This article was originally published on AI Study Room . For the full version with working code examples and related articles, visit the original post. Building Custom AI Agents with LangGraph: A Practical Guide LangGraph extends LangChain with graph-based state machine orchestration for building reliable, multi-step AI agent workflows. Unlike traditional linear chains, LangGraph lets you define cyclic graphs with conditional routing, persistent state, and human-in-the-loop checkpoints. Why LangGraph? Most agent frameworks chain LLM calls linearly: call LLM, parse output, call tool, repeat. This breaks for complex tasks requiring loops, branching, or manual approval. LangGraph models agents as state graphs where each node is a computation step and edges define control flow. The key innovations are state persistence across steps , conditional edges that route based on output content, and built-in checkpointing for pause-and-resume.…