Learn Agentic AI: From Basics to Advanced Multi-Agent Systems - L-18 Agentic RAG with Agno

Learn how to build **smarter AI with Agentic RAG**! In this video, I break down what **Agentic RAG** is, how it differs from traditional **RAG (Retrieval-Augmented Generation)**, and why combining **Agentic AI with RAG** makes your AI more powerful and intelligent. 📥 Download the complete source code: 🔗 GitHub: https://github.com/codewithaarohi/Agentic-AI-Course/tree/main/AgenticRAG 🔗 Agno Framework: https://docs.agno.com/introduction 📧 Email: [email protected] 📌 **What We’ll Cover:** ✅ What RAG is and why it’s important ✅ What Agentic AI is and how it plans, reasons, and uses tools ✅ How Agentic RAG combines planning, reasoning, and tools with RAG ✅ How to implement Agentic RAG using the **Agno Framework** ✅ Real-world examples with **knowledge bases & vector databases**: • TextKnowledgeBase • WebsiteKnowledgeBase • ArxivKnowledgeBase (fetch latest research papers from arXiv) • Vector databases like **pgvector** and **Chroma** By the end of this tutorial, you’ll understand how to **leverage Agentic RAG for smarter AI workflows** and practical applications. 🔔 Don’t forget to **like, comment, and subscribe** for more tutorials on **Agentic AI, RAG, and Agno**! #AgenticRAG #RAG #RetrievalAugmentedGeneration #AgenticAI #AgnoFramework #AIWorkflow #KnowledgeBase #VectorDatabase #ArxivKnowledgeBase #AIPlanning #AIResearch