Early life sleep disruption (ELSD) from P14-P21 in the socially monogamous prairie vole impacts adult social behavior (Jones et al, 2019). The use of automated tools to study animal behavior is called computational neuroethology (Datta et al, 2019). One primary tool is “deep learning” which requires a sequence of neural networks to learn information from the image and make predictions on other frames (Mathis and Mathis, 2020). In the context of studying social behavior among multiple animals, the neural networks predict pose by finding individual parts of each animal. Recent work from our group extended earlier findings with pose-estimation of two cohabitating prairie voles separated by a mesh-divider (Bueno-Soares Jr, 2023). One primary limitation of the study is that the animals had to be physically separated for the program to accurately identify pose. Here, we demonstrate the functionality and use-cases of a new, more precise analysis pipeline, SLEAP (Social LEAP Estimates Animal Pose), that enables identification of pose from 2+ animals in the same space (Pereira et al, 2022). This particular program was chosen based on its advanced performance in identifying multiple animals across time. We report the required steps within the SLEAP GUI (Graphical User Interface) from training the algorithm with manual labels, evaluating model performance and correct identification errors. Moreover, we transform the data output from SLEAP to analyze two behavioral paradigms (social investigation and partner preference testing). Application of SLEAP in a diversity of animal behavioral paradigms will improve research productivity and reproducibility.