RLeXplore provides eight representative intrinsic reward algorithms for reinforcement learning, including count-based methods (PseudoCounts, RND, E3B), curiosity-driven approaches (ICM, Disagreement, RIDE), and memory-based techniques. The toolkit offers a unified workflow for constructing, computing, and optimizing intrinsic reward modules to enable standardized comparison of exploration strategies across different RL implementations.