Long-term analysis of animal behavior has been limited by reliance on real-time sensors or manual scoring. Existing machine learning tools can automate analysis but often fail under variable conditions or ignore temporal dynamics. We developed a scalable pipeline for continuous, real-time acquisition and classification of behavior across multiple animals and conditions. At its core is a self-supervised vision model paired with a lightweight classifier that enables robust performance with minimal manual labeling. Our system achieves expert-level performance and can operate indefinitely across diverse recording environments. As a proof-of-concept, we recorded 97 mice over 2 weeks to test whether sex hormones influence circadian behaviors. We discovered sex- and estrogen-dependent rhythms in behaviors such as digging and nesting. We introduce the Circadian Behavioral Analysis Suite (CBAS), a modular toolkit that supports high-throughput, long-timescale behavioral phenotyping, allowing for the temporal analysis of behaviors that were previously difficult or impossible to observe.