Complete Agentic Ai Bootcamp With Langgraph And Langchain

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.