LangChain- Agentic AI Engineering with LangChain & LangGraph

s in Python 07:00 Understanding the ReAct Prompt: Building AI Agents Without Function Calling 08:05 Implementing Manual Tool Calling for LLMs 06:43 Agent Loop With ReAct Prompt 15:26 Agents Interview Function Calling 2 lectures • 9min Intro 02:04 [Theory] Understanding Function Calling for LLMs 07:04 The GIST of RAG- Embeddings, Vector Databases and, & Retrieval 9 lectures • 1hr 45min Introduction to Retrieval Augmentation Generation (RAG) 07:46 Introduction to RAG Implementation 13:58 Medium Analyzer- Boilerplate Project Setup 12:46 Medium Analyzer- Class Review: TextLoader,TextSplitter,OpenAIEmbeddings,Pinecone 09:00 Medium Analyzer- Ingestion Implementation 15:01 RECAP 01:38 Medium Analyzer- Naive Retrieval Implementation Implementation 17:53 Medium Analyzer- 2 Step RAG 16:58 LangChain RAG Documentation 09:48 RAG Implementation with Vector Stores Quiz 7 questions Building a documentation assistant (Embeddings, VectorDBs, Retrieval, Memory) 16 lectures • 2hr 13min What are we building? A lightweight Cursor/ Feature Feature (RAG) Preview 02:12 Quick Note: Pipenv vs uv 00:26 Environment Setup 05:33 Ingestion Pipeline Intro 02:18 Imports 13:08 Tavily Crawling 12:27 [Optional] TavilyMap, TavilyExtract for High customizability 12:19 [Optional] Crawling Deep Dive 16:48 Recap 01:06 Chunking (Text Splitting) 04:37 Batch Indexing 11:28 Retrieval Agent Implementation 12:45 Run, Debug, Trace RAG Agent 08:21 "Frontend" with Streamlit (UI) 19:24 Documentation Helper In Production 06:28 RAG Architecture 04:04 Prompt Engineering Theory 9 lectures • 58min The GIST of LLMs 03:52 What is a Prompt? Composition of a formal prompt 02:55 Zero Shot Prompting 02:42 Few Shot Prompting 08:26 Chain of Thought Prompting 08:33 ReAct Prompting 07:18 Prompt Engineering Quick Tips 08:59 Context Engineering 05:20 Context Engineering a System Prompt 10:20 Let's Talk About LLM Applications In Production 9 lectures • 1hr LLM Applications in Production 08:33 LLM Application Development landscape 04:01 LLMs in Production: Privacy & Data Retention 08:41 Generative UI/UX featuring CopilotKit 04:51 Official LangChain Academy Courses 02:22 Open Source LLMs VS Managed LLM Providers (Deepseek) 09:05 Confidence in AI Results By Assaf Elovic & Harrison Chase 05:00 [NEW] AI FOMO is the New Normal 11:09 Finished course? Whats next! 06:00 -------------------Introduction To LangGraph ------------------- 13 lectures • 1hr 23min What is LangGraph? 04:56 Why LangGraph? LangGraph VS LangChain 16:42 What are Graphs? 03:36 LangGraph & Flow Engineering 06:03 LangGraph Core Components 05:17 --------- [Hands On] Implementing ReAct AgentExecutor with LangGraph --------- 02:20 Quick Note: poetry vs uv 00:48 [Hands On] Get Started: Setting Up Your ReAct Agent Project Environment 05:09 [Hands On] Coding the Agent's Brain: Implementing the ReAct Runnable 08:28 [Hands On] 43. Building Blocks: Defining Your Agent's Nodes in LangGraph 05:52 [Hands On] Bringing Your ReAct Agent to Life: Connecting Nodes into a Graph 07:21 [Hands On] Running Our LangGraph React Agent with Function Calling 07:45 [IMPORTANT] Building Modern LLM Agents: From History to LangGraph v1.0 08:49 Reflection Agent 5 lectures • 33min What are we building? A Reflection Agent 01:26 Project Setup 04:44 Creating the Reflector Chain and the Tweet Reviosr Chain 04:48 Defining our LangGraph Graph 19:17 LangSmith Tracing 03:09 Reflexion Agent 8 lectures • 58min What are we building? A Reflexion Agent 06:20 Project Setup 03:42 Section Resources 00:47 Actor Agent V2 16:40 Revisor Agent 04:17 ToolNode - Executing Tools 07:10 Building Our LangGraph Graph 11:54 Tracing Our Graph 07:22 Agentic RAG 14 lectures • 1hr 26min What are Building In this Section- Agentic RAG Architecture 02:39 Improving RAG Quality with the Corrective RAG Flow 01:24 Boilerplate Setup for an Agentic RAG Agent with LangGraph 04:33 Code Structure 07:25 LangChain Vector Store Ingestion Pipeline (WebLoader, ChromaDB) 06:54 Managing Information Flow in LangGraph: The GraphState 02:08 Fetching Context for LLMs: The LangGraph Retrieve Node 02:04 Building a Relevance Filter for RAG using LangChain's Structured Output 12:53 Implementing a Web Search Node in LangGraph using Tavily API 04:53 Creating the LLM Generation Chain and Node for LangGraph 05:04 Building and Running the Complete LangGraph Agent 07:40 Self RAG- Intro 01:30 Self RAG- Implementation 15:33 Adaptive RAG 11:34 -----------------Introduction to Model Context Protocol (MCP)------------------- 5 lectures • 35min [Theory] Why MCP (Model Context Protocol) 04:41 [Theory] How LLMs REALLY Use Tools: Understanding Tool Calling 05:06 [Theory] MCP Architecture 07:20 [Theory] The GIST of the Protocol with Tool Calling 09:00 [Theory] MCP Servers 08:44 MCP Quiz 9 questions Using a Pre-built Server (mcpdoc) with AI Clients (Cursor & Claude) 4 lectures • 22min What are we building? MCP Doc 01:11 MCP Inspector 02:54 LLM.txt 05:33 mcpdoc 12:13 Building MCP Servers and Clients with LangChain 9 lectures • 50min Intro 00:39 Boilerplate 10:01 Servers 05:43 What are we MCBuilding? 01:10 Simple MCP Server 02:42 Bridging the Gap: The LangChain MCP Adapter Explained 04:23 Imports 08:46 Client 08:45 Tracing 08:13 Useful tools when developing LLM Applications 4 lectures • 19min [New, Important] Stop Writing Deprecated Code: LangChain's Official MCP Server 08:00 LangChain Hub - Downloads prompt from the community 04:08 TextSplitting Playground 03:52 LangChain VS LlamaIndex 02:45 Deep Agents 6 lectures • 34min Introduction to Deep Agents Section 01:21 Deep Agents: Taxonomy of Agents: Shallow Agents, Deep Agents, Coding Agents 11:11 Deep Agents: How Deep Agents Use Dynamic To-Do Lists to Solve Complex Tasks 02:48 Deep Agents: Sub Agents and Hierarchical Delegation 06:12 Deep Agents: Subagents context flow 06:02 Deep Agents File Systems 06:31 Deep Agents Skills 5 lectures • 33min The 3 Layers of AI Agent Skills: From Usage to Source Code 01:56 Level 1: Using Agent Skills in the Deep Agents CLI 07:57 Layer 2: Tracing AI Agent Skills with LangSmith 12:45 RECAP (How LangChain Deep Agents Implement Skill Middleware) 01:46 Layer 3: Inside skills.py- The Mechanics of Progressive Disclosure 08:13 Agent Skills Quiz 5 questions LangChain Glossary 7 lectures • 39min ChatModels 08:49 Messages 04:08 RecursiveCharacterTextSplitter 01:55 Document 01:23 LangChain Token Limitation Handeling Strategies 11:29 LangChain Memory Intro- Co Reference Resolution 03:10 LangChain Memory Theory Deepdive (LangGraph) 08:31 Industry Insights: Building Production Agents with Assaf Elovic 3 lectures • 8min The Core Architecture of Production-Grade AI 03:19 How to Make Users Trust Your AI Agents 02:48 Tutorial: Building a Lean AI Feedback Loop 01:45 Industry Insights: Building Production Agents with Roy Miara 4 lectures • 15min Intro 02:14 AI Agents in Cybersecurity CTF Competitions 04:39 Harness Engineering 04:41 Managing Variance and Hallucinations in Production Agents 03:21 Agent Security 1 lecture • 4min What is LLM App Sec? 03:44 The Dark Side of "Vibe Coding": Vulnerabilities in AI-Generated Apps 6 lectures • 27min Introduction 04:51 AI Coding Rarely Write SQL Injections or XSS Bugs 04:07 AI Agents Struggle with Role-Based Access Control 04:20 AI Coding Agents Struggle with Business Logic and SSRF 05:51 AI Coding Agents Struggle with Rate Limiting and CSRF 04:23 Prompt Engineering Won't Fix Insecure AI Code 03:57 Bonus 1 lecture • 1min Bonus 00:22 Requirements This is not a beginner course. Basic software engineering concepts are needed I assume students will be familiar software engineering subjects such as: git, python, pipenv, environment variables, classes, testing and debugging No Machine Learning experience is needed. Description This course contains the use of artificial intelligence :) 2026- COURSE WAS RE-RECORDED and supports- LangChain Version 1.2+ **Ideal students are software developers / data scientists / AI/ML Engineers** Welcome to the Agentic AI Engineering with LangChain and LangGraph course. In this course you will learn how to design and build AI agents and agentic AI systems using LangChain and LangGraph, the most powerful frameworks for developing modern LLM applications. Agentic AI Engineering focuses on building AI systems that can reason, plan, use tools, and autonomously complete tasks. With LangChain and LangGraph, you will build production-ready AI agents, RAG systems, and advanced LLM applications.

What is LangChain? LangChain is an open-source development framework designed to simplify creating applications powered by large language models (LLMs). Using LangChain, LangGraph, MCP, and modern LLM frameworks, you will build production-ready AI agents, multi-agent systems, and advanced RAG applications. 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 . You will build real-world Agentic AI systems using LangChain and LangGraph: Search Agent Documentation Helper – A chatbot over Python package docs (and any data you choose), using advanced retrieval and RAG. Prompt Engineering Theory Context Engineering Theory Introduction to LangGraph Model Context Protocol (MCP) Deep Agents Agentic AI Engineering Topics Covered: Agentic AI Fundamentals AI Agents Agentic AI architectures Multi-agent systems AI engineering principles LLM and Prompt Engineering Prompt Engineering Few-Shot Prompting Chain of Thought ReAct prompting Context Engineering Agent Frameworks LangChain LangGraph Model Context Protocol (MCP) Tool Calling AI Agent Infrastructure Vector databases (Pinecone, FAISS, Chroma) Retrieval Augmented Generation (RAG) Memory systems LangSmith tracing 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). This course is ideal for developers who want to learn Agentic AI Engineering, AI agents with Python, and LLM application development. You will learn how to design agent architectures, implement tool-using agents, and build scalable agentic AI systems using LangChain and LangGraph. 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.

Eden Marco LLM Specialist @ Google Cloud I’m a passionate Software Engineer with years of experience in back-end development and cloud architecture. I was one of the first engineers at Orca Security, where I helped shape the company’s core technology, and today I work as a GenAI Architect at Google Cloud, helping organizations design and deploy advanced generative AI and cloud-native solutions. I’m also proud to be a LangChain Ambassador, actively contributing to the open-source community and helping developers build powerful LLM applications using the LangChain ecosystem. I hold a Bachelor’s degree in Computer Science from the Technion, and I’ve always had a deep passion for teaching and mentorship. I taught Functional Programming and Introduction to Computer Science at Reichman University, where I guided the next generation of software engineers. My courses are built on real-world experience and designed to give you practical, production-ready skills—whether you’re just getting started or looking to level up.