Hybrid quantum–classical reinforcement learning (QRL) has recently emerged as a line of research exploring the benefits of quantum models in reinforcement-learning tasks, yet progress remains hindered by the lack of standard, modular tooling. SimplyQRL fills this gap with a lightweight Python library that decouples RL algorithms from agent architecture, allowing fair, reproducible comparisons across classical, quantum and hybrid designs. Built on CleanRL, Gymnasium and PennyLane, SimplyQRL ships native PPO and DQN implementations and exposes a single configuration dictionary that controls encoding strategies, parameterised quantum circuits and optional inference networks, plus advanced mechanisms such as Data Reuploading, Output Reuse and Output Scaling. Two illustrative case studies on CartPole-v1 and FrozenLake-v1 demonstrate SimplyQRL’s modular architecture, highlighting how agent components can be flexibly swapped and combined. By providing the first purpose-built, architecture-aware benchmark for QRL, SimplyQRL offers the community a concise, extensible foundation for systematic experimentation and accelerated discovery.