Emerging applications such as embodied intelligence, AI hardware, autonomous driving, and intelligent cockpits rely on a real-time perception-decision-action closed loop, posing stringent challenges for streaming video understanding. However, current agents mostly 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 input. These shortcomings have become a key bottleneck for preventing agents from sustaining perception, making real-time decisions, and executing closed-loop actions in complex real-world environments, constraining their deployment and potential in dynamic, open physical worlds. To alleviate these issues, we propose StreamingClaw, a unified agent framework for streaming video understanding and embodied intelligence. Beyond maintaining full compatibility with the OpenClaw framework, it natively supports real-time, multimodal streaming interactions. 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 memory storage, hierarchical memory evolution, efficient memory retrieval, and memory sharing 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 leverage the resources and support of the open-source community.
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Figure 1: Pipeline of StreamingClaw: Multi-end inputs and the user query are fed into the main–sub agents for perception and decision-making. The instructions produced by the main–sub agents then guide the execution of tools and skills, whose results are fed back to the main–sub agents, forming a closed-loop pipeline of perception–decision–action.
Figure 2: Flowchart of SteamingMind’s streaming inference and self-planning scheduling.
Figure 3: Flowchart of StreamingMemory’s memory storage, evolution, and retrieval algorithm process.
Figure 4: Interaction scenarios of the StreamingProactivity agent: time-aware and event-grounding.
Figure 5: Implementation of the StreamingProactivity agent: training-free adaptation and training-based adaptation.
Figure 6: Flowchart of the training-based adaptation pipeline for the StreamingProactivity agent.