Quantitative behavioral analysis increasingly relies on video-based approaches combined with machine-learning tools to capture posture, locomotion, and complex behaviors that cannot be easily quantified manually. Commercial camera systems are often expensive, inflexible, and difficult to scale, while existing open-source solutions can be limited in low-light performance, multi-camera integration, and long-term stability. Here, we present VeNFLoPi (Versatile Night/Day Framework for Longitudinal Pi-based Imaging), an open-source Raspberry Pi-based video acquisition system optimized for longitudinal behavioral recordings across light and dark conditions. VeNFLoPi integrates Raspberry Pi cameras, custom infrared illumination, and centralized recording through Open Broadcaster Software (OBS), enabling real-time monitoring, high-resolution and high-frame-rate acquisition, robust file management, and automated local or cloud storage. Flexible 3D-printed mounts allow cameras and IR lighting to be positioned for top-down or angled views in compact behavioral chambers, with wide-angle lenses and fisheye correction ensuring distortion-free recording. The system is plug-and-play, requires no custom coding, and produces video formats immediately compatible with machine-learning workflows such as DeepLabCut, LabGym, and other pipelines. Optional scripts allow timestamping, preprocessing, and stitching of long-duration recordings. A full setup guide and system image are provided, making VeNFLoPi a flexible, affordable, and widely applicable platform for diverse behavioral experiments.