With the significant advancements in cognitive intelligence driven by LLMs, autonomous agent systems have attracted extensive attention. Despite this growing interest, the development of stable and efficient agent systems poses substantial practical challenges. In this paper, we introduce FinVerse, a meticulously crafted agent system designed for a broad range of financial topics. FinVerse integrates over 600 financial APIs, enabling access to more accurate and extensive financial information compared to generalist agents. To enhance financial information processing capabilities, FinVerse is equipped with an embedded code interpreter, enabling the execution of complex data analysis tasks with precision and efficiency. Our work includes an empirical comparison of several LLMs in driving FinVerse. Specifically, we propose our own scheme for training LLMs using SFT to optimize LLM performance within FinVerse. Recognizing the scarcity of specialized datasets to build LLMs for agents, we have constructed a dataset and plan to make it open-source, providing a valuable resource for peer application developers. The demo video has been released on YouTube at https://www.youtube.com/watch?v=sk8L9_Wv7J4

翻译：随着大语言模型驱动的认知智能取得显著进展，自洽智能体系统已引起广泛关注。尽管关注度日益增长，开发稳定高效的智能体系统仍面临巨大的实际挑战。本文介绍FinVerse，这是一个为广泛金融主题精心设计的智能体系统。FinVerse集成了超过600个金融API，相比通用智能体能够获取更准确、更全面的金融信息。为增强金融信息处理能力，FinVerse配备了嵌入式代码解释器，能够精准高效地执行复杂的数据分析任务。我们的工作包含对驱动FinVerse的多种大语言模型的实证比较。具体而言，我们提出了利用监督微调训练大语言模型的自主方案，以优化其在FinVerse内的性能。鉴于构建面向智能体的大语言模型缺乏专用数据集，我们构建了一个数据集并计划将其开源，为同业应用开发者提供宝贵资源。演示视频已发布于YouTube平台：https://www.youtube.com/watch?v=sk8L9_Wv7J4