Hands-on course teaching n8n automation, AI agents, RAG, multimodal workflows, production patterns and deployment
Automation is no longer just a buzzword — it’s reshaping how teams work, collaborate, and scale. At the center of this transformation is n8n: an open-source, extensible automation platform that helps you connect APIs, orchestrate workflows, and build intelligent AI-driven automations.This course — n8n: Zero to Hero by KodeKloud — walks you from the basics to advanced, production-ready automations. I’m Marconi Darmawan, and in this course we’ll move step-by-step from beginner concepts to advanced multi-agent systems and orchestration patterns. Whether you are a DevOps engineer, AI practitioner, or a non-technical professional wanting to automate real-world tasks, this course is designed to get you hands-on with n8n quickly.
This course teaches n8n fundamentals (nodes, inputs/outputs, data types), how workflows execute, and how to securely configure API keys for services such as OpenAI, Anthropic, and KodeKloud Keyspaces. Expect a mix of conceptual material, demos, and hands-on labs that reinforce learning by doing.
What you’ll learn (high level)
Core n8n concepts: nodes, connectors, inputs/outputs, and execution logic
How to configure API keys and integrate LLMs and web APIs
Building AI agents: single-agent and multi-agent workflows
Retrieval-Augmented Generation (RAG) with vector databases
Multimodal workflows: text-to-image, text-to-video, and image-to-video
Production considerations: retries, error handling, and reusable workflow patterns (MCPs)
Course modules at a glance
Module
Focus
Outcome
Introduction & Setup
n8n basics, Playground, API keys
Run your first workflow and connect to OpenAI/Anthropic
Nodes & Data Flow
Node types, data formats, execution model
Design predictable workflows and inspect node data
AI Agents (Single & Multi)
Email agents, research agents, multi-agent patterns
Build agents that draft responses and conduct research
We begin by covering the essentials: how nodes work, how data flows between nodes, and the main data types you’ll encounter inside n8n. You’ll also learn how workflows run under the hood using n8n’s default execution logic and how to inspect intermediate data while debugging.From there we start building hands-on AI agent workflows. These include:
An email AI agent that drafts and replies autonomously.
A multi-agent research workflow that pulls facts from tools like Perplexity and OpenAI, drastically reducing research time.
Workflows using the HTTP Request node to scrape, fetch, and call external APIs safely.
We’ll also explore creative, multimodal automations: text-to-image, text-to-video, and image-to-video pipelines using cutting-edge models like Veo3 and Seedance. You’ll build a Slack automation that replies to coworkers on your behalf, enabling routine queries to be handled automatically while you focus on higher-value tasks.In the optional deployment section you’ll learn how to self-host n8n with Docker, run a local LLM via Ollama, and host Playground environments on Amazon EC2. This gives you flexible deployment choices according to your scale and budget.
RAG (Retrieval-Augmented Generation) and vector databases are core to building workflows that remember and reference context. We’ll demonstrate indexing documents, querying a vector store (e.g., Pinecone), and combining retrieved knowledge with LLM outputs to create a customer-support RAG agent — the kind of workflow real businesses use to give accurate, context-aware responses.
We’ll discuss and compare workflow patterns, including MCPs (Modular, Composable Patterns), and show how sub-workflows help you manage complexity. An advanced, multi-workflow build ties several agents together into an enterprise-style orchestration system that scales without chaos.
We pay special attention to production-readiness: retries and backoff strategies, error handling and logging, secrets management, and accelerators like the n8n Workflow Template Marketplace so you can bootstrap common automations quickly.Practical labs and checkpoints are embedded throughout the course so you apply concepts immediately — reinforcing learning and building confidence with each module.
Security reminder: treat API keys and secrets like credentials. Use n8n’s built-in credentials store or environment variables when deploying (Docker, EC2, or managed hosting). Avoid hardcoding keys in workflows or public repositories.
Getting started (recommended first steps)
Sign up for access to any APIs you plan to use (OpenAI, Anthropic, Pinecone).
Launch the n8n Playground (or self-host via Docker) and configure credentials.
Follow the first lab to create a simple trigger → action workflow and inspect node execution.
Progress to the AI agent labs and start integrating LLMs and external APIs.
At KodeKloud we believe in learning by building. Join the community, ask questions, and share your workflows. Ready to move from zero to hero with n8n?
