One-Eval: An Agentic System for Automated and Traceable LLM Evaluation

TL;DR

One-Eval is an agentic system that automates and makes LLM evaluation traceable by converting natural language requests into customizable, executable workflows with integrated benchmarks, metrics, and human review.

Contribution

It introduces a novel system that automates LLM evaluation workflows from natural language requests, enhancing reproducibility and traceability in industrial settings.

Findings

Supports end-to-end evaluation with minimal effort

Enables reproducible and customizable evaluation workflows

Includes human-in-the-loop checkpoints for review

Abstract

Reliable evaluation is essential for developing and deploying large language models, yet in practice it often requires substantial manual effort: practitioners must identify appropriate benchmarks, reproduce heterogeneous evaluation codebases, configure dataset schema mappings, and interpret aggregated metrics. To address these challenges, we present One-Eval, an agentic evaluation system that converts natural-language evaluation requests into executable, traceable, and customizable evaluation workflows. One-Eval integrates (i) NL2Bench for intent structuring and personalized benchmark planning, (ii) BenchResolve for benchmark resolution, automatic dataset acquisition, and schema normalization to ensure executability, and (iii) Metrics \& Reporting for task-aware metric selection and decision-oriented reporting beyond scalar scores. The system further incorporates human-in-the-loop checkpoints for review, editing, and rollback, while preserving sample evidence trails for debugging and auditability. Experiments show that One-Eval can execute end-to-end evaluations from diverse natural-language requests with minimal user effort, supporting more efficient and reproducible evaluation in industrial settings. Our framework is publicly available at https://github.com/OpenDCAI/One-Eval.