LangChain- Develop AI Agents with LangChain & LangGraph | Udemy [Update 11/2025]
English | Size: 9.69 GB
Genre: eLearning[/center]
Learn LangChain and LangGraph by building real world AI Agents (Python, Latest Version 1.0+)
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
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 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 Section
Introduction to LangGraph
Introduction to Model Context Protocol (MCP)
Ice Breaker Agent - An AI agent that searches Google, finds LinkedIn and Twitter profiles, scrapes public info, and generates personalized icebreakers.
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
LangSmith
LangGraph
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 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.
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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