Benchmarking new optimization methods on test problems is essential for assessing their performance and tuning their parameters. Yet, few problems are available in the literature for mixed-variable optimization. This manuscript introduces Cat-Suite, a collection of optimization problems involving categorical, integer, and continuous variables. Currently, Cat-Suite includes 60 analytical problems, half of which are constrained and half unconstrained. Each of these problems is either a modification of an existing one from the literature or derived from classical continuous test functions, such as Rosenbrock. Cat-Suite is an ongoing project, and additional problems will be added in the future. The goal is to facilitate benchmarking and testing of novel mixed-variable optimization methods with a diverse problem collection. The problems are implemented in Python and available at https://github.com/bbopt/Cat-Suite.