LangChain- Develop AI Agents with LangChain & LangGraph | Udemy [Update 07/2025]
English | Size: 7.92 GB
Genre: eLearning[/center]

Learn LangChain and LangGraph by building real world AI Agents (Python, Latest Version 0.3.0+)

What you'll learn
Become proficient in LangChain
Have 3 end to end working LangChain based generative AI applications
Prompt Engineering Theory: Chain of Thought, ReAct, Few Shot prompting and understand how LangChain is build under the hood
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 Databasrs (Pinecone, FAISS)
Model Context Protocol
LangGraph

COURSE WAS RE-RECORDED and supports- LangChain Version 0.3+

**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 LLM!
This  course is designed to teach you how to QUICKLY harness the power the LangChain library for LLM applications.
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:

Ice Breaker Agent - An AI agent that searches Google, finds LinkedIn and Twitter profiles, scrapes public info, and generates personalized icebreakers.

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 Section

Introduction to LangGraph

Introduction to Model Context Protocol (MCP)

The topics covered in this course include:

AI Agents

LangChain, LangGraph

LLM + GenAI History

LLMs: Few shots prompting, Chain of Thought, ReAct prompting

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

LangSmith

LangGraph

FireCrawl

GIST of Cursor IDE

Cursor Composter

Curser Chat

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 v0.3+ 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.

The Ice-Breaker project requires usage of 3rd party APIs-
Scrapin, Tavily, Twitter API  which are generally paid services.
All of those 3rd parties have a free tier we will use to create stub responses development and testing.

Who this course is for:
Software Engineers that want to learn how to build Generative AI based applications with LangChain and LangGraph
Developers that want to learn how to build Generative AI based applications with LangChain and LangGraph
Engineers that want to learn how to build Generative AI based applications with LangChain and LangGraph

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