification to your webhooks.
📖 Self-Host Security Hardening: Environment Variables & Config
📖 Webhook Node Security: HMAC, Auth Headers & IP Allowlisting
5 Master data transformation & expressions
Go deeper with the Code node (JavaScript + Python), the Set node, Merge node, and the full expression system including JMESPath and Luxon. These tools let you reshape, aggregate, and route data with precision.
📖 Code Node: JavaScript & Python Transformation Examples
📖 Expression Node: Variables, Functions & JMESPath Reference
6 Orchestrate AI agents and LLMs
Build autonomous agents with LangChain, connect OpenAI GPT-4o or Anthropic Claude, implement RAG with vector stores (Pinecone, Weaviate, pgvector), and add persistent memory and tool‑calling.
📖 AI Nodes: LangChain, OpenAI & Vector Store Reference
📖 Vector Store Nodes: Pinecone, Weaviate & pgvector Config
7 Scale with queue mode and production architecture
Move from single‑process to distributed execution using Redis‑backed queue mode, configure PostgreSQL for high availability, tune worker concurrency, and deploy a multi‑main HA setup.
📖 Scaling: Concurrency, PostgreSQL & Worker Queue Configuration
📖 Docker Compose: Full Production Stack Guide
Visual Learning Path
flowchart TD
subgraph Beginner["Getting Started"]
B1["1. Deploy n8n Locally
(Docker + PostgreSQL)"] --> B2["2. Build Your First Workflow
(Canvas, Triggers, Nodes)"]
B2 --> B3["3. Explore Workflow Ecosystem
(Templates, Triggers, Branching)"]
end
Beginner --> Advanced
subgraph Advanced["Advanced"]
A1["4. Secure Your Workflows
(Basic Auth, HMAC, IP Allowlisting)"] --> A2["5. Master Data Transformation
(Code Node, Expressions, JMESPath)"]
A2 --> A3["6. Orchestrate AI Agents & LLMs
(LangChain, RAG, Vector Stores)"]
A3 --> A4["7. Scale with Queue Mode & Architecture
(Redis, Workers, Multi-main HA)"]
end
A4 --> Done["Production-Ready Automation 🚀"]
