kingers Posted May 13 Report Share Posted May 13 Complete Agentic Ai Bootcamp With Langgraph And Langchain Published 5/2025 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 26.69 GB | Duration: 28h 42mLearn to build real-world AI agents, multi-agent workflows, and autonomous apps with LangGraph and LangChain What you'll learn Understand the core principles of Agentic AI and how to design intelligent, autonomous agents for real-world tasks. Master building AI agents using LangGraph, including creating workflows, managing agent state, memory, and event-driven behavior. Develop and deploy multi-agent collaborative systems that can communicate, reason, and solve complex problems together. mplement hands-on projects to create powerful agentic applications like autonomous research agents, task automation systems, and knowledge retrieval assistants. Requirements Basic knowledge of Python programming (variables, functions, classes). Understanding of APIs and RESTful services (basic level). Familiarity with Large Language Models (LLMs) concepts (like OpenAI, Hugging Face models, etc.). Curiosity and willingness to build real-world AI applications - no prior experience with LangGraph needed! Description Are you excited about the future of AI where intelligent agents can think, act, and collaborate to solve complex tasks autonomously? Welcome to the Complete Agentic AI Bootcamp with LangGraph and LangChain - your one-stop course to master the art of building agentic AI applications from scratch!This course is designed to teach you everything you need to know about Agentic AI, LangGraph, and LangChain - two of the most powerful frameworks for building intelligent AI agents and multi-agent systems.You will start by understanding the fundamentals of Agentic AI - how it differs from traditional AI models, the key components of agents (memory, tools, decision-making), and real-world use cases.We will then dive deep into LangGraph, a cutting-edge framework that helps you design complex agent workflows using graphs, events, and state transitions. You'll also learn how to combine LangChain's power with LangGraph to build production-ready agent applications.Throughout the course, you will build real-world projects step-by-step, including:Creating single intelligent agents with memory and tool-usage capabilities.Designing multi-agent collaboration systems with message passing and shared goals.Implementing autonomous research assistants, task automation bots, and retrieval-augmented generation (RAG) agents.You will not just learn theory - you will build and deploy multiple end-to-end agentic applications, gaining real-world experience in constructing powerful AI systems.By the end of this course, you will have the skills and confidence to create your own AI agents and deploy complex agentic applications for various domains like search, research, task planning, customer support, and beyond.What You Will Learn:Core concepts behind Agentic AI and how intelligent agents operate.Hands-on mastery of LangGraph and LangChain for building agent systems.Building autonomous, event-driven AI workflows with memory, reasoning, and tools.Deploying and optimizing single-agent and multi-agent applications.Real-world project experience with RAG agents, auto-research agents, and more.Why Take This Course?Hands-on, Project-Based Learning: Build actual AI agent applications, not just toy examples.Complete and Beginner-Friendly: Designed to take you from beginner to advanced agent builder.Real-World Skills: Learn techniques that companies are starting to use for next-generation AI products.Cutting-Edge Technologies: Master the latest innovations in AI agent orchestration with LangGraph and LangChain.If you are a developer, data scientist, AI/ML engineer, or tech enthusiast looking to future-proof your skills and build cutting-edge AI applications, this is the course for you!Enroll now and start building the future with intelligent AI agents today! Overview Section 1: Introduction To the Course Lecture 1 Welcome Section 2: Installation Of Anaconda And VS Code IDE Lecture 2 Installation Of Anaconda And VS Code Editor Lecture 3 Creating Virtual Environments Using Conda Lecture 4 Creating Virtual Environments Using UV Package Manager Section 3: Python Prerequisites Lecture 5 Getting Started With VS Code Lecture 6 Python Basics- Syntax And Semantics Lecture 7 Variables In Python Lecture 8 Basic Datatypes In Python Lecture 9 Operators In Python Lecture 10 Conditional Statements(if,elif,else) Lecture 11 Loops In Python Lecture 12 List And List Comprehension In Python Lecture 13 Practical Exmaples Of List Lecture 14 Sets In Python Lecture 15 Dictionaries In Python Lecture 16 Tuples In Python Lecture 17 Getting Started With Functions Lecture 18 More Coding Examples With Functions Lecture 19 Python Lambda Funbction Lecture 20 Maps Functions Python Lecture 21 Filter Function In Python Lecture 22 Import Modules And Package In Python Lecture 23 Standard Library Overview Lecture 24 File Operation In Python Lecture 25 Working With File Paths Lecture 26 Exception Handling Lecture 27 Classes And Objects In Python Lecture 28 Inheritance In OOPS Lecture 29 Polymorphism In OOPS Lecture 30 Encapsulations In OOPS Lecture 31 Abstraction In OOPS Lecture 32 Magic Methods In Python Lecture 33 Operative Overloading In Python Lecture 34 Custom Exception Handling Lecture 35 Iterators In Python Lecture 36 Generators In Python Lecture 37 Fucntion Copy.Closures and Decorators Lecture 38 Numpy In Python Lecture 39 Pandas-DataFrame And Series Lecture 40 Data Manipulation With Pandas And Numpy Lecture 41 Reading Data From Various Data Source Using Pandas Lecture 42 Logging Practical Implementation In Python Lecture 43 Logging With Multiple Loggers Lecture 44 Logging With A Real World Examples Section 4: Getting Started With Pydantic In Python Lecture 45 Introduction To Pydantic Lecture 46 Pydantic Practical Implementation Section 5: Langchain Hands On Lecture 47 Getting Started With Langchain And Open AI Lecture 48 Creating Virtual Environment Lecture 49 Important Components Of LangChain Lecture 50 Data Ingestion With Documents Loaders Lecture 51 Recursive Character Text Splitter Lecture 52 Character Text Splitter With Langchain Lecture 53 HTML Header Text Splitter Lecture 54 Recursive Json Text Splitter Lecture 55 Introduction To OPENAI Embeddings Lecture 56 Ollama Embeddings Lecture 57 HuggingFace Embeddings Lecture 58 Vector Stores-FAISS Lecture 59 Vector Store And Retriever- Chroma DB Section 6: Getting Started With OpenAI And Ollama Lecture 60 Building Important Components Of Langchain Lecture 61 Building GENAI Apps Lecture 62 Understanding Retrievers And Chains Lecture 63 Introduction To Ollama And Set Up Lecture 64 Simple GenAI App Using Ollama Lecture 65 Tracking GENAI App Using Langsmith Section 7: Building Basic LLM Application Using LCEL Lecture 66 Getting Started With Open Source Models Uing Groq API Lecture 67 Building LLM Prompt And StrOutput Parser Chain With LCEL Lecture 68 Deploy Langserve Runnable And Chains As API Section 8: Building AI agents With Conversation History Using Langchain Lecture 69 Building Chatbot With Message History Using Langchain Lecture 70 Working With Prompt Template And Message ChatHistory Using LAngchain Lecture 71 Managing the Chat Conversation History Using Langchain Lecture 72 Working With VectorStore And Retriever Section 9: AI Agents Vs Agentic AI Lecture 73 What is Ai Agent Vs Agentic AI Lecture 74 Some More Examples Section 10: Getting Started With LangGraph Lecture 75 Introduction To LangGraph Lecture 76 Getting Started LangGraph Application- Creating The Environment Lecture 77 Setting Up OpenAI API Key Lecture 78 Setting Up GROQ API KEY Lecture 79 Setting Up LangSmith API Key Lecture 80 Developing A Simple Graph or Workflow Using LangGraph- Building Nodes And Edges Lecture 81 Building Simple Graph StateGraph And Graph Compiling Lecture 82 Developing LLM Powered Simple Chatbot Using LangGraph Section 11: LangGraph Components Lecture 83 State Schema With DataClasses Lecture 84 Pydantic Lecture 85 Chain In LangGraph Lecture 86 Routers In LangGraph Lecture 87 Tools And ToolNode With Chain Integration- Part 1 Lecture 88 Tools And Tool Node With Chain Integration-Part 2 Lecture 89 Building Chatbot With Multiple Tools Integration- Part 1 Lecture 90 Building Chatbot With Multiple Tools Integration-Part 2 Lecture 91 Introduction To Agents And ReAct Agent Architecture In LangGraph Lecture 92 ReAct Agent Architecture Implementation Lecture 93 Agent With Memory In LangGraph Lecture 94 Streaming In LangGraph Lecture 95 Streaming using astream events Using Langgraph Section 12: Debugging LangGraph Application With LangSmith Lecture 96 LangGraph Studio Section 13: Different Workflows In LangGraph Lecture 97 Prompt Chaining Lecture 98 Prompt Chaining Implementation With Langgraph Lecture 99 Parallelization Lecture 100 Routing Lecture 101 Orchestrator-Worker Lecture 102 Orchestrator Worker Implementation Lecture 103 Evaluator-optimizer Section 14: Human In The Loop In LangGraph Lecture 104 Human In The Loop With LangGraph Workflows Lecture 105 Human In the Loop Continuation Lecture 106 Editing Human Feedback In Workflow Lecture 107 Runtime Human Feedback In Workflow Section 15: RAG With LangGraph Lecture 108 Agentic RAG Theoretical Understanding Lecture 109 Agentic RAG Implementation- Part 1 Lecture 110 Agentic RAG Implementation-Part 2 Lecture 111 Adaptive RAG Theoretical Understanding Lecture 112 Adaptive RAG Implementation AI/ML Engineers and Developers who want to build advanced AI agent workflows and autonomous applications.,Data Scientists and Researchers looking to integrate agentic behavior into their data-driven projects.,Tech Enthusiasts and Students eager to explore the next generation of AI application development with practical hands-on projects.,Software Engineers interested in learning how to orchestrate multi-agent systems using modern frameworks like 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