🚀 Agentic AI Learning Lab

A structured learning repository documenting my journey in Agentic AI, LangGraph, MCP, RAG, and Multimodal AI with hands-on implementations and notes.

📌 What This Repository Covers

This repository contains practical experiments, notes, and projects related to:

• 🤖 Agentic AI Systems
• 🔗 LangGraph & LangChain
• 📚 Retrieval Augmented Generation (RAG)
• 🧠 Agentic RAG
• 🛠️ Model Context Protocol (MCP)
• 🖼️ Multimodal AI
• 🔄 Multi-Agent Systems
• 🐍 Python Implementations

📂 Repository Structure

agentic-ai-learning-lab/
│
├── RAG/
│   ├── basics/
│   ├── advanced/
│   └── projects/
│
├── LangGraph/
│   ├── tutorials/
│   ├── agents/
│   └── multi_agents/
│
├── MCP/
│   ├── servers/
│   ├── tools/
│   └── examples/
│
├── AgenticRAG/
│   ├── pipelines/
│   └── workflows/
│
├── Multimodal/
│   ├── vision/
│   ├── audio/
│   └── text/
│
├── Projects/
│   ├── chatbots/
│   ├── assistants/
│   └── autonomous_agents/
│
├── Notes/
│   ├── concepts.md
│   ├── langgraph.md
│   ├── rag.md
│   └── mcp.md
│
└── README.md


## 🧪 Learning Goals
Build production-grade AI agents using LangGraph

Design scalable RAG pipelines

Implement Agentic workflows

Create MCP servers and tools

Develop multimodal AI applications

Understand debugging & monitoring for agent systems

🛠️ Tech Stack
Python 🐍

LangChain

LangGraph

OpenAI / LLM APIs

Vector Databases (FAISS, Chroma, Typesense, etc.)

FastAPI / Flask

MCP Protocol


## 🎯 Future Plans
Build autonomous research agent

Create multimodal AI assistant

Deploy agent workflows with monitoring

Open-source reusable agent templates

## 🤝 Contributions
This is a personal learning repository, but suggestions and improvements are always welcome!

## ⭐ Connect
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It helps motivate continuous learning and sharing.