As intelligent systems and multi-agent coordination become increasingly central to real-world applications, there is a growing need for simulation tools that are both scalable and accessible. Existing high-fidelity simulators, while powerful, are often computationally expensive and ill-suited for rapid prototyping or large-scale agent deployments. We present GAMMS (Graph based Adversarial Multiagent Modeling Simulator), a lightweight yet extensible simulation framework designed to support fast development and evaluation of agent behavior in environments that can be represented as graphs. GAMMS emphasizes five core objectives: scalability, ease of use, integration-first architecture, fast visualization feedback, and real-world grounding. It enables efficient simulation of complex domains such as urban road networks and communication systems, supports integration with external tools (e.g., machine learning libraries, planning solvers), and provides built-in visualization with minimal configuration. GAMMS is agnostic to policy type, supporting heuristic, optimization-based, and learning-based agents, including those using large language models. By lowering the barrier to entry for researchers and enabling high-performance simulations on standard hardware, GAMMS facilitates experimentation and innovation in multi-agent systems, autonomous planning, and adversarial modeling. The framework is open-source and available at https://github.com/GAMMSim/GAMMS/

翻译：随着智能系统与多智能体协同在现实应用中日益重要，对兼具可扩展性与易用性的仿真工具需求不断增长。现有高保真模拟器虽功能强大，但通常计算成本高昂，难以适用于快速原型设计或大规模智能体部署。本文提出GAMMS（基于图的对抗性多智能体建模模拟器），这是一种轻量级且可扩展的仿真框架，专为支持在图结构可表征环境中快速开发与评估智能体行为而设计。GAMMS强调五大核心目标：可扩展性、易用性、集成优先架构、快速可视化反馈及现实场景锚定性。该框架能高效模拟城市道路网络与通信系统等复杂领域，支持与外部工具（如机器学习库、规划求解器）集成，并提供开箱即用的可视化功能。GAMMS对策略类型保持中立，支持启发式、基于优化及基于学习的智能体，包括使用大语言模型的智能体。通过降低研究者的使用门槛，并支持在标准硬件上进行高性能仿真，GAMMS为多智能体系统、自主规划与对抗性建模领域的实验与创新提供了便利。本框架已开源，访问地址为：https://github.com/GAMMSim/GAMMS/