The paper titled "CaBRNet, an open-source library for developing and evaluating Case-Based Reasoning Models" presents a new framework aimed at enhancing the field of explainable artificial intelligence (AI). The authors, Romain Xu-Darme and his colleagues from LSL, highlight the growing interest in self-explainable models, which serve as a more principled alternative to traditional post-hoc methods that attempt to clarify decisions made by opaque models after the fact. Despite the advancements in self-explainable models, the authors point out several challenges that persist in this area. These include issues related to reproducibility, difficulties in making fair comparisons between different models, and the lack of standardized practices across the field. To address these challenges, the authors introduce CaBRNet, a modular and backward-compatible framework specifically designed for Case-Based Reasoning Networks. This framework aims to provide a structured approach to developing and evaluating models, thereby facilitating better reproducibility and comparison. The paper was submitted on September 25, 2024, and is set to be presented at the 2nd World Conference on eXplainable Artificial Intelligence in July 2024 in La Valette, Malta. The authors encourage the use of their open-source library to foster collaboration and innovation in the development of explainable AI systems. By providing a robust platform for researchers and practitioners, CaBRNet aims to contribute significantly to the advancement of self-explainable models in artificial intelligence.