The rapid advancement of Large Language Models (LLMs) and their integration into autonomous agent systems has created unprecedented opportunities for document analysis, decision support, and knowledge retrieval. However, the complexity of developing, evaluating, and iterating on LLM-based agent workflows presents significant barriers to researchers, particularly those without extensive software engineering expertise. We present FROAV (Framework for RAG Observation and Agent Verification), an open-source research platform that democratizes LLM agent research by providing a plug-and-play architecture combining visual workflow orchestration, a comprehensive evaluation framework, and extensible Python integration. FROAV implements a multi-stage Retrieval-Augmented Generation (RAG) pipeline coupled with a rigorous "LLM-as-a-Judge" evaluation system, all accessible through intuitive graphical interfaces. Our framework integrates n8n for no-code workflow design, PostgreSQL for granular data management, FastAPI for flexible backend logic, and Streamlit for human-in-the-loop interaction. Through this integrated ecosystem, researchers can rapidly prototype RAG strategies, conduct prompt engineering experiments, validate agent performance against human judgments, and collect structured feedback-all without writing infrastructure code. We demonstrate the framework's utility through its application to financial document analysis, while emphasizing its material-agnostic architecture that adapts to any domain requiring semantic analysis. FROAV represents a significant step toward making LLM agent research accessible to a broader scientific community, enabling researchers to focus on hypothesis testing and algorithmic innovation rather than system integration challenges.

翻译：大型语言模型（LLM）的快速发展及其与自主智能体系统的融合，为文档分析、决策支持和知识检索创造了前所未有的机遇。然而，基于LLM的智能体工作流的开发、评估与迭代的复杂性给研究者带来了显著障碍，特别是对那些缺乏深厚软件工程背景的研究人员。本文提出FROAV（检索增强生成观察与智能体验证框架），这是一个开源研究平台，通过提供结合可视化工作流编排、综合评估框架和可扩展Python集成的即插即用架构，使LLM智能体研究民主化。FROAV实现了多阶段检索增强生成（RAG）流水线，并与严格的“LLM即裁判”评估系统相结合，所有功能均可通过直观的图形界面访问。该框架集成了n8n用于无代码工作流设计、PostgreSQL用于细粒度数据管理、FastAPI用于灵活的后端逻辑以及Streamlit用于人机交互。通过这一集成生态系统，研究者能够快速原型化RAG策略、开展提示工程实验、依据人工判断验证智能体性能并收集结构化反馈——所有这些都无需编写基础设施代码。我们通过该框架在金融文档分析中的应用展示了其实用性，同时强调其领域无关的架构可适配任何需要语义分析的领域。FROAV标志着向更广泛科学界开放LLM智能体研究的重要一步，使研究者能专注于假设检验和算法创新，而非系统集成挑战。