Holos: A Web-Scale LLM-Based Multi-Agent System for the Agentic Web

作者：Xiaohang Nie, Zihan Guo, Zicai Cui, Jiachi Yang, Zeyi Chen, Leheyi De, Yu Zhang, Junwei Liao, Bo-Sheng Huang, Yingxuan Yang, Zhien Han, Zimian Peng, Linyao Chen, Wenzheng Tang, Zongkai Liu, Tao Zhou, B. Hu, Shuyang Tang, Jianghao Lin, Weiwen Liu, Muning Wen, Yuan Zhou, Weinan Zhang · 发表于：arXiv.org · 年份：2026 · DOI：10.48550/arXiv.2604.02334 · 被引用次数：3 · 研究领域：Computer Science

As large language models (LLM)-driven agents transition from isolated task solvers to persistent digital entities, the emergence of the Agentic Web, an ecosystem where heterogeneous agents autonomously interact and co-evolve, marks a pivotal shift toward Artificial General Intelligence (AGI). However, LLM-based multi-agent systems (LaMAS) are hindered by open-world issues such as scaling friction, coordination breakdown, and value dissipation. To address these challenges, we introduce Holos, a web-scale LaMAS architected for long-term ecological persistence. Holos adopts a five-layer architecture, with core modules primarily featuring the Nuwa engine for high-efficiency agent generation and hosting, a market-driven Orchestrator for resilient coordination, and an endogenous value cycle to achieve incentive compatibility. By bridging the gap between micro-level collaboration and macro-scale emergence, Holos hopes to lay the foundation for the next generation of the self-organizing and continuously evolving Agentic Web. We have publicly released Holos (accessible at https://holosai.io), providing a resource for the community and a testbed for future research in large-scale agentic ecosystems.