OpenEval: Why LLM Evaluation Needs a Standard Format

Adha AK — Thu, 30 Jul 2026 03:40:11 +0000

Every LLM evaluation framework today invents its own test case format, its own grader definitions, and its own results schema. DeepEval, Promptfoo, Inspect AI, and lm-evaluation-harness all solve the same core problem (checking whether a model's output is correct) but none of them can read each other's eval datasets.

That means every time a team wants to compare frameworks, or move from a notebook prototype to a production eval pipeline, they end up hand-rewriting the same test cases over and over.

OpenEval is an attempt to fix that by defining a small, portable JSON Schema for eval test cases, graders, and results, plus tooling to move data between frameworks instead of retyping it.

What's in the repo:

A versioned JSON Schema spec for test cases, grader configs, and result records.

TypeScript and Python SDKs for reading and writing OpenEval-formatted datasets.

A CLI with validate, convert, init, and summarize commands.

Converters for popular frameworks so existing datasets can be brought in or exported out without a manual rewrite.

The project just published v1.0.0 to npm and PyPI, and issues are open on 17+ framework integrations if anyone wants to help wire up a converter for a framework not yet covered.

Repo: https://github.com/adhabnr-ux/openeval

Would love feedback from anyone who has hit this same portability problem while switching between eval tools.

DEV Community: Adha AK

OpenEval: Why LLM Evaluation Needs a Standard Format