Typically distributed coordination algorithms for multi-agent systems assume continuous-time agent dynamics, sensing and actuation. While this holds true for the plant dynamics, the majorities of agents have digital microprocessors which take sensor measurements at certain discrete time instances and process them to update the control inputs. This inherently discrete process is commonly approximated by a time-continuous one by assuming significantly higher sensing and actuation updating frequencies, compared to the time constants of the agent and network dynamics. However, this requirement of a high  sampling rate can lead to an inefficient usage of (limited) energy resources and high actuator wear. In order to reduce the amount of sensing and actuation, this project investigates event-based control for cooperating multi-agent systems. In event-based control updating the state measurement and/or the control input occurs only at certain discrete time instance, determined by a trigger function. Especially considering highly nonlinear plant dynamics, such as the Euler-Lagrange equations of motion which can be used to model a variety of agents, the reduced information available to the controller, as well as the piecewise continuous control law, can reduce the convergence speed of the system or in the worst case lead to instability. Thus the key challenge is the design of triggering functions which maintain the stability of the closed-loop system and achieve coordination between the agents.