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    Complete Agentic Ai Bootcamp With Langgraph And Langchain

    Posted By: ELK1nG
    Complete Agentic Ai Bootcamp With Langgraph And Langchain

    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 42m

    Learn 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 LangGraph.