Our research group studies the role of molecular mechanisms in learning and memory. Manual scoring of rodent exploratory behavior is time-consuming and prone to bias. To address this, we developed RAINSTORM (Real and Artificial Intelligence for Neuroscience — Simple Tracker for Object Recognition Memory), an open-source, AI-driven behavioral labeling tool designed to learn from experimenters labeling criteria. RAINSTORM processes pose estimation data (e.g., from DeepLabCut) to automate precise quantification of object exploration, and easily extends to many other tasks. We performed a series of experiments on object recognition in mice, focusing on the localized administration of ERK pathway inhibitors and activators. Our protocol goes beyond simply measuring exploration time by revealing drug-induced changes in mobility and exploration dynamics. This enables a comprehensive assessment of effects on mouse training, learning, and memory that were previously overlooked. By standardizing and accelerating analysis, RAINSTORM enhances reproducibility and comparability of results. Fully available on GitHub, it fosters collaboration to validate and expand its utility. We believe open, collaborative AI tools, especially in the current scientific climate, are key to revolutionizing behavioral neuroscience.