GAICo: A Deployed and Extensible Framework for Evaluating Diverse and Multimodal Generative AI Outputs

TL;DR

GAICo is an open-source framework that standardizes and streamlines the evaluation of diverse, multimodal Generative AI outputs, enhancing reproducibility and development efficiency.

Contribution

It introduces a comprehensive, extensible Python library supporting multi-modal, reference-based metrics for standardized GenAI output evaluation.

Findings

GAICo has been downloaded over 16K times, indicating strong community adoption.

It enables detailed comparison and debugging of complex multi-modal AI systems.

Demonstrated utility through a case study on AI Travel Assistant pipelines.

Abstract

The rapid proliferation of Generative AI (GenAI) into diverse, high-stakes domains necessitates robust and reproducible evaluation methods. However, practitioners often resort to ad-hoc, non-standardized scripts, as common metrics are often unsuitable for specialized, structured outputs (e.g., automated plans, time-series) or holistic comparison across modalities (e.g., text, audio, and image). This fragmentation hinders comparability and slows AI system development. To address this challenge, we present GAICo (Generative AI Comparator): a deployed, open-source Python library that streamlines and standardizes GenAI output comparison. GAICo provides a unified, extensible framework supporting a comprehensive suite of reference-based metrics for unstructured text, specialized structured data formats, and multimedia (images, audio). Its architecture features a high-level API for rapid, end-to-end analysis, from multi-model comparison to visualization and reporting, alongside direct metric access for granular control. We demonstrate GAICo's utility through a detailed case study evaluating and debugging complex, multi-modal AI Travel Assistant pipelines. GAICo empowers AI researchers and developers to efficiently assess system performance, make evaluation reproducible, improve development velocity, and ultimately build more trustworthy AI systems, aligning with the goal of moving faster and safer in AI deployment. Since its release on PyPI in Jun 2025, the tool has been downloaded over 16K times, across versions, by Dec 2025, demonstrating growing community interest.