StreamingClaw Technical Report

2026-03-23

AI Summary

This work addresses the fragmentation among perception, memory, and action capabilities in existing embodied intelligence systems during streaming video understanding and real-time interaction. To bridge this gap, we propose StreamingClaw, a unified end-to-end closed-loop framework that integrates streaming multimodal memory, online object-evolution-driven real-time reasoning, future event prediction, and an action-centric skill library. StreamingClaw enables low-latency perception-decision-action cycles, supports multi-agent collaboration through shared memory, and is compatible with the OpenClaw open-source ecosystem. The framework facilitates direct control over physical environments and provides an efficient, deployable solution for scalable embodied intelligence.

Technology Category

• Intelligent Robots: Embodied AI
• Multiagent Systems: Multiagent Learning
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

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.

Problem

• streaming video understanding
• embodied intelligence
• real-time reasoning
• multimodal memory
• proactive interaction

Innovation

• streaming video understanding
• embodied intelligence
• multimodal memory
• real-time reasoning
• proactive interaction