LangChain and LangGraph: Building Stateful AI Agents

As AI applications become more complex, simple prompt chains are no longer enough. Developers need ways to build intelligent agents that can remember past interactions, make decisions, and correct their own mistakes. This course guides you through the evolution of AI development, bridging the gap between traditional LangChain concepts and modern LangGraph architectures. You will learn how to transition from linear chains to dynamic, stateful agent graphs, enabling you to build applications that handle complex, multi-step reasoning. What you will learn: • Understand the core differences between LangChain and LangGraph architectures. • Learn foundational agentic AI concepts, including tool calling and dynamic routing. • Design stateful workflows that maintain memory across multiple conversational turns. • Apply modern retrieval-augmented generation (RAG) patterns within a graph structure. • Build and structure your own agent graph using written coding exercises. • Practice fundamental prompt engineering techniques tailored for autonomous agents. The journey begins with a clear breakdown of essential AI terminology and foundational concepts before moving into practical code implementations. You will systematically explore how to define state, create nodes, and route logic to form a complete agentic system. This course is designed for beginners and developers who are new to agentic AI, requiring no prior experience with complex AI frameworks. Start reading today to modernize your AI development skills and build smarter, stateful applications.