Description
“AI Agents with LangGraph & MCP: Build RAG & Multi-Agents” is a course on using LangGraph, the Model Context Protocol (MCP), and Retrieval-Augmented Generation (RAG), published by the online learning platform Udemy. You will discover how to design AI agents capable of reasoning about tasks, interacting with external tools, retrieving relevant information, and orchestrating complex workflows. You will learn how LangGraph supports stateful agent orchestration, how the MCP enables standardized connections between AI models and external services, and how RAG systems augment language models with relevant, context-specific data. The course also covers multi-agent architectures, tool integration, workflow management, and practical techniques for developing AI systems capable of handling increasingly complex tasks.
Whether you are a Python developer, an AI engineer, or a technology enthusiast, this course provides actionable insights into building scalable AI agents and knowledge-driven applications using modern agentic AI frameworks and technologies. Throughout the course, you will build an agent loop from scratch, generate structured output from language models, implement a search system for RAG, connect agents to tools and data via the MCP, design memory and multi-agent systems, evaluate agents using test suites, secure them against prompt injection, and finally, deploy an agent using FastAPI and Docker. This path provides a deep, practical understanding of how these concepts are applied in real-world scenarios, preparing you to build professional and scalable AI agent systems.
What you will learn in AI Agents with LangGraph & MCP: Build RAG & Multi-Agents:
- Build a working AI agent loop from scratch in Python: tool calling, observations, step limits and error recovery.
- Get reliable structured output from LLMs with JSON schemas, Pydantic validation and repair loops.
- Build search from scratch (BM25, embeddings, vector search, hybrid search) and use it for retrieval augmented generation (RAG).
- Connect agents to tools and data with the Model Context Protocol (MCP), writing both a server and a client.
- Design memory, planning and multi-agent systems, and know when a single agent is the better choice.
- and …
Course specifications
Publisher: Udemy
Instructors: Vaibhav Kumar Singh
Language: English
Level: Introductory to Advanced
Number of Lessons: 56
Duration: 2 hours and 6 minutes
Course topics
AI Agents with LangGraph & MCP: Build RAG & Multi-Agents Prerequisites
Basic Python: functions, classes, dictionaries and lists
A laptop with about 8 GB of RAM. No GPU and no paid API key required
Optional: a Claude API key, if you want faster runs
Pictures
AI Agents with LangGraph & MCP: Build RAG & Multi-Agents introduction video
Installation guide
After Extract, watch with your favorite Player.
English subtitle
Quality: 1080p
Downloadly link
Rapidgator link
File password (s): www.downloadly.ir
Size
1.7 GB