Autonomous AI Agents with LangChain & LangGraph

This course contains the use of artificial intelligence.


Step into the world of agentic AI and learn how modern AI agents are built using LangChain and LangGraph.

This course provides a practical introduction to the core concepts behind AI agents and the tools used to build intelligent, LLM-powered applications. You'll begin by exploring the evolution from traditional chatbots to autonomous AI agents, understand the core components of an agent, and discover how LLMs, memory, tools, and prompts work together to create intelligent systems.

You'll then set up your Python environment and learn the fundamentals of the LangChain ecosystem. Through practical demonstrations, you'll install LangChain, make your first API call, and learn best practices for structuring LangChain projects.

Next, you'll explore essential LangChain concepts, including models, prompts, output parsers, chains, and memory. You'll build a basic LLM chain and add conversation memory to create more context-aware applications.

You'll also learn how to equip AI agents with external tools. You'll explore built-in LangChain tools, create custom Python tools for automation, and understand how to handle tool errors and timeouts.

Finally, you'll be introduced to LangGraph and learn why graph-based architectures are important for building more flexible and powerful AI agent systems. You'll explore core concepts such as state, nodes, and edges, providing the foundation for building advanced agent workflows.

By the end of this course, you'll understand the essential building blocks of AI agents and have a strong foundation in LangChain and LangGraph for developing intelligent AI-powered applications.