Recent large language models (LLMs) have demonstrated great potential toward intelligent agents and next-gen automation, but there currently lacks a systematic benchmark for evaluating LLMs' abilities as agents. We introduce SmartPlay: both a challenging benchmark and a methodology for evaluating LLMs as agents. SmartPlay consists of 6 different games, including Rock-Paper-Scissors, Tower of Hanoi, Minecraft. Each game features a unique setting, providing up to 20 evaluation settings and infinite environment variations. Each game in SmartPlay uniquely challenges a subset of 9 important capabilities of an intelligent LLM agent, including reasoning with object dependencies, planning ahead, spatial reasoning, learning from history, and understanding randomness. The distinction between the set of capabilities each game test allows us to analyze each capability separately. SmartPlay serves not only as a rigorous testing ground for evaluating the overall performance of LLM agents but also as a road-map for identifying gaps in current methodologies. We release our benchmark at github.com/LLMsmartplay/SmartPlay

近期的大型语言模型（LLMs）展示了在智能代理和下一代自动化方面的巨大潜力，但目前缺乏一个系统性的基准来评估LLMs作为代理的能力。我们介绍了SmartPlay：一个具有挑战性的基准和评估LLMs作为代理的方法论。SmartPlay包括6个不同的游戏，包括石头剪刀布、汉诺塔、Minecraft。每个游戏都有独特的设定，提供最多20个评估设置和无限的环境变化。SmartPlay中的每个游戏独特地挑战了智能LLM代理的9个重要能力的子集，包括处理对象的依赖关系、提前规划、空间推理、从历史中学习和理解随机性。每个游戏测试的能力集之间的区别使我们能够单独分析每个能力。SmartPlay不仅是一个用于评估LLM代理整体性能的严格的测试平台，也是识别当前方法论中的空白的路线图。我们在github.com/LLMsmartplay/SmartPlay发布了我们的基准。

SmartPlay: LLM为智能化代理测试基准