INTRODUCTION: How can we make mouse studies more reproducible? The obvious answer is the standardization of experimental conditions, minimization of human interference, automation of behavioral tests and data analysis, and introduction of data analysis pipelines to automate the process. Eco‑HAB, a system for automated measurement of social preference and in-cohort sociability in mice, provides a solution for the first two issues. Eco‑HAB closely follows murine ethology, providing a 4‑compartment apparatus with narrow tunnels, and minimizes contact between the experimenter and tested animals. Introduction of pyEcoHAB, a Python library for analysis of EcoHAB murine behavioral data. AIM(S): pyEcoHAB, a Python package, has been developed to automate and facilitate data analysis. METHOD(S): Combining data access and initial interpretation, pyEcoHAB removes the need to do this manually, and allows the researcher to build data analysis pipelines and automation of behavioral tests facilitating data interpretation. pyEcoHAB provides an object‑oriented application programming interface (API) and a data abstraction layer. Auxiliary utilities supporting development of analysis workflows are integrated with pyEcoHAB, including data validation and workflow configuration tools. Moreover, pyEcoHAB provides methods for assessment of mice social behavior, such as approach to social odor, total time spent by each pair of mice together in each compartment (in-cohort sociability), number of times each mouse follows other mice in narrow tunnels (following), and also, the number of times each mouse pushes other mice out of a narrow tunnel. The latter behaviorissimilarto tube dominance tests and is an example of how traditional behavioral tests can be automated. CONCLUSIONS: pyEcoHAB is a computational framework facilitating automatic analysis of behavioral data from EcoHAB system. FINANCIAL SUPPORT: This work was supported by the Polish National Science Centre grant 2017/27/B/ NZ4/02025.