LangChain- Develop AI Agents with LangChain & LangGraph

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 - Content 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.

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