---
license: other
task_categories:
- question-answering
language:
- en
tags:
- data-science-agents
- benchmark
- agent-evaluation
size_categories:
- 100<n<1K
configs:
- config_name: krama_full
  data_dir: .
---

# KramaBench (evo-eval `krama_full`)

End-to-end data-science agent benchmark, packaged for the [evo-eval](https://gitcode.com/datagallery/evo-eval) evaluation framework (`evo_eval.dataset.v1` generic dataset schema).

Source: KramaBench (Lai et al., 2025), https://github.com/mitdbg/kramabench. If you use this data, please cite the upstream preprint:

```bibtex
@misc{lai2025KramaBench,
  title  = {KramaBench: Evaluating End-to-End Data-Science Agents},
  author = {Eugenie Lai and Gerardo Vitagliano and Ziyu Zhang and *et al.*},
  year   = {2025},
}
```

## Layout (repo root = evo-eval `DATA_ROOT`)

- `krama_full/tasks.jsonl` — 104 top-level tasks, integer ids `1..104` (one per line: `task_id`, `query.text`, `assets_dir`, `meta`)
- `krama_full/references/{task_id}.json` — golden answers with `answer_type` (`numeric_exact` / `string_exact` / `list_exact` / `numeric_approximate` / `list_approximate` / `string_approximate`)
- `krama_full/assets/{domain}/` — per-domain input data (copied from upstream `data/{domain}/input`)
- `krama_full/subtasks/` — 631 per-step subtasks (`N-K` ids derived from parent task `N`), for intermediate-step diagnostics; not part of the official top-level metric

## Notes

- Original KramaBench task ids (e.g. `legal-hard-1`) are preserved in `meta.source_task_id` (tasks) and `meta.source_subtask_id` (subtasks).
- Deterministic mapping: top-level tasks sorted by `(domain, source_task_id)`, then numbered `1..104`; subtask `K` of task `N` gets id `N-K`.
- Scoring (official metric dispatch by `answer_type` → success / f1 / f1_approximate / rae_score / llm_paraphrase) ships with the evo-eval dataset template `configs/datasets/krama_full/`, not with this data repo.