ToolRegistry: A Protocol-Agnostic Tool Management Library for Function-Calling LLMs
This addresses development overhead for LLM application builders, offering a practical solution but is incremental as it builds on existing tool integration paradigms.
The paper tackles the problem of fragmented and complex tool integration for LLMs by introducing ToolRegistry, a protocol-agnostic library that reduces integration code by 60-80% and improves performance up to 3.1x through concurrent execution.
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/.