Stateful LLM Workflows: Transitioning from LangChain to LangGraph

While basic sequential chains are excellent for simple prompt-and-response tasks, they quickly break down when your AI application requires loops, memory, and complex decision-making. Transitioning to a state-based architecture is essential for building resilient, production-ready AI agents that can maintain context over long interactions. This written course guides you through the architectural limitations of traditional linear workflows and introduces you to the stateful paradigms of LangGraph. You will understand how to manage shared state, handle complex agentic loops, and maintain robust conversation context across multiple LLM calls. What you'll learn: - Understand the fundamental limitations of sequential chains regarding memory and context. - Explore the core concepts of stateful orchestration and graph-based LLM workflows. - Implement LangGraph shared state to pass context seamlessly between different execution nodes. - Design agentic loops that allow LLMs to self-correct and iterate on tasks. - Apply persistence and checkpointing mechanisms to maintain reliable conversation history. The course starts with foundational definitions of chains and state, then guides you through reading and analyzing structured code examples that transition a linear workflow into a robust, state-controlled agentic graph. This course is designed for developers and AI enthusiasts who have a basic familiarity with Python and want to build more advanced, context-aware LLM applications. No prior experience with LangGraph is required. Read through our structured guides and start building smarter, stateful AI workflows today.