Agentic AI with LangGraph: Chatbots & Multi-Agent AI Systems

About this course

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 building real-world AI applications requires much more than sending prompts to an LLM.Modern AI systems need state, memory, decision-making, routing, tool usage, streaming, human approval, and reliable execution.

That's where LangGraph comes in.

In this hands-on course, you will learn how to use LangGraph 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 for weather, research/arXiv, and CSV data, c

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