L-14 | Agno (Phidata) Explained: Fast & Multimodal Agentic AI Framework | Agentic AI Tutorial

Learn everything about the **Agno framework** and how to build **multi-modal AI agents** in this detailed tutorial! Discover why Agno is different from LangChain, LangGraph, and CrewAI, and see how it enables **ultra-lightweight agents** in microseconds. 📥 Download the complete source code: 🔗 GitHub: https://github.com/codewithaarohi/Agentic-AI-Course/tree/main/L14_agno 🌐 Website: https://codewithaarohi.ai/downloads/agentic_ai/ Both links contain the exact code used in this tutorial. 📌 **What You’ll Learn:** 1️⃣ What Agno is and why it stands out from LangChain, LangGraph, and CrewAI 2️⃣ How Agno creates agents in **microseconds** 3️⃣ Why Agno is **incredibly lightweight** (6.5 KiB per agent!) 4️⃣ How to build **multi-modal AI agents** using Agno (text, images, audio, video) 5️⃣ Comparison with LangGraph: building **image and audio-based agents** 6️⃣ Real-world use cases like a **Healthcare Assistant** and **Audio Transcription Agent** 💬 Questions or need assistance? Contact me at **aarohisingla1987@gmail.com** 🔔 Don’t forget to **like, comment, and subscribe** for more tutorials on **Agentic AI frameworks and generative AI**! #AgenticAI #GenerativeAI #AgnoFramework #MultiModalAI #LightweightAI #AIAgents #LangChainVsAgno #LangGraphVsAgno #AIWorkflow #PythonAI