Every week, someone ships an LLM demo that wows a room and then quietly falls apart in production. The gap between "it worked when I tried it" and "it holds up with real users" is where most AI projects die — and it's exactly the gap this book closes. Written for developers who are comfortable with Python but new to building with large language models, this book takes you from your first single-line model call all the way to multi-agent systems running behind real APIs, with checkpointing, evaluation, and observability built in from the start. You won't just learn what LangChain's functions do — you'll learn why production systems need memory scoping, human approval gates on risky tool calls, retrieval pipelines that are graded on more than vibes, and tests that catch real regressions in a system that's allowed to be non-deterministic. Across eight chapters and dozens of working examples, you'll build a command-line tutor, a memory-backed assistant that doesn't forget or blow its context window, a tool-using agent with proper guardrails, a citation-backed RAG system over your own documents, a branching LangGraph workflow with time-travel debugging, and a supervisor-led team of specialized agents — then harden all of it with the testing, tracing, and deployment discipline that separates a hobby project from shippable software. Fully updated for LangChain v1.0 and LangGraph v1.0 — including create_agent, the middleware system, and the unified content-blocks API — this is the book to reach for when you're done collecting tutorials and ready to build something that survives contact with real users. Includes a complete glossary, a troubleshooting reference for the errors you'll actually hit, a downloadable exercise pack mapped to every chapter, and a pre-launch checklist worth bookmarking.
