Udemy – Complete Agentic Ai Bootcamp 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.

Homepage

https://www.udemy.com/course/complete-agentic-ai-bootcamp-with-langgraph-and-langchain/

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