Build Autonomous AI Agents with LangChain & LangGraph

This course contains the use of artificial intelligence. Artificial Intelligence is rapidly evolving from simple chatbots to autonomous AI agents capable of reasoning, using tools, maintaining memory, and solving complex tasks with minimal human intervention. As businesses increasingly adopt Agentic AI, skills in frameworks like LangChain and LangGraph have become highly valuable for developers and AI enthusiasts.

In this hands-on course, you'll learn how to build intelligent AI agents from scratch using Python, LangChain, and LangGraph. We'll start with the fundamentals of Agentic AI, exploring how large language models, prompts, memory, and tools work together to create autonomous systems. You'll then build LLM-powered applications, add conversational memory, integrate external tools, and create custom Python tools for automation.

Next, you'll move beyond traditional AI chains and discover how LangGraph enables powerful graph-based workflows. Through practical coding demonstrations, you'll build autonomous agents using the ReAct architecture, implement state management and persistent memory, create human-in-the-loop approval workflows, and develop collaborative multi-agent systems with supervisor and worker agents. Finally, you'll learn how to deploy your AI agents using FastAPI, containerize them with Docker, and build a simple Streamlit interface for interacting with your applications.

This course focuses on practical implementation rather than theory, with step-by-step demos that help you build real-world AI applications. Whether you're a Python developer, AI engineer, machine learning enthusiast, or software developer looking to enter the world of Agentic AI, this course will give you the knowledge and hands-on experience needed to build, customize, and deploy modern AI agents with confidence.