On September 21, Tsinghua University, Infinigen, and Zhengxing Innovation jointly open-sourced RPent, an embodied agent infrastructure that connects general large-model task planning, VLA expert model fine manipulation, memory, tools, and robot interfaces to form a closed loop of perception, decision, execution, and feedback correction. It achieved a 92.6% task success rate on the LIBERO-PRO benchmark, improved end-to-end task completion speed by over 7 times, and released a series of real-robot demos.