Recent advancements in Large Language Models (LLMs)  have revolutionized decision-making by breaking down complex problems into more manageable language sequences referred to as “thoughts”. An effective thought design should consider three key perspectives: performance, efficiency, and flexibility. However, existing thought can at most exhibit two of these attributes. To address these limitations, we introduce a novel thought prompting approach called “Everything of Thoughts” (XoT)  to defy the law of “Penrose triangle” of existing thought paradigms.XoTleverages pretrained reinforcement learning and Monte Carlo Tree Search (MCTS)  to incorporate external domain knowledge into thoughts, thereby enhancing LLMs’ capabilities and enabling them to generalize to unseen problems efficiently. Through the utilization of the MCTS-LLM collaborative thought revision framework, this approach autonomously produces high-quality comprehensive cognitive mappings with minimal LLM interactions. Additionally,XoTempowers LLMs to engage in unconstrained thinking, allowing for flexible cognitive mappings for problems with multiple solutions. We evaluateXoTon several challenging problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our results demonstrate thatXoTsignificantly outperforms existing approaches in various dimensions, showcasing its remarkable proficiency in addressing complex problems across diverse domains.