标题： 工具注册表：一种用于函数调用大语言模型的协议无关工具管理库 显示英文标题

标题： ToolRegistry: A Protocol-Agnostic Tool Management Library for Function-Calling LLMs

摘要： 大型语言模型（LLM）应用正越来越多地依赖外部工具来扩展其超出文本生成的能力。 然而，当前的工具集成方法存在碎片化、协议限制和实现复杂性，导致大量的开发开销。 本文介绍了Toolregistry，这是一个与协议无关的工具管理库，通过统一接口简化工具注册、表示、执行和生命周期管理。 我们的评估表明， \toolregistry 在工具集成代码方面实现了60-80%的减少，通过并发执行实现了最高3.1倍的性能提升，并且完全兼容OpenAI函数调用标准。 实际案例研究显示，在各种集成场景中，开发效率和代码可维护性均有显著提升。 \toolregistry 是开源的，可在https://github.com/Oaklight/ToolRegistry获取，详细文档可在https://toolregistry.readthedocs.io/查阅。 显示英文摘要

摘要： Large Language Model (LLM) applications are increasingly relying on external tools to extend their capabilities beyond text generation. However, current tool integration approaches suffer from fragmentation, protocol limitations, and implementation complexity, leading to substantial development overhead. This paper presents Toolregistry, a protocol-agnostic tool management library that simplifies tool registration, representation, execution, and lifecycle management via a unified interface. Our evaluation demonstrates that \toolregistry achieves 60-80% reduction in tool integration code, up to 3.1x performance improvements through concurrent execution, and 100% compatibility with OpenAI function calling standards. Real-world case studies show significant improvements in development efficiency and code maintainability across diverse integration scenarios. \toolregistry is open-source and available at https://github.com/Oaklight/ToolRegistry, with comprehensive documentation at https://toolregistry.readthedocs.io/.