Production-Ready Multi-Agent Systems with LangGraph: A Complete Tutorial

A step-by-step guide to building, testing and deploying a multi-agent document processing pipeline with LangGraph Multi-agent systems are one of the most powerful patterns in modern AI engineering and one of the easiest to get wrong. A single LLM call is straightforward. Chaining a few together is manageable. But orchestrating four specialized agents that pass state, handle failures, loop on feedback and support human intervention? That requires real architecture. This tutorial walks through building a production-grade multi-agent document processing system using LangGraph . By the end, you'll have a working pipeline where a Researcher agent gathers information, a Writer drafts content, a Reviewer critiques it and an Editor produces the final output, all orchestrated as a compiled state graph with error handling, human-in-the-loop breakpoints and a clear deployment path. Let's build it. Architecture Overview Before writing any code, let's map out the system. Here's the high-level agent