LangChain- Agentic AI Engineering with LangChain & LangGraph

<div>This course contains the use of artificial intelligence&nbsp;</div><div><br></div><div>20206- COURSE WAS RE-RECORDED and supports- LangChain Version 1.2+</div><div><br></div><div>Ideal students are software developers / data scientists / AI/ML Engineers</div><div><br></div><div>Welcome to the <u><b>AI Agents with LangChain and LangGraph Udemy course</b></u> - Unleashing the Power of Agentic AI!</div><div>This&nbsp; course is designed to teach you how to QUICKLY harness AI Engineering, Agent Engineering with the power the LangChain &amp; LangGraph libraries for LLM applications and Agentic AI.</div><div>This course will equip you with the skills and knowledge necessary to develop cutting-edge LLM solutions for a diverse range of topics.</div><div><br></div><div>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 .</div><div><br></div><div>What You’ll Build:&nbsp; No fluff. No toy examples. You’ll build:</div><div><ul><li><span style="font-size: 1rem;">Search Agent</span></li><li><span style="font-size: 1rem;">Documentation Helper – A chatbot over Python package docs (and any data you choose), using advanced retrieval and RAG.</span></li><li><span style="font-size: 1rem;">Prompt Engineering Theory</span></li><li><span style="font-size: 1rem;">Context Engineering Theory</span></li><li><span style="font-size: 1rem;">Introduction to LangGraph</span></li><li><span style="font-size: 1rem;">Model Context Protocol (MCP)</span></li><li><span style="font-size: 1rem;">Deep Agents</span></li></ul></div><div><br></div><div>The topics covered in this course include:</div><div><ul><li><span style="font-size: 1rem;">AI Agents</span></li><li><span style="font-size: 1rem;">Agentic AI</span></li><li><span style="font-size: 1rem;">AI Engineering</span></li><li><span style="font-size: 1rem;">LangChain, LangGraph</span></li><li><span style="font-size: 1rem;">LLM + GenAI History</span></li><li><span style="font-size: 1rem;">Prompt Engineering: Few shots prompting, Chain of Thought, ReAct prompting</span></li><li><span style="font-size: 1rem;">Context Engineering</span></li><li><span style="font-size: 1rem;">Chat Models</span></li><li><span style="font-size: 1rem;">Open Source Models</span></li><li><span style="font-size: 1rem;">Prompts, PromptTemplates, langchainub</span></li><li><span style="font-size: 1rem;">Output Parsers, Pydantic Output Parsers</span></li><li><span style="font-size: 1rem;">Chains: create_retrieval_chain, create_stuff_documents_chain</span></li><li><span style="font-size: 1rem;">Agents, Custom Agents, Python Agents, CSV Agents, Agent Routers</span></li><li><span style="font-size: 1rem;">OpenAI Functions, Tool Calling</span></li><li><span style="font-size: 1rem;">Tools, Toolkits</span></li><li><span style="font-size: 1rem;">Memory</span></li><li><span style="font-size: 1rem;">Vectorstores (Pinecone, FAISS, Chroma)</span></li><li><span style="font-size: 1rem;">RAG (Retrieval Augmentation Generation)</span></li><li><span style="font-size: 1rem;">DocumentLoaders, TextSplitters</span></li><li><span style="font-size: 1rem;">Streamlit (for UI), Copilotkit</span></li><li><span style="font-size: 1rem;">LCEL</span></li><li><span style="font-size: 1rem;">Agent tracing with LangSmith</span></li><li><span style="font-size: 1rem;">Cursor IDE&nbsp;</span></li><li><span style="font-size: 1rem;">MCP - Model Context Protocol &amp; LangChain Ecosystem</span></li><li><span style="font-size: 1rem;">Introduction To LangGraph</span></li><li><span style="font-size: 1rem;">Deep Agents</span></li><li><span style="font-size: 1rem;">ReAct</span></li></ul></div><div><br></div><div>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.</div><div><br></div><div>Why This Course?</div><div><ul><li><span style="font-size: 1rem;">Up-to-date: Covers LangChain V.1+ and the latest LangGraph ecosystem.</span></li><li><span style="font-size: 1rem;">Practical: Real projects, real APIs, real-world skills.</span></li><li><span style="font-size: 1rem;">Career-boosting: Stay ahead in the LLM and GenAI job market.</span></li><li><span style="font-size: 1rem;">Step-by-step guidance: Clear, concise, no wasted time.</span></li><li><span style="font-size: 1rem;">Flexible: Use any Python IDE (Pycharm shown, but not required).</span></li></ul></div><div><br></div><div>DISCLAIMERS</div><div><br></div><div>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.</div><div>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.</div>

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