StreamingClaw Technical Report
Authors: name: Jiawei Chen, name: Zhe Chen, name: Chaoqun Du, name: Maokui He, name: Wei He, name: Hengtao Li, name: Qizhen Li, name: Zide Liu, name: Hao Ma, name: Xuhao Pan, name: Chang Ren, name: Xudong Rao, name: Xintian Shen, name: Chenfeng Wang, name: Tao Wei, name: Chengjun Yu, name: Pengfei Yu, name: Shengyu Yao, name: Chunpeng Zhou, name: Kun Zhan, name: Lihao Zheng, name: Pan Zhou, name: Xuhan Zhu, name: Yufei Zheng
Organization: LiAuto Foundation Model

Applications such as embodied intelligence rely on a real-time perception-decision-action closed loop, posing stringent challenges for streaming video understanding. However, current agents suffer from fragmented capabilities, such as supporting only offline video understanding, lacking long-term multimodal memory mechanisms, or struggling to achieve real-time reasoning and proactive interaction under streaming inputs. These shortcomings have become a key bottleneck for preventing them from sustaining perception, making real-time decisions, and executing actions in real-world environments. To alleviate these issues, we propose StreamingClaw, a unified agent framework for streaming video understanding and embodied intelligence. It is also an OpenClaw-compatible framework that supports real-time, multimodal streaming interaction. StreamingClaw integrates five core capabilities: (1) It supports real-time streaming reasoning. (2) It supports reasoning about future events and proactive interaction under the online evolution of interaction objectives. (3) It supports multimodal long-term storage, hierarchical evolution, and efficient retrieval of shared memory across multiple agents. (4) It supports a closed-loop of perception-decision-action. In addition to conventional tools and skills, it also provides streaming tools and action-centric skills tailored for real-world physical environments. (5) It is compatible with the OpenClaw framework, allowing it to fully leverage the resources and support of the open-source community. With these designs, StreamingClaw integrates online real-time reasoning, multimodal long-term memory, and proactive interaction within a unified framework. Moreover, by translating decisions into executable actions, it enables direct control of the physical world, supporting practical deployment of embodied interaction.