Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
18 Hour(s) 35 Minute(s)
Language
English
Taught by
Eden Marco
Rating
4 views
Course Overview
Learn AI Engineering with LangChain and LangGraph by building real world AI Agents (Python, Latest Version 1.0+)
This course contains the use of artificial intelligence :)
COURSE WAS RE-RECORDED and supports- LangChain Version 1.0+
**Ideal students are software developers / data scientists / AI/ML Engineers**
Welcome to the AI Agents with LangChain and LangGraph Udemy course - Unleashing the Power of Agentic AI!
This course is designed to teach you how to QUICKLY harness AI Engineering, Agent Engineering with the power the LangChain & LangGraph libraries for LLM applications and Agentic AI.
This course will equip you with the skills and knowledge necessary to develop cutting-edge LLM solutions for a diverse range of topics.
Please note that this is not a course for beginners. This course assumes that you have a background in software engineering and are proficient in Python. I will be using Pycharm IDE but you can use any editor you'd like since we only use basic feature of the IDE like debugging and running scripts .
What You’ll Build: No fluff. No toy examples. You’ll build:
Search Agent
Documentation Helper – A chatbot over Python package docs (and any data you choose), using advanced retrieval and RAG.
Slim ChatGPT Code Interpreter – A lightweight code execution assistant.
Prompt Engineering Theory
Context Engineering Theory
Introduction to LangGraph
Model Context Protocol (MCP)
The topics covered in this course include:
AI Agents
Agentic AI
AI Engineering
LangChain, LangGraph
LLM + GenAI History
Prompt Engineering: Few shots prompting, Chain of Thought, ReAct prompting
Context Engineering
Chat Models
Open Source Models
Prompts, PromptTemplates, langchainub
Output Parsers, Pydantic Output Parsers
Chains: create_retrieval_chain, create_stuff_documents_chain
Agents, Custom Agents, Python Agents, CSV Agents, Agent Routers
OpenAI Functions, Tool Calling
Tools, Toolkits
Memory
Vectorstores (Pinecone, FAISS, Chroma)
RAG (Retrieval Augmentation Generation)
DocumentLoaders, TextSplitters
Streamlit (for UI), Copilotkit
LCEL
Agent tracing with LangSmith
Cursor IDE
MCP - Model Context Protocol & LangChain Ecosystem
Introduction To LangGraph
Throughout the course, you will work on hands-on exercises and real-world projects to reinforce your understanding of the concepts and techniques covered. By the end of the course, you will be proficient in using LangChain to create powerful, efficient, and versatile LLM applications for a wide array of usages.
Why This Course?
Up-to-date: Covers LangChain V.1+ and the latest LangGraph ecosystem.
Practical: Real projects, real APIs, real-world skills.
Career-boosting: Stay ahead in the LLM and GenAI job market.
Step-by-step guidance: Clear, concise, no wasted time.
Flexible: Use any Python IDE (Pycharm shown, but not required).
DISCLAIMERS
Please note that this is not a course for beginners. This course assumes that you have a background in software engineering and are proficient in Python.
I will be using Pycharm IDE but you can use any editor you'd like since we only use basic feature of the IDE like debugging and running scripts.
Course Content
- 10 section(s)
- 163 lecture(s)
- Section 1 Introduction
- Section 2 The GIST of LangChain- Get started by with your "Hello World" chain
- Section 3 THE GIST Of AI Agents
- Section 4 The Original LangChain ReAct Agent
- Section 5 Diving Deep Into ReAct Agents- Whats is the magic?
- Section 6 Function Calling
- Section 7 The GIST of RAG- Embeddings, Vector Databases and, & Retrieval
- Section 8 Building a documentation assistant (Embeddings, VectorDBs, Retrieval, Memory)
- Section 9 Prompt Engineering Theory
- Section 10 Let's Talk About LLM Applications In Production
What You’ll Learn
- Become proficient in LangChain
- Have end to end working LangChain based generative AI agents
- Prompt Engineering Theory: Chain of Thought, ReAct, Few Shot prompting and understand how LangChain is build under the hood
- Context Engineering
- Understand how to navigate inside the LangChain opensource codebase
- Large Language Models theory for software engineers
- LangChain: Lots of chains Chains, Agents, DocumentLoader, TextSplitter, OutputParser, Memory
- RAG, Vectorestores/ Vector Databases (Pinecone, FAISS)
- Model Context Protocol (MCP)
- LangGraph
Reviews
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AAkshay katti
its good for basic understanding, but would have loved more real scenario based agent examples unlike add, multiply examples, repeatitive weather examples. but thanks for curating this. helps a lot.
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JJoaquín Guzmán
At the beginning I was struggling with some of the code in this course, but while I was watching the lessons and building my demo models, the workflow started to make sense. I'm finishing this course with a big smile, thank you.
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WWilliam Fernandes Dias
It is good. He explain the concepts very well, but think the projects are to simple and they lack a broader variety of tools and integrations with external systems.
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GGabriele Farace
Very bad course. I feel like I am more confused now than when I started this course. Where do I even begin? 1) The structure of the course is poor. The lectures are not in a natural order and many times the instructor tells you that he will explain later a concept that he introduce but then he never explains anything. Sometimes he uses high level modules without explaining in details what happens under the hood (except for the react agent which he develop from scratch). The imports and the code sometimes change from a lecture to the next because of the framework changes, However all these updates make it difficult to follow a precise flow and if you try the code on your laptop most of the times it will not work. 2) You can't just simply edit the video of the lectures and leave it as it is. If something change you should prepare a new video. And if a video included in a series that explain something change, you should mantain the entire old series and start creating a new one (with a NEW tag perhaps) and when it's finished you can upload it entirely. If you change a single video then there are new and old video and it only creates confusion. And I don't even want to start talking about the entire mess created by the consistent changes in package managers, code editors and voice pitch that make you have to adjust the headphone volume and so on. 3) There are lectures that literally end while the instructor is still explaining something, please fix that. 4)The last 4 lectures on MCP are confusing. First the instructor tells you that we are going to use MCP langchain adapter but then he uses the mcp library instead I convinced myself that this was a good course by reading the reviews and considering the number of students who were enrolled, but I must say that I am very disappointed. It's a pity because I can tell that the instructor is very smart and knows his stuff and the teaching is not so bad, however the structure makes it extremely confusing . Also, I rate this course 2 stars and not 1 because all the sections about Langgraph are very well structured and you can actually understand something, and I hope that the instructor take this review in a constructive manner to help him improve the materials. If I see some changes I may update my review later.
