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Reinforcement learning, optimization, and planning/search are interconnected domains in artificial intelligence. Algorithms within these domains share many similarities. They complement each other in solving complex decision-making problems, and also offer opportunities for cross-disciplinary integration. However, conducting research on algorithms across these domains typically requires learning the specialized libraries. These libraries often couple algorithms with domain-specific problem classes, making it difficult to conduct cross-disciplinary researches. In order to solve this problem, we developed a generic and lightweight framework for reinforcement learning, optimization, and planning/search algorithms (RLOP). It implements only the core logic of algorithms, abstracting away domain-specific details by defining interface functions, which enables flexible customization and efficient integration across different domains. The framework has been open-sourced at https://github.com/songzhg/RLOP.