The rapid pupillary constriction to an abrupt light stimulus is signaled through an oligosynaptic neural pathway that dominates over other supranuclear influences on pupillary movement. A pupillometric recording of the pupil light reflex shows the steep change in pupil size from baseline to maximal constriction. However, when the pupil is recorded in darkness in the phase after light stimulation or in response to non-light stimuli like a sudden noise or cognitive activity, pupil size changes are small and slow. In such cases, pre-processing of pupil recordings to reduce the noise due to intrusion of various artifactual and non-evoked pupillary movements is particularly important but may be time-consuming. To address the paucity of automated tools for pupil light reflex analysis in pupillometry, we aimed to develop a software for automated, user-guided pupillometric data analysis. We identified two types of commonly observed artifacts on pupil recordings. We designed a software, called PupilMetrics, which imports and displays raw pupil data, detects and removes these two types of artifacts, and quantifies outcome measures like pupil size, response time, maximal contraction amplitude and PIPR. The right pupil of 29 healthy adults was recorded using a Neurolight pupillometer (IDMed, Marseilles) in response to 9 different light stimuli. Data analysis of the total 261 pupil responses were performed manually or automatically using PupilMetrics. High correlation was observed between PupilMetrics and manual analysis outcome measures across all stimuli (average R ² = 0.9891 and p < 0.0001) with a near 1-to-1 correspondence (Beta = 0.9940). PupilMetrics reduced the total analysis time from 30 h to under 1 h. PupilMetrics offers a time-efficient alternative to manual processing and delivers comparable results. Such software can facilitate standardization of pupillometry for clinical and research uses.