Last week I introduced you to Crawl4AI - an open source and LLM friendly web scraper that makes it super easy to crawl any website and format it for a RAG knowledgebase for your AI agent. I even created a full AI agent as a follow up video that leverages this knowledgebase I created with Crawl4AI. A TON of you asked me to do the same thing in n8n, so here it is! In this video I show you exactly how to deploy Crawl4AI super easily with Docker and leverage it within your n8n workflows to crawl website pages in seconds. We even build a simple AI agent that uses this knowledgebase to become an expert at the documentation for Pydantic AI - my favorite AI Agent framework right now! There are a lot of ways to crawl websites, but many of them are expensive, slow, and/or difficult to work with. Crawl4AI on the other hand is easy to use, fast, and completely free since it is open source. The only thing you have to pay for is the machine in the cloud to run your crawler, and that’s only if you aren’t just running it on your computer! ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Check out TEN Agent now (completely open source!) and see how easy it is to get started building voice AI agents for free: GitHub repo: https://github.com/TEN-framework/TEN-Agent Playground: https://agent.theten.ai/ If you aren't aware, voice agents are one of the biggest needs businesses have right now, so if you're a developer looking to make money with AI, tools like TEN Agent are definitely worth learning and using! ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Here is the n8n workflow I covered in this video! It’s in a folder along with all the other Crawl4AI stuff I’ve done on my channel recently with Python. https://github.com/coleam00/ottomator-agents/blob/main/crawl4AI-agent/n8n-version/Crawl4AI_Agent.json Register now for the oTTomator AI Agent Hackathon with a $6,000 prize pool! https://studio.ottomator.ai/hackathon/register Try the Pydantic AI expert out now on the Live Agent Studio! https://studio.ottomator.ai Crawl4AI: https://github.com/unclecode/crawl4ai ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 00:00 - Intro to Crawl4AI + n8n 01:45 - Showing off the n8n Workflow 02:31 - What We're Crawling (and Ethics) 04:36 - How to Deploy Crawl4AI for n8n 07:57 - Deploying Crawl4AI with Docker 13:06 - TEN Agent 15:27 - Building Crawl4AI into n8n 29:15 - n8n + Crawl4AI RAG Demo 32:43 - Outro ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Join me as I push the limits of what is possible with AI. I'll be uploading videos at least two times a week - Sundays and Wednesdays at 7:00 PM CDT!

Buckle up - HUGE amount of value in this video for building RAG AI Agents that actually work. Honestly I could have made this video into an entire course but I wanted to give it away to you for free. :) RAG is the most common approach for providing external knowledge to an LLM. The problem is, once you have your own curated data in a vector database as a knowledgebase for your LLM, often times these RAG setups can be very underwhelming. The wrong text is returned from the search, the LLM ignores the context provided, etc. The logic of RAG makes sense in your head but it just doesn’t work in practice. And you certainly aren’t alone! That’s why there is a TON of research in the industry for how to essentially just do RAG better. There are a lot of strategies out there, but out of all the ones I’ve researched and tried myself, agentic RAG is the most obvious, works the best, and is what I’m going to introduce you to and show you exactly how to implement in this video. In the last video on my channel, I showed you how to use Crawl4AI, an open source LLM-friendly web crawler, to scrape entire websites for RAG SUPER fast. We used the entire documentation for my favorite agent framework, Pydantic AI, as an example. Now we’re taking this MUCH further by: 1. Putting all the documentation in a database for RAG 2. Creating an agentic RAG agent to use this knowledgebase with Pydantic AI 3. Building a frontend to chat with our agent using Streamlit I’ll explain exactly what Agentic RAG is, what makes it so powerful, and then this AI agent we’ll build in the video will be the perfect example! ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Try GPUStack for free - it's open source and you can find their GitHub repo here: https://github.com/gpustack/gpustack I don't have the pleasure of being sponsored by open source projects often, so this was a treat! It's the best GPU cluster manager for LLM inference that I have seen and a very honest recommendation! Here is their main site as well: https://gpustack.ai/ Key features of GPUStack: 1. Heterogeneous GPU cluster management including Linux, Mac and Windows with Nvidia, and Apple Silicon. AMD coming soon! 2. Distributed inference with smart scheduling: GPUStack can distribute a big model to multiple heterogeneous workers. Automatically calculates and decide whether distributed inference is required and configure it automatically. 3. Rich model types support: GPUStack supports LLM, VLM, Image Generation, Embedding, Rerank, TTS&STT models. ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Previous video with Crawl4AI: https://youtu.be/JWfNLF_g_V0 All code for this Agentic RAG Agent can be found here: https://github.com/coleam00/ottomator-agents/tree/main/crawl4AI-agent Try this agent yourself right now on the Live Agent Studio (called the "Pydantic AI Expert")! https://studio.ottomator.ai Diagram to follow along with the knowledgebase creation flow: https://claude.site/artifacts/f4dca1c3-f137-4b82-9254-dfa01ca43802 Weaviate Article on Agentic RAG: https://weaviate.io/blog/what-is-agentic-rag ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 00:00 - Agentic RAG - the Holy Grail of RAG 02:18 - What is Agentic RAG? 06:22 - Breaking our Agent Down Step by Step 08:33 - Try this Agent Now for Free 09:00 - Code Overview 09:58 - Crawl4AI Review 10:52 - Creating Our Knowledgebase for Supabase 21:38 - GPUStack 23:33 - Supabase Setup 26:08 - Getting Crawl4AI Data into Supabase 28:09 - Basic RAG AI Agent with Pydantic AI 33:44 - Testing our Basic RAG Agent 36:33 - Agentic RAG Implementation 40:40 - Demo of Our Agentic RAG Agent 41:37 - Streamlit UI 44:53 - Outro ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Join me as I push the limits of what is possible with AI. I'll be uploading videos at least two times a week - Sundays and Wednesdays at 7:00 PM CDT! Sundays and Wednesdays are for everything AI, focusing on providing insane and practical educational value. I will also post sometimes on Fridays at 7:00 PM CDT - specifically for platform showcases - sometimes sponsored, always creative in approach!

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