Description
“Build AI Agents in Python: LangChain, LangGraph, MCP & RAG” is a course on building intelligent AI agents using Python and modern AI development frameworks, published by the online academy Udemy. You will learn how to create applications capable of understanding user requests, retrieving relevant information, interacting with external tools, and executing multi-step tasks with a high degree of autonomy. The course covers LangChain for connecting language models to tools and data sources; LangGraph for designing stateful agent workflows; the Model Context Protocol (MCP) for integrating AI systems with external services; and Retrieval-Augmented Generation (RAG) for grounding model responses in relevant documents and knowledge bases. Through hands-on projects, you will learn how to design agent architectures, manage conversational state, build retrieval pipelines, integrate APIs, implement tool calling, and orchestrate complex workflows.
Additionally, you will gain insights into prompt engineering, error handling, and techniques for enhancing the reliability and maintainability of AI applications. Whether you are a Python developer, an AI enthusiast, or an aspiring AI engineer, this course provides a practical foundation for building sophisticated AI agents that combine language models, external tools, and custom knowledge to solve real-world problems. Throughout the course, you will build six functional agents, manually implement a complete RAG pipeline, master LangGraph, develop your own MCP server and client, and construct an agentic RAG system featuring query routing and rewriting. You will also learn to debug agents using LangSmith traces. This provides a deep, practical understanding of how these concepts apply to real-world scenarios, preparing you to build professional and reliable AI agent systems.
What you will learn in Build AI Agents in Python: LangChain LangGraph MCP & RAG:
- Build six working AI agents in Python from scratch. You type every line yourself, so you understand them and can fix them when they break.
- Build a complete RAG pipeline by hand: chunking, embeddings, ChromaDB, retrieval, and short-term and long-term memory.
- Master LangGraph: typed state, parallel branches, conditional routing, cycles, checkpointers, and human approval.
- Write your own MCP server and client, plug them into Claude Desktop, and combine them with the Filesystem and Notion community servers.
- Build an agentic RAG that routes its own queries, rewrites bad ones, retries failed retrieval, filters by metadata, and audits a whole document.
- and …
Course specifications
Publisher: Udemy
Instructors: Dr. Amar Massoud
Language: English
Level: Introductory to Advanced
Number of Lessons: 78
Duration: 13 hours and 21 minutes
Course topics
Build AI Agents in Python: LangChain LangGraph MCP & RAG Prerequisites
Any Operating System (Windows, macOS or Linux), with 16 GB RAM recommended (8 GB is fine on the cloud path).
Basic programming in any language. Java, C#, Go, JavaScript or PHP is fine. Python is not a prerequisite. Every Python feature is explained before it gets typed.
No AI, ML or maths background. We never train a model. No paid API key. Everything runs on Ollama, and a cloud key is optional.
Pictures
Build AI Agents in Python: LangChain LangGraph MCP & RAG introduction video
Installation guide
After Extract, watch with your favorite Player.
English subtitle
Quality: 1440p
Downloadly link
Rapidgator link
File password (s): www.downloadly.ir
Size
10.7 GB