Recent surge in Large Language Model (LLM) availability has opened exciting avenues for research. However, efficiently interacting with these models presents a significant hurdle since LLMs often reside on proprietary or self-hosted API endpoints, each requiring custom code for interaction. Conducting comparative studies between different models can therefore be time-consuming and necessitate significant engineering effort, hindering research efficiency and reproducibility. To address these challenges, we present prompto, an open source Python library which facilitates asynchronous querying of LLM endpoints enabling researchers to interact with multiple LLMs concurrently, while maximising efficiency and utilising individual rate limits. Our library empowers researchers and developers to interact with LLMs more effectively and enabling faster experimentation and evaluation. prompto is released with an introductory video (https://youtu.be/-eZAmlV4ypk) under MIT License and is available via GitHub (https://github.com/alan-turing-institute/prompto).

本研究解决了与大型语言模型（LLM）交互效率低下的问题，尤其是在需要与多个模型进行比较时。作者提出了一个名为prompto的开源Python库，支持异步查询LLM端点，从而提高研究人员与多个LLM的互动效率，促进更快速的实验和评估。研究结果表明，该库显著提高了研究的效率和可重现性。

Prompto：一个用于异步查询大型语言模型端点的开源库