Designing A Scalable LLM Agent Framework for Large-scale Urban Segregation Simulation

Abstract

Agent-based models (ABMs) have long been employed to explore how individual behaviors aggregate into complex societal phenomena in urban space. The rise of Large Language Models (LLMs) offers a new paradigm for ABM, enabling high-fidelity modeling of human behavior. However, urban dynamics often require extensive agent interactions to emerge, and scaling up LLM agent simulations is limited by high-latency remote LLM inference and high costs. To address this, we propose the OpenCity framework for large-scale LLM agent simulation. We design a novel "Group-and-Distill" strategy that significantly reduces costs without sacrificing behavioral diversity by aggregating agents with shared static characteristics while preserving their unique dynamic states. Moreover, a system-level request scheduler is designed to optimize I/O concurrency and communication bottlenecks. Experiments in six cities globally demonstrate that OpenCity achieves a 600x speedup and a 45% reduction in token usage compared to standard baseline models, while maintaining 96% behavioral consistency with independently invoked agent baselines. Based on OpenCity, we conduct first benchmark test on large-scale social segregation with LLM agents. The results show that OpenCity-accelerated generative agents are effective in reproducing socioeconomic segregation patterns. Furthermore, our scalability analysis demonstrates that the realism of emerging social phenomena increases as the agents scale up, quantitatively validating the necessity of large-scale simulations. Finally, we demonstrate the framework's capability as a policy sandbox through a counterfactual analysis of urban equity. Overall, OpenCity provides a robust and efficient framework that unlocks the potential of LLMs for large-scale, high-fidelity multi-agent systems analysis. Code repo is available at https://github.com/tsinghua-fib-lab/OpenCity.