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n8n: Build AI Agents, APIs, and Automation

Instructor: Mumshad Mannambeth

Included with Learn more

5 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

5 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Configure n8n nodes, data flow, execution logic, and credentials for Google, Slack, and third-party APIs.
Build production AI agents using chat models, memory, and tools, expanding into multi-agent and media workflows via subworkflows.
Build a Pinecone RAG pipeline for document-based agent retrieval, and set up MCP servers and clients to scale tools.
Design a multi-agent marketing system with master-delegated sub-agents, hardened using error handling, retries, and fallbacks.

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

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There are 5 modules in this course

Go from n8n basics to production AI agents, RAG pipelines, and multi-agent workflows — step by step.

This course teaches you to build real, production-ready AI agents and automated workflows in n8n — one of the fastest-growing workflow automation platforms available. You'll start with n8n's core mechanics: node types, data flow, JSON, and execution logic. Then you'll move straight into building — eight workflows constructed end to end on n8n Cloud, including an AI email agent, a multi-agent research system, image and video generation pipelines, and a Slack automation. Along the way you'll learn how to connect APIs using the HTTP Request node, handle authentication and cURL imports, and structure reusable subworkflows. The course takes you into advanced AI territory: retrieval-augmented generation (RAG) with Pinecone, so your agents can answer from your own content, and the Model Context Protocol (MCP), so you can hand agents their tools in a cleaner, more scalable way. The capstone module brings everything together in one long-form build: a marketing-team system where a master agent delegates to specialised sub-agents that produce blog posts, social posts, and reels from a single request. The course closes with production essentials — error workflows, retries, fallbacks, and the n8n template marketplace — and an optional self-hosting track using Docker Desktop, Ollama, and AWS EC2. Who this is for: Developers, technical marketers, product managers, and automation builders who want to build AI-powered workflows in n8n without starting from scratch every time.

The mechanics every later build depends on: the types of node n8n offers, how data moves in and out of a node, the data types passed between nodes, and the architecture and default run logic that decide what executes and in what order.

What's included

3 videos2 assignments

The core of the course: eight workflows built end to end on n8n Cloud, from a single email agent up to multi-agent research, image and video generation, and Slack automation. Along the way it covers the plumbing those builds rely on - Google Cloud credentials, the HTTP Request node, API authentication and cURL imports, and subworkflows - with a hands-on lab after most workflows. It closes with an optional self-hosting track: running n8n locally with Docker Desktop and Ollama, or on an AWS EC2 instance, and when that is worth the extra work.

What's included

41 videos6 readings2 assignments

41 videosTotal 184 minutes

Sign Up on n8n Cloud + Recommended Build-Along Setup (Settings, n8n Folders, Admins, and Updates)5 minutes
Explore KodeKloud Keyspace4 minutes
Call Keyspace via HTTP Node2 minutes
Start Your First Email Agent5 minutes
Add Memory to Your AI Agent4 minutes
Configure Email Tool Inputs5 minutes
Prompt and Test Email Agent6 minutes
Push Workflow to Production6 minutes
Google Cloud Console Setup6 minutes
Start the AI News Workflow6 minutes
Prompt the Research Agent4 minutes
Build the News Checking Agent6 minutes
Send the Daily News Digest6 minutes
Log Headlines to Google Sheets5 minutes
Call APIs with HTTP Node5 minutes
Scrape the Web with Firecrawl6 minutes
Wait and Loop for API Results5 minutes
API's Auth Security and cURL Imports2 minutes
Build an Image Prompt Agent4 minutes
Generate Images on WaveSpeed4 minutes
Loop and Email the Image3 minutes
Generate Video with Veo35 minutes
Fetch and Email the Video5 minutes
Set Up Drive and Telegram Bot5 minutes
Notify Uploads on Telegram4 minutes
Build the Video Prompt Agent5 minutes
Generate Video with Seedance4 minutes
Call Veo3 on Vertex AI3 minutes
Authenticate with Google OAuth3 minutes
Fetch the Veo3 Video Result5 minutes
Add the Execute Workflow Node5 minutes
Pass Data and Await Response5 minutes
Run the Split Workflow4 minutes
Set Up the Slack API Bot5 minutes
Chat with AI Agent on Slack3 minutes
Configure Slack Impersonator6 minutes
Let the Agent Do Your Job6 minutes
Self-Hosting Locally With Docker Desktop and Ollama4 minutes
Launch an EC2 for n8n4 minutes
Install n8n with Docker5 minutes
Self-Hosting vs Building on n8n Cloud2 minutes

6 readingsTotal 60 minutes

Lab: Building your first AI Email Agent on n8n10 minutes
Lab: Multi Agent Research WF with Perplexity OpenAI10 minutes
Lab: Text to image10 minutes
Lab: Text to video generation10 minutes
Lab: Calling subworkflows10 minutes
Lab: Fun Slack Impersonator10 minutes

2 assignmentsTotal 60 minutes

Quiz 230 minutes
n8n Agent Workflow Build-Along30 minutes

Retrieval-augmented generation on n8n: why an agent needs a vector store, how source documents are prepared, embedded and upserted into Pinecone, and how to wire that store into a working customer-support agent that answers from your own content. It then turns to the Model Context Protocol in n8n - using MCP servers and clients to hand an agent its tools - and where MCP is the better choice than wiring the same capability as a workflow or subworkflow.

What's included

6 videos2 readings2 assignments

6 videosTotal 27 minutes

RAG Agents on n8n - RAG and Vector DB3 minutes
Prep Files for Vector Upsert6 minutes
Upsert Documents to Pinecone5 minutes
Your First n8n RAG Agent: Customer Support RAG Agent6 minutes
MCPs on n8n5 minutes
MCP vs Workflows on n8n2 minutes

2 readingsTotal 20 minutes

Lab: RAG Agent Pinecone Vector DB10 minutes
Lab: MCP in n8n10 minutes

2 assignmentsTotal 60 minutes

Quiz 330 minutes
n8n RAG Agent & MCP30 minutes

A long-form capstone that combines the course into one build: a marketing-team system where a master agent delegates to specialised sub-agents for blog posts, social posts and reels, assembled and tested step by step.

What's included

4 videos1 reading2 assignments

Production concerns, the wider ecosystem, and the wrap-up: catching failures with a dedicated error workflow, alerting on them, adding retries and fallbacks, and starting from someone else's build via the n8n template marketplace - then a recap of everything built, the patterns worth carrying into your own automations, and where to go next with n8n and AI agents.

What's included

4 videos2 assignments

Instructor

Mumshad Mannambeth

KodeKloud

63 Courses49,938 learners

Offered by

KodeKloud

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Frequently asked questions

You do not need to be a developer, but some comfort with technical concepts — such as what an API is, what JSON looks like, and how HTTP requests work — will help you move faster. n8n is a visual workflow builder, so most of the course involves connecting nodes rather than writing code. The course includes labs with step-by-step guidance after most workflow builds.

You'll build eight workflows end to end, including an AI email agent with memory, a multi-agent research system that sends daily news digests, an image generation pipeline using WaveSpeed, a video generation workflow using Veo3 and Seedance, a Slack bot that lets an AI agent respond as you, a RAG-powered customer support agent backed by Pinecone, and a full marketing-team system where a master agent delegates blog posts, social posts, and reels to specialised sub-agents. Most workflows are accompanied by a hands-on lab.

Yes — the course includes an optional self-hosting track that covers running n8n locally using Docker Desktop and Ollama, and deploying it on an AWS EC2 instance. This section is clearly marked as optional; you can complete the full course on n8n Cloud without it. It is most relevant to learners who want full control over their n8n environment or who need to keep data on their own infrastructure.

RAG — retrieval-augmented generation — is the technique that lets an AI agent answer questions from your own documents rather than relying solely on what it was trained on. In this course, you'll build a RAG pipeline in n8n that prepares and chunks source documents, upserts them into a Pinecone vector database, and wires that store into a working customer-support agent. It is one of the most practical and widely used patterns in production AI systems.

MCP is a standardised way to give AI agents access to external tools and data sources. The course introduces it from scratch — you do not need any prior knowledge. You'll learn what MCP servers and clients are in n8n, build a working example, and understand when MCP is the better choice compared to wiring the same capability as a native workflow or subworkflow.

To access course materials, assignments, and earn a Certificate, you'll need to purchase the Certificate experience when you enroll in a course. Eligible learners may also have the option to start with a Free Trial. Some courses may also offer a Full Course, No Certificate option. This lets you access course materials, submit required assessments, and receive a final grade, but you won't be able to earn or purchase a Certificate.