In an era where artificial intelligence (AI) is reshaping countless fields, the research community of social sciences needs to adapt to the changes posed by these technologies [1, 2]. In particular, data quality and authenticity play a significant role in social sciences [3], where the conclusions drawn rely heavily on data collected, for instance, from surveys. There are many traditional ways of gathering data, such as public datasets or private surveys, but AI has led to innovative approaches, like using agent-based models (ABMs). In recent years, the use of this paradigm has gained significant attention across a variety of fields, from economics and social sciences to artificial intelligence and computational biology [4, 5, 6]. ABMs allow researchers to simulate complex situations by modeling the behaviors and interactions of individual agents within a given environment [7]. These models provide a powerful way to understand emergent phenomena--such as market dynamics, social behaviors, or ecological systems--that arise from the independent actions and interactions of individual agents, each following its own set of rules. In spite of their flexibility, these models face some limitations, particularly when dealing with complex environments. One of the main challenges is that the agents' behaviors are programmed by the modeler based on assumptions or simplified rules. This rigid structure limits the ability to account for the full range of possible interactions that can emerge in real-world scenarios.

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.