The COVID-19 pandemic has led to an increase in known risk factors for mental health problems. This evidence has underscored an urgent need for models that can project mental health outcomes over time, explore lockdown scenarios, and target the needs of specific populations. Here we describe COMMA (COvid Mental-health Model with Agents), a new open-source microsimulation model developed to help address these questions. COMMA takes as input demographic information on age structures, population size, etc; and lockdown policies operationalised as a set of action probabilities. The lockdown policies affect the likelihood of individuals developing mental health issues, notably depression, based on demographic profile. Implemented purely in Python, COMMA has been designed with equal emphasis on performance, ease of use, and flexibility. Users can customise lockdown scenarios and population characteristics and execute simulations on a standard laptop within minutes. In a collaboration between the Netherlands eScience center and Wageningen university, COMMA has already been employed to assess the impact of various lockdown strategies on the mental well-being of a population resembling the demographic profiles of the inhabitants of the Groningen area.