Agentic AI

L-8 (English Channel) and L-6 (Hindi Channel) 🧠 What You’ll Learn: ✅ What is a Simple Reflex Agent ✅ How reflex agents work without memory ✅ If-Then rule logic in agent decision-making ✅ Visualizing the agent's behavior using Matplotlib ✅ Step-by-step coding and simulation ✅ Updating environment states dynamically

L-9 (English Channel) and L-7 (Hindi Channel) 📌 **What You’ll Learn in This Video:** ✅ What a Goal-Based Agent is and how it works ✅ How it differs from **Reflex Agents** and **Model-Based Agents** ✅ Real-world examples of Goal-Based Agents ✅ Step-by-step coding demo to build a simple Goal-Based Agent in Python ✅ Complete understanding by combining **theory + practical demo**

L-13 (English Channel) and L-11 (Hindi Channel) We’ll cover: ✅ What is a Learning Agent? ✅ Fundamentals of Q-Learning and how it works ✅ The Epsilon-Greedy strategy ✅ How to initialize and update a Q-Table ✅ Step-by-step Python implementation of Q-Learning in a simple environment

L-14 (English Channel) and L-12 (Hindi Channel) Agentic AI framework | LangGraph What you’ll learn in this video: 1️⃣ What Agentic AI means and how it works 2️⃣ Introduction to LangGraph and its key components: Node, Edge, State, State Machine 3️⃣ Step-by-step guide to building a chatbot using LangGraph 4️⃣ How to create and deploy an Agent in LangGraph 5️⃣ Designing and using custom tools 6️⃣ Connecting multiple tools to a single agent 7️⃣ Building a complete system with multiple agents 8️⃣ Real-world use cases and insights into design decisions

L-15 (English Channel) and L-13 (Hindi Channel) 📌 **What You’ll Learn:** ✅ What CrewAI is and how it works ✅ How agents simulate real-world team roles in an organization ✅ Setting up AI-powered agents for research, writing, and automation ✅ Using web search tools to gather information dynamically ✅ Automating blog writing and structured workflows

L-16 (English Channel) and L-14 (Hindi Channel) Download ZIP 📌 **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**

L-17 (English Channel) and L-15 (Hindi Channel) 📌 **What’s Covered in This Video:** ✅ Setting up your first AI agent ✅ Using predefined and custom tools to enhance agent capabilities ✅ Enabling memory for **contextual conversations** and advanced interactions

L-18 (English Channel) and L-16 (Hindi Channel) (Agentic RAG Explained with Agno) 📌 **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**

L-20 (English Channel) and L-19 (Hindi Channel) 📌 **What You’ll Learn:** ✅ Quick recap — What is MCP and why it matters ✅ How to set up and run a **Weather MCP Server** locally ✅ Connecting an **MCP client** to the server and fetching live data ✅ Running two MCP servers simultaneously — Weather + Calculator ✅ How MCP makes **tool discovery and swapping** easy for AI agents ✅ Local vs Hosted MCP servers — when to use each

L-21 (English Channel) and L-19 (Hindi Channel) 📌 **What You’ll Learn:** ✅ What an MCP server is and why you might want to build a custom one ✅ How to create tools using **FastMCP** ✅ How to run your MCP server over **HTTP or stdio** ✅ How to connect MCP tools to **LangGraph** ✅ How to make an **LLM use MCP tools automatically**

L-22 (English Channel) and L-20 (Hindi Channel) **What You’ll Learn in This tutorial:** ✅ Setting up a brand-new VPS (Hostinger) ✅ Installing required packages (Python, pip, etc.) ✅ Writing a custom MCP server directly on the cloud ✅ Running the MCP server on the cloud ✅ Connecting a local MCP client to the cloud server using LangGraph ✅ Testing the full setup end-to-end: local requests → cloud responses

L-23 (English Channel) and L-21 (Hindi Channel) In this tutorial, we’ll learn how to use MCP servers with Agno in a practical, step-by-step way. You’ll see how MCP clients and servers work together, starting from ready-made servers and moving towards building your own.

L-24 (English Channel) and L-22 (Hindi Channel) Build **Agentic AI applications** step by step using the **OpenAI Agents SDK** in this in-depth tutorial! Learn how to create your first AI agent and scale to advanced **multi-agent systems** with practical examples and hands-on guidance.

L-25 (English Channel) and L-23 (Hindi Channel). Learn how to use the **Model Context Protocol (MCP)** with the **OpenAI Agents SDK** in this hands-on Python tutorial!