FROAV: A Framework for RAG Observation and Agent Verification - Lowering the Barrier to LLM Agent Research
Tzu-Hsuan Lin, Chih-Hsuan Kao
TL;DR
FROAV addresses the barrier to LLM-agent research by providing a plug-and-play platform that combines n8n-based visual workflows, a multi-dimensional LLM-as-a-Judge evaluation, and HITL feedback within a PostgreSQL/FastAPI/Streamlit stack. The framework enables rapid prototyping of RAG strategies, systematic evaluation against human judgments, and extensible integration with Python ML pipelines, demonstrated on financial document analysis. It emphasizes domain-agnostic architecture, open-source accessibility, and reproducible deployment via Docker Compose. Collectively, FROAV lowers the engineering burden, accelerates hypothesis testing, and fosters algorithmic innovation in LLM-agent research.
Abstract
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.