LangChain vs LangGraph: Choosing the Right Orchestration Layer for AI Agents

A developer completing IBM's Agentic AI course on Coursera draws a practical distinction between LangChain, LangGraph, and IDE-based agent harnesses. LangChain uses a linear chain model — input, output, done — with no built-in loop, persistence, or human-in-the-loop support. LangGraph adds stateful nodes, conditional cycles, checkpointing, and pause-and-resume capabilities, making it suited for true agentic workflows. The author notes that using LangChain's agent shortcut does not eliminate the graph layer; the runtime still runs one under the hood. The key takeaway is that teams should define their flow's structure before picking a library, since a stateless invoke pipeline is not an agent, regardless of which tool is used.