How to Build a Local AI Agent With n8n (NO CODE!)

Video Description

🚀 Access ALL video resources & get personalized help in my community: https://www.skool.com/agentic-labs/about?ref=3fd61190e13d426dbf4f3b38adc7de69 🚀 Start building with n8n (use my link to support my channel ❤️): https://n8n.partnerlinks.io/f7f19w3vrhin 💬 My AI voice-to-text software (Wispr Flow): https://wisprflow.ai/r?LEON114 ☕ Buy me a coffee: https://www.buymeacoffee.com/leonvanzyl 💵 Donate using PayPal: https://www.paypal.com/ncp/payment/EKRQ8QSGV6CWW Discover how to build a powerful AI agent that runs completely on your local machine in this step-by-step tutorial. Learn to combine N8N's workflow automation with Ollama's LLM capabilities and PostgreSQL's vector database features to create a responsive assistant that can access your documents, maintain conversation history, and retrieve real-time information from the web. This video demonstrates a cost-effective solution requiring no paid API services or cloud subscriptions. You'll see how to properly configure Ollama with two essential models, set up Docker with PostgreSQL for vector search, and orchestrate everything through N8N's intuitive interface. Perfect for developers, AI enthusiasts, and business users looking to implement private AI solutions, this tutorial covers common troubleshooting issues and provides simplified alternatives to more complex setups. Watch as your agent retrieves information from uploaded documents, maintains context across conversations, and delivers accurate responses using locally-run large language models. If you're interested in self-hosted AI tools that respect your privacy while delivering powerful functionality, this comprehensive guide walks you through building an extensible foundation you can customize for your specific needs. -------------------------------------------------------- POSTGRES DOCKER COMMAND: -------------------------------------------------------- docker run --name pgvector-container -e POSTGRES_USER=myuser -e POSTGRES_PASSWORD=mypassword -e POSTGRES_DB=mydatabase -p 5432:5432 -d ankane/pgvector 🕒 TIMESTAMPS: 00:00 - Intro 00:33 - Demo 02:29 - Note on Starter Kit 02:55 - Ollama Setup 04:31 - Postgres Setup 06:20 - n8n Setup 07:01 - Create AI Agent 08:49 - Assign LLM 10:01 - Chat Memory 11:44 - Postgres Chat Memory 13:13 - Adding Tools 13:42 - Adding RAG 15:13 - Using OpenAI Node instead 16:24 - Housekeeping 17:02 - Add Web Search 17:59 - Add Docs to Vector DB #n8n #aiagents #ollama