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

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arxiv 2405.14767 v2 pith:7I4IBGCW submitted 2024-05-23 q-fin.ST cs.CLcs.LGq-fin.TR

Hongyang Yang , Boyu Zhang , Neng Wang , Cheng Guo , Xiaoli Zhang , Likun Lin , Junlin Wang , Tianyu Zhou
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Mao Guan Runjia Zhang Christina Dan Wang
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classification q-fin.STcs.CLcs.LGq-fin.TR
keywords financialfinrobotlayermodelsopen-sourceplatformaccessagent
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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}.

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Forward citations

Cited by 9 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. MASPOB: Bandit-Based Prompt Optimization for Multi-Agent Systems with Graph Neural Networks

    cs.LG 2026-03 conditional novelty 6.0 of 10

    MASPOB combines a GNN surrogate, LinUCB-style uncertainty, and coordinate ascent to optimize prompts in fixed-topology multi-agent LLM systems, beating AFlow and MIPRO on average across six benchmarks.

  2. NGDBench: Towards Neural Graph Data Management

    cs.DB 2026-02 conditional novelty 6.0 of 10

    NGDBench is a five-domain noisy-graph benchmark showing current LLM query methods fail on aggregation and state tracking under data perturbation.

  3. ContestTrade: A Multi-Agent Trading System Based on Internal Contest Mechanism

    q-fin.TR 2025-08 reject novelty 6.0 of 10

    An LLM trading system that selects agents through an internal contest scored by a zero-intelligence trader and LightGBM predictions reports 52.8% returns and Sharpe 3.12 on A-shares over six months.

  4. Single-agent or Multi-agent Systems? Why Not Both?

    cs.MA 2025-05 conditional novelty 6.0 of 10

    On 15 agentic benchmarks, the accuracy advantage of multi-agent LLM systems over single-agent systems mostly disappears with stronger base models, and a hybrid single/multi-agent cascade improves accuracy and cuts cost.

  5. Cognify: Supercharging Gen-AI Workflows With Hierarchical Autotuning

    cs.LG 2025-02 conditional novelty 6.0 of 10

    Cognify uses a budget-adaptive hierarchical search to automatically tune gen-AI workflows, reporting up to 2.8x quality gains, 10x cost cuts, and 2.7x latency cuts on six benchmarks.

  6. MetaGraph: A Large-Scale Meta-Analysis of GenAI in Financial NLP (2022-2025)

    cs.CL 2025-09 unverdicted novelty 5.0 of 10

    Using LLM extraction on 681 papers, the authors build a public knowledge graph showing financial NLP moved from LLM adoption to limitation-aware, modular system design between 2022 and 2025.

  7. AgentDistill: Training-Free Agent Distillation with Generalizable MCP Boxes

    cs.AI 2025-06 reject novelty 5.0 of 10

    AgentDistill distills agent capabilities without any training by having a teacher generate reusable MCP tool boxes that small-model students invoke at inference time.

  8. FinRobot: Generative Business Process AI Agents for Enterprise Resource Planning in Finance

    cs.AI 2025-06 reject novelty 4.0 of 10

    A generative multi-agent framework for ERP dynamically orchestrates financial workflows and claims 40% time and 94% error reductions in two bank case studies, with limited public evidence.

  9. A Survey on Agent Workflow -- Status and Future

    cs.AI 2025-08 conditional novelty 3.0 of 10

    A review that classifies 24 agent workflow systems along functional and architectural axes and argues for standardization, optimization, and security work.

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