Agentic AI with LangGraph Chatbots & Multi– Agent AI Systems

Master Agentic AI with LangGraph: Build AI Agents, Chatbots, HITL Workflows & Multi-Agent Systems Using Python

What you'll learn

⚡ Understand the fundamentals, mental model, and architecture of LangGraph
⚡ Build stateful AI workflows using State, Functions, Nodes, and Edges
⚡ Design multi-step Agentic AI workflows with LangGraph and Python
⚡ Implement conditional routing and dynamic branching based on AI decisions
⚡ Build an AI Blog Generator using a multi-step LangGraph workflow
⚡ Build an AI Shopping Recommendation System with intelligent conditional routing
⚡ Create multi-turn conversational AI chatbots with message-based state
⚡ Manage conversation history using threads, checkpointers, and persistent memory
⚡ Build streaming AI applications that deliver responses progressively
⚡ Implement Human-in-the-Loop workflows with pause, human approval, state updates, and resume functionality
⚡ Build an AI Coding Tutor capable of generating, streaming, and saving coding lessons
⚡ Improve AI workflow reliability using retries, regeneration, iterative workflows, and validation techniques
⚡ Visualize and debug LangGraph workflows to understand graph execution and routing
⚡ Design specialist AI agents that handle different types of tasks
⚡ Build an advanced Multi-Agent AI System with intelligent intent classification and routing
⚡ Integrate AI agents with weather APIs, arXiv research, and CSV data
⚡ Understand how to coordinate multiple specialist agents within a single LangGraph architecture
⚡ Apply LangGraph concepts to design and build real-world Agentic AI applications
⚡ Gain hands-on experience building projects that can be showcased in your portfolio and AI/GenAI resume

Requirements

❗ A basic understanding of Python programming
❗ Familiarity with fundamental Python concepts such as variables, functions, loops, conditionals, lists, and dictionaries
❗ No prior LangGraph experience is required — we will build everything step by step from the fundamentals
❗ Most importantly, a willingness to learn by building real-world AI applications

Description

Learn to build Agentic AI applications with LangGraph—from intelligent chatbots and Human-in-the-Loop workflows to advanced Multi-Agent AI Systems.
Large Language Models can generate impressive responses, but buildingreal-world AI applications requires much more than sending prompts to an LLM.
Modern AI systems needstate, memory, decision-making, routing, tool usage, streaming, human approval, and reliable execution.
That's whereLangGraph comes in.
In this hands-on course, you will learn how to useLangGraph and Python to design and build stateful AI workflows, conversational agents, Human-in-the-Loop systems, and multi-agent architectures.
You won't just learn the concepts.
You will build complete AI applications from the ground up.
Build 7 Practical Agentic AI Applications
Throughout the course, you will progressively build real projects that put each concept into practice
✨AI Blog Generator — Build a multi-step AI workflow using LangGraph.
✨AI Shopping Recommendation System — Create a personalized recommendation workflow with conditional routing.
✨Conversational AI Chatbot — Build a multi-turn chatbot with message-based state, conversation history, threads, and persistent memory.
✨Streaming AI Application — Stream LangGraph and chatbot responses as they are generated.
✨Human-in-the-Loop AI Workflow — Build an AI workflow that can pause, request human approval, and resume execution based on human feedback.
✨AI Coding Tutor — Build an AI tutor that generates, streams, and saves coding lessons.
✨Multi-Agent Research Assistant — Build an advanced multi-agent system with specialist agents forweather, research/arXiv, and CSV data, connected through intelligent intent-based routing.
Master the LangGraph Architecture
You will begin with the fundamentals and learn the core building blocks behind LangGraph applications
State → Functions → Nodes → Edges → Conditional Routing → Graph Execution
You will learn how information flows through a graph, how nodes perform individual tasks, how edges control execution, and how conditional routing enables dynamic AI workflows.
By the end of this foundation, you will be able to independently design and build your own LangGraph workflows.
Build Stateful Conversational AI Chatbots
Go beyond simple prompt-and-response applications and learn how to buildmulti-turn conversational AI systems.
You will work with message-based state, conversation history, threads, and checkpointers to create chatbots that canmaintain and resume previous conversations.
You will also learn how to addstreaming so your applications can deliver responses progressively rather than waiting for the entire workflow to finish.
Build Human-in-the-Loop AI Systems
Not every AI decision should happen completely autonomously.
You will learn how to buildHuman-in-the-Loop workflows where an AI application can pause execution, request human approval or feedback, update its state, and then continue or reject the workflow.
This gives you a practical understanding of how human oversight can be incorporated into Agentic AI workflows.
Build More Reliable AI Workflows
Building an agent is only the beginning.
You will also learn practical techniques for making LangGraph applicationsmore reliable and easier to debug, including retries, regeneration of weak outputs, iterative workflows, graph visualization, routing inspection, and common graph design mistakes.
Build an Advanced Multi-Agent AI System
The course culminates in an advancedMulti-Agent Research Assistant.
You will build an architecture where different specialist agents handle different types of requests.
The system will include
✨Intent Classification
✨Intelligent Routing
✨Weather Agent
✨Research/arXiv Agent
✨CSV Data Agent
✨Conditional Graph Branches
✨Unified Final Response
✨Graph Visualization and Debugging
You will see how multiple specialized agents can work together within a single LangGraph architecture to handle different real-world tasks.

What You Will Learn

By the end of this course, you will be able to
✨ Understand theLangGraph mental model and architecture
✨ Design and managestateful AI workflows
✨ Create LangGraph applications usingState, Functions, Nodes, and Edges
✨ Implementconditional routing and branching workflows
✨ Build multi-stepAgentic AI applications
✨ Createmulti-turn conversational AI chatbots
✨ Manage conversation history usingmessage-based state
✨ Implementpersistent memory with threads and checkpointers
✨ Addstreaming to LangGraph applications
✨ BuildHuman-in-the-Loop approval workflows
✨ Design morereliable and debuggable AI agents
✨ Create specialist agents and intelligentlyroute user requests
✨ Connect AI agents withexternal APIs, arXiv, and CSV data
✨ Build a completeMulti-Agent AI System
Who Is This Course For?
This course is designed for
✨Python Developers who want to build modern AI applications
✨GenAI Developers looking to move beyond basic LLM applications
✨AI/ML Engineers interested in Agentic AI
✨Software Developers who want to integrate LLMs into real applications
✨Aspiring Agentic AI Engineers who want practical LangGraph experience
You should have a basic understanding ofPython before taking this course.
Why This Course?
This is not a course where you simply watch someone explain LangGraph concepts.
You will progressively move fromfundamentals to complete AI applications, applying every major concept through hands-on projects.
You will start by building simple graph workflows, then progress tochatbots, persistent memory, streaming, Human-in-the-Loop workflows, reliable AI applications, and finally a Multi-Agent AI System.
By the end, you will have both theconceptual foundation and practical project experience needed to start building your own Agentic AI applications with LangGraph.
Stop building simple prompt-and-response applications. Start building stateful, intelligent AI systems with LangGraph.

Who this course is for

⭐ Python Developers who want to build modern AI applications using LangGraph
⭐ Generative AI Developers who want to move beyond simple prompt-and-response applications
⭐ AI/ML Engineers who want to learn practical Agentic AI and workflow orchestration
⭐ Software Developers interested in integrating LLMs into real-world applications
⭐ Full-Stack Developers who want to add Agentic AI capabilities to their applications
⭐ Aspiring Agentic AI Engineers looking to build practical, portfolio-ready AI projects
⭐ Developers learning LangChain who want to take the next step into stateful, graph-based AI workflows with LangGraph
⭐ AI enthusiasts and professionals who want hands-on experience building conversational AI, Human-in-the-Loop, and Multi-Agent AI systems
⭐ Students and career switchers with basic Python knowledge who want to explore modern Agentic AI development

Homepage

https://www.udemy.com/course/agentic-ai-langgraph

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