If you want to learn:
How do I run n8n self-hosted on my local machine instead of using the cloud version?
What is Ollama and how can I use it to run AI models locally on my computer?
How do I integrate Ollama with n8n to create AI workflows using local LLMs?
What's the difference between connecting to APIs in n8n cloud versus self-hosted n8n?
How can I set up AI automation workflows that run completely locally without external API calls?
What are the steps to configure Docker for running n8n with Ollama integration?
Then this lecture is for you!
This lecture demonstrates how to run n8n self-hosted using Docker and integrate it with Ollama to execute AI workflows using local LLMs. You'll learn the practical differences between n8n cloud and self-hosted instances, particularly regarding credential setup for services like Google Sheets. The lecture walks through installing Ollama, downloading open-source models like Mistral and Gemma, and configuring the Ollama chat model node in n8n. You'll discover how to use Docker's host.docker.internal mapping to connect your n8n container to Ollama running on localhost port 11434. The demonstration includes building a functional AI agent workflow that uses local language models to answer queries and call tools, all running entirely on your local machine. You'll see real examples of model selection based on your computer's RAM and GPU capabilities, and understand the performance considerations when running local AI automation. This hands-on guide covers the complete setup process for self-hosted AI workflows, from Docker container configuration to testing AI-powered applications with locally deployed LLMs.