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FinRobot: An Open-Source AI Agent Platform for Financial Applications using Large Language Models

Author

Listed:
  • Hongyang Yang
  • Boyu Zhang
  • Neng Wang
  • Cheng Guo
  • Xiaoli Zhang
  • Likun Lin
  • Junlin Wang
  • Tianyu Zhou
  • Mao Guan
  • Runjia Zhang
  • Christina Dan Wang

Abstract

As financial institutions and professionals increasingly incorporate Large Language Models (LLMs) into their workflows, substantial barriers, including proprietary data and specialized knowledge, persist between the finance sector and the AI community. These challenges impede the AI community's ability to enhance financial tasks effectively. Acknowledging financial analysis's critical role, we aim to devise financial-specialized LLM-based toolchains and democratize access to them through open-source initiatives, promoting wider AI adoption in financial decision-making. In this paper, we introduce FinRobot, a novel open-source AI agent platform supporting multiple financially specialized AI agents, each powered by LLM. Specifically, the platform consists of four major layers: 1) the Financial AI Agents layer that formulates Financial Chain-of-Thought (CoT) by breaking sophisticated financial problems down into logical sequences; 2) the Financial LLM Algorithms layer dynamically configures appropriate model application strategies for specific tasks; 3) the LLMOps and DataOps layer produces accurate models by applying training/fine-tuning techniques and using task-relevant data; 4) the Multi-source LLM Foundation Models layer that integrates various LLMs and enables the above layers to access them directly. Finally, FinRobot provides hands-on for both professional-grade analysts and laypersons to utilize powerful AI techniques for advanced financial analysis. We open-source FinRobot at \url{https://github.com/AI4Finance-Foundation/FinRobot}.

Suggested Citation

  • Hongyang Yang & Boyu Zhang & Neng Wang & Cheng Guo & Xiaoli Zhang & Likun Lin & Junlin Wang & Tianyu Zhou & Mao Guan & Runjia Zhang & Christina Dan Wang, 2024. "FinRobot: An Open-Source AI Agent Platform for Financial Applications using Large Language Models," Papers 2405.14767, arXiv.org, revised May 2024.
  • Handle: RePEc:arx:papers:2405.14767
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    References listed on IDEAS

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    1. Abarbanell, JS & Bushee, BJ, 1997. "Fundamental analysis, future earnings, and stock prices," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 35(1), pages 1-24.
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    Cited by:

    1. Patrick Cheridito & Jean-Loup Dupret & Zhexin Wu, 2025. "ABIDES-MARL: A Multi-Agent Reinforcement Learning Environment for Endogenous Price Formation and Execution in a Limit Order Book," Papers 2511.02016, arXiv.org.
    2. Kunihiro Miyazaki & Takanobu Kawahara & Stephen Roberts & Stefan Zohren, 2026. "Toward Expert Investment Teams:A Multi-Agent LLM System with Fine-Grained Trading Tasks," Papers 2602.23330, arXiv.org.
    3. Aadi Singhi, 2025. "An Adaptive Multi Agent Bitcoin Trading System," Papers 2510.08068, arXiv.org, revised Nov 2025.
    4. Weixian Waylon Li & Hyeonjun Kim & Mihai Cucuringu & Tiejun Ma, 2025. "Can LLM-based Financial Investing Strategies Outperform the Market in Long Run?," Papers 2505.07078, arXiv.org, revised Feb 2026.
    5. Mostapha Benhenda, 2026. "Look-Ahead-Bench: a Standardized Benchmark of Look-ahead Bias in Point-in-Time LLMs for Finance," Papers 2601.13770, arXiv.org.

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