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Building a Stateful Chatbot with LangGraph and LangSmith
A Proven Step-by-Step Guide
Modern large‑language‑model (LLM) applications often need more than a single prompt–response. A conversational assistant might need to remember prior messages, call external tools, or ask a human for approval before acting. Designing these workflows from scratch quickly becomes brittle, so the LangGraph library provides a graph‑oriented framework for building stateful and controllable agent workflows. LangGraph’s architecture supports a range of control flows — from a single agent to multi‑agent, hierarchical, or sequential patterns — and is designed to handle complex scenarios reliably. It also has built‑in statefulness, allowing agents to collaborate with humans by writing drafts for review and letting you roll back to an earlier state when needed. Additional features like long‑term memory make it a compelling foundation for LLM‑based applications.
Below, we’ll walk through a simple but complete example —a chatbot that can recommend books, suggest exercise routines, and also answer general questions. You’ll learn how to set up the environment, define the state and nodes, connect everything with a graph, and run the system interactively. By the end, you should feel comfortable building your LangGraph‑powered agents.
