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

2025-07-11

AI Summary

Current large language model (LLM) applications face significant bottlenecks in tool integration—including fragmentation, tight protocol coupling, and implementation complexity. To address these challenges, this paper proposes a protocol-agnostic unified tool management architecture. The architecture abstracts tool registration, representation, execution, and lifecycle management, ensuring full (100%) compatibility with the OpenAI function-calling standard. It integrates a unified interface design, concurrent execution mechanisms, and modular encapsulation techniques to enable multi-protocol adaptation and dynamic tool discovery. Experimental results demonstrate a 60–80% reduction in tool integration code and up to a 3.1× improvement in end-to-end execution performance. The architecture has been validated in real-world production environments, confirming its efficiency, maintainability, and scalability.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

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 oolregistry 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. oolregistry is open-source and available at https://github.com/Oaklight/ToolRegistry, with comprehensive documentation at https://toolregistry.readthedocs.io/.

Problem

• Fragmented and complex tool integration for LLMs
• Protocol limitations in current tool management approaches
• High development overhead in LLM tool lifecycle management

Innovation

• Protocol-agnostic tool management library
• Unified interface for tool lifecycle
• Concurrent execution boosts performance