LLMs are increasingly applied to tasks with inherent graphical structure, but often suffer hallucinations and mathematical inaccuracies. We propose Graph-Grounded LLMs, which integrate a graph library through function calls to ground reasoning on graph-related tasks. This yields significant reductions in hallucinations and improved mathematical accuracy on NLGraph, and we demonstrate a disaster-rescue decision-support application.