Handheld data-collection interfaces such as the Universal Manipulation Interface (UMI) allow manipulation demonstrations to be gathered in the wild without a robot in the loop, but the quality of the resulting action labels is bounded by the accuracy of vision-based localization. Existing systems recover end-effector pose from monocular visual-inertial odometry, which is subject to scale ambiguity and drift, or delegate tracking to closed-source modules and external infrastructure that reintroduce cost and setup burden. We present OpenJaws, a fully open hardware and software framework for sensorized UMI that improves localization and simplifies deployment across robot platforms. OpenJaws pairs a modular, 3D-printed gripper exposing a standardized ISO adapter with a low-cost stereo camera to recover metric camera trajectories. We further introduce UMI-Loc-Bench-v1, a benchmark quantifying real-world localization accuracy for handheld data-collection devices, and show that stereo localization substantially outperforms monocular alternatives. Finally, we demonstrate that policies trained on data collected with OpenJaws can be deployed on a real robot across multiple tasks. Hardware, software, and data are released under a commercially permissive license. Code, data, and videos are available at https://open-jaws.github.io/

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