Phylogenetic methods and software have clearly played an essential role in advancing biological and biomedical discovery, yet more can be done to meet researchers’ needs. Significant technical and pedagogical barriers––both for software developers and users––limit the adoption of these powerful modeling tools. Thus, strengthening community-level relationships among users and developers, as well as enhancing access to cutting-edge statistical analysis is a critical direction for research in the life sciences. RevBayes is a highly flexible platform for Bayesian phylogenetic inference that allows users to build complex statistical models using a graphical modeling framework and an interpreted programming language. As part of an open-source project, the RevBayes team develops and maintains a variety of software resources and educational materials that are used by researchers, teachers, and students, including modeling and inference tools, development environments, documentation, visualization packages, and detailed user tutorials. These products and materials are the result of dedicated efforts from over 50 contributors and help to sustain a large international user base. We are investigating and piloting strategies for enhancing the experiences of community members at all levels (user, contributor, developer), with the goal of making the software and opportunities to join the development team more accessible, and to ultimately ensure the long-term stability and continued growth of RevBayes.