Title: Multi-agent Linear Contextual Bandits with Bounded O(1) Regret. Abstract: Asymptotically unbounded regret of order $O(\sqrt{T})$ has been proved to be the lowest possible regret order that can be achieved in typical linear contextual bandit settings. Here we present a linear contextual bandit setting with repetitive arrivals of a set of agents where bounded, i.e., $O(1)$, expected regret can be achieved for each agent. We provide a novel Counterfactual UCB (CFUCB) policy where agents benefit from the experiences of other agents. It is shown that sharing of information is a Subgame Perfect Nash Equilibrium for the agents with respect to the order of the regret, which results in each agent realizing bounded regret. Personalized recommender systems and adaptive experimentation are two important applications.