Abstract While modern Autonomous Vehicle (AV) systems can develop reliable driving policies under regular traffic conditions, they frequently struggle with safety-critical traffic scenarios. This difficulty primarily arises from the rarity of such scenarios in driving datasets and the complexities associated with predictive modeling of multiple vehicles. The simulation of safety-critical traffic events that can support the testing and refinement of AV policies is an essential challenge to be addressed. In this paper, we introduce TrafficGamer, which facilitates game-theoretic traffic simulation by viewing common road driving as a multi-agent game. When we evaluate the empirical performance across various real-world datasets, TrafficGamer ensures both the \textit{fidelity} and \textit{exploitability} of the simulated scenarios, guaranteeing that they not only statically aligned with real-world traffic distribution but also efficiently capture equilibriums for representing safety-critical scenarios involving multiple agents. Additionally, the results demonstrate that TrafficGamer provides highly flexible simulations across various contexts. Specifically, we demonstrate that the generated scenarios can dynamically adapt to equilibriums of varying tightness by configuring risk-sensitive constraints during optimization. To the best of our knowledge, TrafficGamer is the first simulator capable of generating more realistic and adaptable traffic simulations based on the game-theoretic oracles, enhancing decision-making for autonomous agents and improving the overall quality of safety-critical scenarios. We have provided a demo webpage for the project at: https://qiaoguanren.github.io/trafficgamer-demo/.