Agent-based modeling has been around for decades, and applied widely across the social and natural sciences. The scope of this research method is now poised to grow dramatically as it absorbs the new affordances provided by Large Language Models (LLM)s. Generative Agent-Based Models (GABM) are not just classic Agent-Based Models (ABM)s where the agents talk to one another. Rather, GABMs are constructed using an LLM to apply common sense to situations, act "reasonably", recall common semantic knowledge, produce API calls to control digital technologies like apps, and communicate both within the simulation and to researchers viewing it from the outside. Here we present Concordia, a library to facilitate constructing and working with GABMs. Concordia makes it easy to construct language-mediated simulations of physically- or digitally-grounded environments. Concordia agents produce their behavior using a flexible component system which mediates between two fundamental operations: LLM calls and associative memory retrieval. A special agent called the Game Master (GM), which was inspired by tabletop role-playing games, is responsible for simulating the environment where the agents interact. Agents take actions by describing what they want to do in natural language. The GM then translates their actions into appropriate implementations. In a simulated physical world, the GM checks the physical plausibility of agent actions and describes their effects. In digital environments simulating technologies such as apps and services, the GM may handle API calls to integrate with external tools such as general AI assistants (e.g., Bard, ChatGPT), and digital apps (e.g., Calendar, Email, Search, etc.). Concordia was designed to support a wide array of applications both in scientific research and for evaluating performance of real digital services by simulating users and/or generating synthetic data.

翻译：基于智能体的建模方法已存在数十年，并在社会科学和自然科学领域得到广泛应用。随着大型语言模型(LLM)新功能的引入，这一研究方法的适用范围正迎来显著扩展。生成式智能体模型(GABM)并非仅是经典智能体模型(ABM)中智能体之间相互对话的简单升级，其核心在于利用LLM构建能够运用常识应对情境、展现"合理"行为、调用通用语义知识、通过API接口控制数字技术（如应用程序），并在模拟系统内部与外部研究者之间进行通信的智能体。本文提出Concordia——一个专为构建和运用GABM设计的工具库。Concordia能够便捷地构建基于语言交互的物理或数字环境模拟系统。其智能体通过灵活的组件系统生成行为，该系统在LLM调用与关联记忆检索这两个核心操作之间建立桥梁。受桌游角色扮演游戏启发而设计的特殊智能体"游戏主持者(GM)"负责模拟智能体交互的环境。智能体以自然语言描述其意图来采取行动，GM则将这些行动转化为相应的执行方案。在模拟物理世界中，GM负责验证智能体行为的物理合理性并描述其影响；在模拟数字技术（如应用程序和服务）的环境中，GM可通过API调用与外部工具（如通用AI助手Bard、ChatGPT）及数字应用（如日历、邮件、搜索等）进行集成。Concordia旨在支持科学研究的广泛场景，同时通过模拟用户行为或生成合成数据，用于评估实际数字服务的性能。