This paper proposes a new approach to selecting large language models (LLMs) for strategic interactions, where the opponent's behavior is unknown and can adapt over time. The authors define a meta game on top of the base game and evaluate various selector methods, finding that a lightweight multi-armed bandit algorithm performs more reliably than others.