  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
