Simulators for recommender systems are widely used for recommender systems performance evaluation and feedback loop effects analysis. Existing simulators often propose inflexible pipelines, are focused on narrow research tasks, or are not adapted to work with industrial large data volumes. To address these challenges, we developed the Sim4Rec simulation framework. The Sim4Rec models key aspects of the user-recommender system interaction process, such as user visits, items’ availability, users’ responses, and preferences dynamics using real and synthetic data, and provides additional functionality for the generation of synthetic users and items. The architecture of Sim4Rec is designed to be flexible and extensible to suit particular users’ needs and perform experiments on large-scale industrial datasets.