Optimal control is a powerful mathematical tool that can be used to fill the gap between collected data and equations, making the model more reliable and precise in the prediction of physical phenomena. However, optimal control problems are usually costly, most of all in a parametrized setting where many evaluations of the problem must be run to have a more comprehensive knowledge of the whole system. Reduced order methods (ROMs) help us to tackle this issue. Indeed, they aim at describing the parametric nature of the optimality system in a low-dimensional framework, accelerating the system solutions, maintaining the model accuracy. The talk aims at showing an overview of several applications in this topic through FEniCS-based  libraries, RBniCS and Multiphenics  , developed to deal with parametric partial differential equations. After describing the general problem formulation and the basic ideas behind ROMs, we will show many numerical results in the optimal control field highlighting the potential of FEniCS and of the two libraries for these very complex problems, moving from steady linear problems to nonlinear time-dependent ones   .