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有数十年的发展历史，并广泛应用于社会科学与自然科学领域。随着大语言模型（LLM）提供的全新可能性被吸纳其中，该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）并非仅让智能体相互对话的经典智能体模型（ABM），而是利用LLM构建的：它能运用常识应对情境、做出"合理"行为、调用通用语义知识、通过API调用控制数字技术（如应用程序），并在模拟系统内部及向外部研究人员进行通信。本文提出Concordia这一工具库，旨在简化生成式智能体模型的构建与应用。Concordia能够便捷地构建基于语言介导的物理或数字环境仿真。其智能体通过灵活的组件系统生成行为，该系统协调两大核心操作：LLM调用与联想记忆检索。受桌游角色扮演游戏启发而设计的特殊智能体"游戏主控（GM）"负责模拟智能体交互环境。智能体通过自然语言描述行为意愿，GM随后将其转化为具体实现。在模拟物理世界中，GM会校验智能体行为的物理合理性并描述其影响；在模拟应用程序与服务等数字技术环境中，GM可处理API调用以整合外部工具，例如通用AI助手（如Bard、ChatGPT）和数字应用（如日历、邮件、搜索等）。Concordia的设计初衷是支持科学研究和现实数字服务性能评估中的广泛应用场景，包括通过模拟用户和/或生成合成数据来达成目标。