Large language models (LLMs) have evolved AI assistants into autonomous reasoning engines that maintain context, invoke tools, and pursue long-horizon tasks. This has spurred Agent Operating Systems (Agent OS) as kernel-like layers for lifecycle management, memory, scheduling, and access control. Yet most designs remain agent-centric, treating the OS as a single-host runtime for internal reasoning and tool use, leaving open how autonomous actions integrate with distributed, collaborative, permission-sensitive workflows. TopoClaw is an open-source, human-centric, topology-aware Agent OS modeling the user's ecosystem as two coupled structures: a physical device topology of heterogeneous surfaces and a social relationship topology of shared spaces, teams, and delegated roles. It unifies device operation, messaging, and skills around accountable cross-boundary execution, with three core contributions: (1) cross-device action placement, decoupling intent from actuation and routing distributed actions across the device cluster based on hardware affordances and user context; (2) cross-user identity attribution, treating agents as socially situated "Digital Twins" that coordinate in multi-user spaces while preserving provenance, role-aware permissions, and human accountability; (3) cross-context authority governance, pairing broad capability with distributed, context-aware policy enforcement across physical and social trust boundaries to bound proactive autonomy at the OS layer. This report presents TopoClaw as an engineering-oriented reference architecture, covering its design principles, runtime, cross-device execution, collaboration mechanisms, security model, and deployment outlook.

翻译：大型语言模型（LLMs）已将AI助手演变为能够维持上下文、调用工具并执行长期任务的自主推理引擎。这催生了智能体操作系统（Agent OS），作为用于生命周期管理、内存管理、调度和访问控制的内核层。然而，大多数设计仍以智能体为中心，将操作系统视为支持内部推理和工具使用的单主机运行时，而未阐明自主行动如何与分布式、协作性、权限敏感的工作流相整合。TopoClaw是一个开源、以人为本、具有拓扑感知能力的智能体操作系统，它将用户生态系统建模为两个耦合结构：一个由异构设备表面组成的物理设备拓扑，以及一个由共享空间、团队和委派角色组成的社会关系拓扑。它将设备操作、消息传递和技能围绕可问责的跨边界执行进行统一，并包含三项核心贡献：（1）跨设备动作放置：将意图与执行解耦，并根据硬件能力和用户上下文在设备集群中路由分布式动作；（2）跨用户身份归属：将智能体视为社会化的“数字孪生”，在多用户空间中协调运作，同时确保来源追溯、基于角色的权限和人类可问责性；（3）跨上下文权限治理：将广泛能力与跨物理和社会信任边界的分布式、上下文感知策略执行相结合，以在操作系统层面约束主动自主性。本报告将TopoClaw作为面向工程的参考架构，涵盖其设计原则、运行时、跨设备执行、协作机制、安全模型及部署前景。