LLM-based agents are rapidly proliferating, yet the infrastructure for discovering, evaluating, and governing them remains fragmented compared to mature ecosystems like software package registries (e.g., npm) and model hubs (e.g., Hugging Face). Existing efforts typically address naming, distribution, or protocol descriptors, but stop short of providing a registry layer that makes agents discoverable, comparable, and governable under automated reuse. We present AgentHub, a registry layer and accompanying research agenda for agent sharing that targets discovery and workflow integration, trust and security, openness and governance, ecosystem interoperability, lifecycle transparency, and capability clarity with evidence. We describe a reference prototype that implements a canonical manifest with publish-time validation, version-bound evidence records linked to auditable artifacts, and an append-only lifecycle event log whose states are respected by default in search and resolution. We also provide initial discovery results using an LLM-as-judge recommendation pipeline, showing how structured contracts and evidence improve intent-accurate retrieval beyond keyword-driven discovery. AgentHub aims to provide a common substrate for building reliable, reusable agent ecosystems.

翻译：基于大语言模型的智能体正在迅速涌现，然而相较于成熟的生态系统（如软件包注册中心npm和模型中心Hugging Face），用于发现、评估和管理智能体的基础设施仍处于碎片化状态。现有工作通常仅涉及命名、分发或协议描述，但尚未提供能够在自动化复用场景下实现智能体可发现、可比较、可治理的注册层。本文提出AgentHub——一个面向智能体共享的注册层及配套研究议程，其目标涵盖发现与工作流集成、信任与安全、开放与治理、生态系统互操作性、生命周期透明度，以及基于证据的能力明晰性。我们描述了一个参考原型，该原型实现了包含发布时验证的规范化清单、与可审计制品关联的版本绑定证据记录，以及默认在搜索与解析过程中保持状态一致性的仅追加式生命周期事件日志。我们还通过基于LLM-as-judge的推荐流程提供了初步发现结果，表明结构化合约与证据如何提升意图精准检索能力，超越关键词驱动的发现机制。AgentHub旨在为构建可靠、可复用的智能体生态系统提供通用基础层。