  This paper presents Graph-of-Thought (GoT), a new model for workflow
automation that enhances the flexibility and efficiency of Large Language
Models (LLMs) in complex task execution. GoT advances beyond traditional linear
and tree-like cognitive models with a graph structure that enables dynamic path
selection. The open-source engine GoTFlow demonstrates the practical
application of GoT, facilitating automated, data-driven decision-making across
various domains. Despite challenges in complexity and transparency, GoTFlow's
potential for improving business processes is significant, promising
advancements in both efficiency and decision quality with continuous
development.
