In recent years, many recommendation algorithms have been proposed, and the research of recommender system has been greatly boosted with the development of deep learning. However, concerns about the reproducibility in this field have increasingly arisen in the research community, owing to the slight but influential differences between recommendation algorithms, such as implementation details, evaluation protocols, dataset splitting, etc. To address this issue, ReChorus is presented, of which it is a comprehensive, efficient, flexible, and lightweight framework for recommendation algorithms based on PyTorch, with aims to form a “Chorus” of recommendation algorithms. In this framework, a wide range of recommendation algorithms of different categories is implemented, covering general recommendation, sequential recommendation, knowledge-aware recommendation, and time-aware recommendation. ReChorus also provides the paradigm of dataset preprocessing for some common datasets. Compared to other recommendation algorithm libraries, ReChorus is featured for that it strives to keep lightweight while unifies as many as different algorithms at the same time. ReChorus is also flexible, efficient, and easy to use, especially for research purposes. Researchers will find it effortless to implement new algorithms with ReChorus. Such a framework can help to train and evaluate different recommendation models under the same experimental setting, so as to avoid the impacts resulting from implementation details and assure an effective comparison among recommendation algorithms. The project has been released on GitHub: https://github.com/THUwangcy/ReChorus.