Biophysical processes are important factors to account for in the soil-plant-atmosphere continuum to predict the fluxes of energy, CO2 and H2O in the system. They include processes such as photosynthesis, evapo-transpiration, energy balance, temperature, or stomatal conductance. Biophysical models have been continuously developed for several decades to better understand and predict those processes, but they are still either complicated to use (e.g. compiled monolithic models) or slow to compute (e.g. implemented in R or Python), and very often the sub-processes included in those models are complicated to extend and evaluate independently. Furthermore, there are often many implementations to simulate the same process (e.g. see stomatal conductance models reviewed in Buckley (2017)), mostly only differing by a slight correction or improvement in their formulation. For example, Schymanski and Or (2017) corrected the model of Penman- Monteith by clarifying the number of sides exchanging sensible and latent fluxes in leaves. Those model implementations are developed for various reasons, including improvements in the knowledge about a process, different constraints for the simulation scale, data availability, or computational intensity. In this context, it can be difficult to choose one model over another, or even to evaluate their accuracy or applicability considering the particular objectives of the simulation. Besides, current tools only propose a limited set of models, without the possibility to easily add or compare external models.