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.
可信的人类行为代理可以增强从沉浸式环境到人际沟通的排练空间再到原型工具的互动应用。在本文中，我们介绍了生成代理：模拟可信人类行为的计算软件代理。生成代理会起床、做早餐，然后去上班；艺术家绘画，作家写作；他们形成观点，注意彼此，并发起对话；他们记住并反思过去的日子，同时计划第二天的活动。为了实现生成代理，我们描述了一种架构，该架构扩展了大型语言模型，以使用自然语言存储代理经历的完整记录，随着时间的推移将这些记忆合成为更高层次的反思，并动态检索它们以规划行为。我们实例化生成代理，以填充一个受《模拟人生》启发的互动沙盒环境，在该环境中，最终用户可以使用自然语言与一个由二十五个代理组成的小镇进行互动。在评估中，这些生成代理产生了可信的个体和涌现的社会行为。 例如，从仅有一个用户指定的概念开始，即一个代理希望举办一个情人节派对，代理们在接下来的两天内自主传播派对邀请，结识新朋友，互相约会参加派对，并协调在正确的时间一起出席派对。我们通过消融实验展示了我们代理架构的各个组成部分——观察、规划和反思——在代理行为的可信度方面各自发挥了关键作用。通过将大型语言模型与计算交互代理相结合，这项工作引入了架构和交互模式，以实现对人类行为的可信模拟。