Recent advances in Large Language Models (LLM) have led to a new class of autonomous agents, renewing and expanding interest in the area. LLM-powered Multiagent Systems (MAS) have thus emerged, both for assistive and simulation purposes, yet tools for realistic human behavior simulation -- with its distinctive challenges and opportunities -- remain underdeveloped. Existing MAS libraries and tools lack fine-grained persona specifications, population sampling facilities, experimentation support, and integrated validation, among other key capabilities, limiting their utility for behavioral studies, social simulation, and related applications. To address these deficiencies, in this work we introduce TinyTroupe, a simulation toolkit enabling detailed persona definitions (e.g., nationality, age, occupation, personality, beliefs, behaviors) and programmatic control via numerous LLM-driven mechanisms. This allows for the concise formulation of behavioral problems of practical interest, either at the individual or group level, and provides effective means for their solution. TinyTroupe's components are presented using representative working examples, such as brainstorming and market research sessions, thereby simultaneously clarifying their purpose and demonstrating their usefulness. Quantitative and qualitative evaluations of selected aspects are also provided, including preliminary experiments with real human behavior as control. Results highlight possibilities, limitations, and trade-offs. The approach, though realized as a specific Python implementation, is meant as a novel conceptual contribution, which can be partially or fully incorporated in other contexts. The library is available as open source at https://github.com/microsoft/tinytroupe.

翻译：近年来，大型语言模型（LLM）的进步催生了一类新型自主智能体，重新激发了该领域的兴趣并拓展了其应用范围。由此，基于LLM的多智能体系统（MAS）应运而生，兼具辅助与模拟功能，然而针对真实人类行为模拟的工具——尽管其具有独特的挑战与机遇——仍发展不足。现有的MAS库和工具缺乏细粒度的人物规格定义、群体采样设施、实验支持及集成验证等关键能力，限制了其在行为研究、社会模拟及相关应用中的效用。为解决这些不足，本文提出了TinyTroupe——一种模拟工具包，支持详细的人物定义（如国籍、年龄、职业、个性、信仰、行为）并通过多种LLM驱动机制实现程序化控制。这使得个体或群体层面具有实际意义的行为问题得以简洁表述，并提供了有效的求解手段。通过代表性工作实例（如头脑风暴和市场调研会话）展示了TinyTroupe的组件，同时阐明了其用途并验证了其实用性。本文还对选定方面进行了定量与定性评估，包括以真实人类行为作为对照的初步实验。结果揭示了可能性、局限性及权衡关系。尽管该方法以特定的Python实现形式呈现，但其旨在作为一种新颖的概念性贡献，可部分或完全融入其他上下文。该库以开源形式提供，地址为https://github.com/microsoft/tinytroupe。