LangChain- Develop AI Agents with LangChain & LangGraph

Table of Contents

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

Preview this Course

GET COUPON CODE

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

Context Engineering

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.