Abstract: This paper studies the cooperative stochastic $k$-armed bandit problem, where $m$ agents collaborate to identify the optimal action. Rather than adapting a specific single-agent algorithm, we propose a general-purpose black-box reduction that extends any single-agent algorithm to the multi-agent setting. Under mild assumptions, we prove that our black-box approach preserves the regret guarantees of the chosen algorithm, and is capable of achieving minimax-optimality up to an additive graph-dependent term. Our method applies to various bandit settings, including heavy-tailed and duelling bandits, and those with local differential privacy. Empirically, it is competitive with or outperforms specialized multi-agent algorithms.