Back to Blog LangGraph is gaining real adoption for agentic AI workflows. But for most teams evaluating it, the question is not how to build a pipeline -- it is whether LangGraph is the right architecture for their problem, and what it actually takes to run in production. LangGraph is becoming the default framework for teams building agentic AI workflows. That is both a good thing and a problem. The good part: it has real production pedigree, is actively maintained, and is used by teams doing serious work. The problem is that its growing reputation means a lot of teams are reaching for it by default -- before they have checked whether their problem actually calls for a graph-based orchestration framework rather than something simpler. This post is not a tutorial. If you want to understand how to wire up nodes, edges, and state management in code, the official documentation covers that.…