This article addresses the consensus problem of a class of unknown nonlinear multiagent systems (MASs) under directed graphs via a novel model-free deep reinforcement learning (DRL) based fully distributed event-triggered control (ETC) method. First, the DRL-based feedback linearization approach is developed to learn an approximated linearized control protocol in a model-free manner. Then, a novel adaptive event-triggered mechanism is proposed to save more communication resources and reduce the computational burden among agents, and the Zeno behavior is ruled out strictly. The control protocol proposed in this article does not involve global information, thus it can be implemented in a fully distributed manner. Furthermore, a new Lyapunov function is constructed using a graph-based diagonal matrix to achieve the consensus of MASs under directed graphs. Generally, distinct from the existing results, the proposed model-free DRL-based fully distributed ETC protocol has the following features: 1) only using the intermittent local information; 2) not requiring the model information and global graph information; and 3) applicable to the more general directed graph. Finally, simulation results are illustrated to show the feasibility and effectiveness of the proposed control scheme.