Co-TAP: Three-Layer Agent Interaction Protocol Technical Report
Abstract
This paper proposes Co-TAP (T: Triple, A: Agent, P: Protocol), a three-layer agent interaction protocol designed to address the challenges faced by multi-agent systems across the three core dimensions of Interoperability, Interaction and Collaboration, and Knowledge Sharing. We have designed and proposed a layered solution composed of three core protocols: the Human-Agent Interaction Protocol (HAI), the Unified Agent Protocol (UAP), and the Memory-Extraction-Knowledge Protocol (MEK). HAI focuses on the interaction layer, standardizing the flow of information between users, interfaces, and agents by defining a standardized, event-driven communication paradigm. This ensures the real-time performance, reliability, and synergy of interactions. As the core of the infrastructure layer, UAP is designed to break down communication barriers among heterogeneous agents through unified service discovery and protocol conversion mechanisms, thereby enabling seamless interconnection and interoperability of the underlying network. MEK, in turn, operates at the cognitive layer. By establishing a standardized ''Memory (M) - Extraction (E) - Knowledge (K)'' cognitive chain, it empowers agents with the ability to learn from individual experiences and form shareable knowledge, thereby laying the foundation for the realization of true collective intelligence. We believe this protocol framework will provide a solid engineering foundation and theoretical guidance for building the next generation of efficient, scalable, and intelligent multi-agent applications.
Cite
@article{arxiv.2510.08263,
title = {Co-TAP: Three-Layer Agent Interaction Protocol Technical Report},
author = {Shunyu An and Miao Wang and Yongchao Li and Dong Wan and Lina Wang and Ling Qin and Liqin Gao and Congyao Fan and Zhiyong Mao and Jiange Pu and Wenji Xia and Dong Zhao and Zhaohui Hao and Rui Hu and Ji Lu and Guiyue Zhou and Baoyu Tang and Yanqin Gao and Yongsheng Du and Daigang Xu and Lingjun Huang and Baoli Wang and Xiwen Zhang and Luyao Wang and Shilong Liu},
journal= {arXiv preprint arXiv:2510.08263},
year = {2025}
}