Hybrid models, which combine physics and machine learning (ML) based models, are becoming increasingly popular in hydrology due to their potential for improved prediction and process representation. However, there are no standards to make such models findable, accessible, interoperable, and reproducible (FAIR). Therefore, the primary objective of FRAME was to collaboratively design, implement, and test a standardized FAIR protocol tailored for hydrological hybrid models. To this end, a workshop was conducted with international collaborators to design a first version of the FAIR protocol for models with different model complexities. This involved defining the rules for considering a hybrid model as FAIR, the structure of the metadata, and the necessary ancillary software needed to support the community in implementing the protocol. In this regard, a collaborative model repository was developed (link, see also Appendix A) to enable modelers to seamlessly share their models in a FAIR manner with the users. Further, a command line tool was developed to enable both model developers and users to download and update models. The metadata specification and the model repository was tested rigorously with different models involved in the project. The FAIR hybrid model protocol has been and will be disseminated through various open research networks and hydrology conferences, and will be released to the broader hydrological community through a peer-reviewed publication.