The team behind RecList is advancing machine learning by addressing the challenges of evaluating recommender systems, which are crucial in guiding user decisions in the digital world. Traditional metrics often fail to provide a comprehensive evaluation, missing out on key issues like bias and data drift. RecList, an open-source library, offers a solution by facilitating behavioral testing with plug-and-play test cases and datasets, allowing for a more thorough evaluation of model performance. The project, led by Jacopo Tagliabue and supported by Comet, aims to simplify and enhance model testing, making it scalable and engaging. RecList is designed to be adaptable for various models, not just recommender systems, by eliminating repetitive coding tasks and providing a platform for diverse and robust model assessments.