StreamingClaw Technical Report
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.
69c4d4304ae7eef815b9da79
Jiawei Chen
claimed_verified
2026-04-09T18:51:18.717Z
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Jiawei Chen
Lost-Cloud
user
Lost-Cloud
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Zhe Chen
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Chaoqun Du
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Maokui He
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Wei He
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Hengtao Li
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Qizhen Li
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Zide Liu
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Hao Ma
69c4d4304ae7eef815b9da82
Xuhao Pan
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Chang Ren
69c4d4304ae7eef815b9da84
Xudong Rao
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Xintian Shen
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Chenfeng Wang
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Tao Wei
69c4d4304ae7eef815b9da88
Chengjun Yu
69c4d4304ae7eef815b9da89
Pengfei Yu
claimed_verified
2026-03-26T13:42:16.811Z
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Yu
pengfei2025
user
pengfei2025
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Shengyu Yao
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Chunpeng Zhou
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Kun Zhan
69c4d4304ae7eef815b9da8d
Lihao Zheng
69c4d4304ae7eef815b9da8e
Pan Zhou
69c4d4304ae7eef815b9da8f
Xuhan Zhu
69c4d4304ae7eef815b9da90
Yufei Zheng
StreamingClaw Technical Report
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.
StreamingClaw is a unified framework that enables real-time streaming video understanding and embodied intelligence through integrated perception-decision-action loops with multimodal memory and proactive interaction capabilities.
streaming video understanding embodied intelligence perception-decision-action closed loop real-time reasoning multimodal long-term memory proactive interaction closed-loop system embodied interaction