Believable proxies of human behavior can empower interactive applications ranging from immersive environments to rehearsal spaces for interpersonal communication to prototyping tools. In this paper, we introduce generative agents--computational software agents that simulate believable human behavior. Generative agents wake up, cook breakfast, and head to work; artists paint, while authors write; they form opinions, notice each other, and initiate conversations; they remember and reflect on days past as they plan the next day. To enable generative agents, we describe an architecture that extends a large language model to store a complete record of the agent's experiences using natural language, synthesize those memories over time into higher-level reflections, and retrieve them dynamically to plan behavior. We instantiate generative agents to populate an interactive sandbox environment inspired by The Sims, where end users can interact with a small town of twenty five agents using natural language. In an evaluation, these generative agents produce believable individual and emergent social behaviors: for example, starting with only a single user-specified notion that one agent wants to throw a Valentine's Day party, the agents autonomously spread invitations to the party over the next two days, make new acquaintances, ask each other out on dates to the party, and coordinate to show up for the party together at the right time. We demonstrate through ablation that the components of our agent architecture--observation, planning, and reflection--each contribute critically to the believability of agent behavior. By fusing large language models with computational, interactive agents, this work introduces architectural and interaction patterns for enabling believable simulations of human behavior.

翻译：可信的人类行为代理能够赋能多种交互式应用，从沉浸式环境到人际沟通演练空间，再到原型设计工具。本文提出生成式智能体——一种模拟可信人类行为的计算软件代理。这些智能体清晨起床、烹饪早餐、前往工作；艺术家作画，作家写作；它们形成观点、察觉彼此并开启对话；它们回忆反思过往经历，同时规划次日行动。为实现生成式智能体，我们描述了一种扩展大型语言模型的架构：该架构用自然语言完整记录智能体的经历，随时间将这些记忆综合为更高层次的反思，并动态检索以规划行为。我们实例化生成式智能体，填充一个受《模拟人生》启发的交互式沙盒环境——终端用户可通过自然语言与包含25个智能体的小镇互动。评估表明，这些生成式智能体能产生可信的个体行为与涌现式社会行为：例如，仅凭用户指定“某个智能体想举办情人节派对”的单一概念，智能体便能在接下来两天自主发送派对邀请、结识新朋友、互相邀约共赴派对，并协调准时集体出现。通过消融实验，我们证明智能体架构的三大组件——观察、规划与反思——均对行为可信度有关键贡献。通过融合大型语言模型与计算型交互式智能体，本工作引入了实现可信人类行为仿真的架构模式与交互模式。