Innovation in Recommender Systems is currently impeded by a fractured ecosystem, where researchers must choose between the ease of in-memory experimentation and the costly, complex rewriting required for distributed industrial engines. To bridge this gap, we present WarpRec, a high-performance framework that eliminates this trade-off through a novel, backend-agnostic architecture. It includes 50+ state-of-the-art algorithms, 40 metrics, and 19 filtering and splitting strategies that seamlessly transition from local execution to distributed training and optimization. The framework enforces ecological responsibility by integrating CodeCarbon for real-time energy tracking, showing that scalability need not come at the cost of scientific integrity or sustainability. Furthermore, WarpRec anticipates the shift toward Agentic AI, leading Recommender Systems to evolve from static ranking engines into interactive tools within the Generative AI ecosystem. In summary, WarpRec not only bridges the gap between academia and industry but also can serve as the architectural backbone for the next generation of sustainable, agent-ready Recommender Systems. Code is available at https://github.com/sisinflab/warprec/

翻译：当前，推荐系统的创新因生态系统割裂而受阻：研究者不得不在便捷的内存内实验与面向分布式工业引擎所需的高成本、复杂重写之间做出选择。为弥合这一鸿沟，我们提出了WarpRec——一个高性能框架，它通过一种新颖的、后端无关的架构消除了这种权衡。该框架包含50多种前沿算法、40项评估指标以及19种过滤与划分策略，能够无缝地从本地执行过渡到分布式训练与优化。通过集成CodeCarbon实现实时能耗追踪，该框架强化了生态责任，表明可扩展性不必以科学严谨性或可持续性为代价。此外，WarpRec预见了向智能体人工智能的转变，推动推荐系统从静态排序引擎演变为生成式人工智能生态系统中的交互式工具。总而言之，WarpRec不仅弥合了学术界与工业界之间的差距，更可作为下一代可持续、支持智能体的推荐系统的架构基石。代码发布于https://github.com/sisinflab/warprec/