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license: apache-2.0
language:
- en
size_categories:
- n<1K
task_categories:
- other
tags:
- agents
- web-agents
- benchmark
- google-workspace
- tool-use
pretty_name: KNOWS Benchmark
configs:
- config_name: default
data_files:
- split: test
path: data/test.jsonl
---
# KNOWS Benchmark
KNOWS evaluates web agents on the work people actually do in Google Workspace: writing documents,
building spreadsheets, and composing slide decks that require web research, multi-step tool use, and
faithful grounding in retrieved sources.
This dataset contains the **task definitions** — the prompt an agent receives, plus the structured
evaluation rubric used to grade the artifact it produces.
| | |
|---|---|
| Tasks | **110** (22 templates × 5 instances) |
| Domains | 20 |
| Mean prompt length | 204 words |
| Evaluation steps per task | ~25 |
| Artifact | Templates | Tasks |
|---|---|---|
| Google Docs | 5 | 25 |
| Google Sheets | 9 | 45 |
| Google Slides | 8 | 40 |
- **Code and evaluators:** https://github.com/alexgill321/KNOWS-benchmark
- **Project page:** https://alexgill321.github.io/KNOWS-benchmark
## This dataset does not score anything
Each task is graded by a task-specific Python evaluator that inspects the *finished artifact*
through the Google Workspace APIs and its rendered form — combining deterministic comparisons,
tolerance-based matching, geometric layout tests, document-structure inspection, browsing-trace
verification, and LLM/VLM judgements. Those evaluators live in the GitHub repository, not here.
To actually run and score the benchmark, follow
[RUNNING_STANDALONE.md](https://github.com/alexgill321/KNOWS-benchmark/blob/main/RUNNING_STANDALONE.md),
or use the reference harnesses:
[BrowserGym-Knows](https://github.com/farhanishmam/BrowserGym-Knows) and
[AgentLab-Knows](https://github.com/farhanishmam/AgentLab-Knows).
Use this dataset when you want the prompts and rubric as data — for analysis, task selection, or
driving your own harness.
## Fields
| field | description |
|---|---|
| `task_id` | Registered harness id, e.g. `knows.docs_1_formal_letter.1`. For nine families this differs from `family` (`sheets_6_investmenttracker` registers as `knows.sheets_6_stock_tracker`) — use `task_id` to launch a task, `family` to find it on disk |
| `short_id` | Short internal id (e.g. `11a`); `null` where none was assigned |
| `family` | Task template, e.g. `docs_11_personal_recipe_ocr` |
| `instance`, `instance_num` | Which of the five instances |
| `artifact` | `Docs`, `Sheets`, or `Slides` |
| `group`, `domain` | Use-case grouping and domain label |
| `prompt` | The agent-facing instruction, verbatim from `task.md` (which itself carries `{{...}}` placeholders where a task writes to Drive — see below) |
| `checkpoints` | Rubric: `[{n, title, description, steps[]}]`, each step carrying `name`, `description`, `text`, `category`, `llm_vlm_primary` |
| `n_steps` | Number of evaluation steps |
| `total_points` | Points available for the task |
| `requires_browsing_history` | Whether the evaluator checks the agent's visited URLs |
| `source_assets` | Hosted, view-only Drive files the prompt references — usable as-is |
| `evaluator_url`, `checkpoints_url` | Links to the evaluator and rubric on GitHub |
Step `category` labels come from the benchmark's step-type taxonomy (`structural_check`,
`information_retrieval`, `formatting_check`, `content_check`, `spatial_check`, `web_visit_check`,
`visual_check`), with `llm_vlm_primary` marking steps decided primarily by a model judge.
## External assets
Six task families reference Google Drive files from their prompts, in two kinds.
**Files the task reads.** Listed in `source_assets` (30 across the benchmark) and left in the prompt
verbatim. These are hosted copies shared *anyone-with-link, view only*, so they work as-is and a
benchmark run cannot modify them.
**Locations the task writes into.** Ten tasks — all of `sheets_10_paper_sorting` and
`slides_17_removeimagesaddplaceholders` — ask the agent to upload files into a Drive folder, or to
edit a scratch copy of a deck. A write destination cannot be shared between users, so the benchmark
does not ship one: the prompt carries a `{{TOKEN}}` you replace with a location in your own Drive.
| placeholder | what to supply |
|---|---|
| `{{OUTPUT_FOLDER_URL}}` | A folder the agent may write into. For `sheets_10` it must contain `pdfs/` and `figures/` subfolders — the prompt names those by name |
| `{{IMAGES_FOLDER_URL}}` | A folder for the images extracted from the deck |
| `{{WORKING_COPY_URL}}` | An editable copy of the source presentation (`slides_17` instance 1 only) |
The code repository provisions all of them for you, for both families at once:
```bash
python src/browsergym/knows/eval/tasks/provision_run_targets.py --all
```
It creates a fresh folder tree per run and writes the URLs to `run_targets.json`, which the
harness and the evaluators both read — so the folder the agent uploaded to is the one that gets
graded. On your own harness, substitute with a plain string replace on `prompt`. Tasks whose
prompt contains no `{{` need no setup at all. See
[ASSETS.md](https://github.com/alexgill321/KNOWS-benchmark/blob/main/ASSETS.md) for the full layout.
## Maintenance and versioning
Tasks are curated to be time-agnostic and independent of any single website, but some reference live
pages that can change. External sources the evaluators depend on are verified periodically; if a
change significantly alters a task's difficulty or solvability, the task is updated or replaced with
one of similar scope. Every change ships as a new tagged release on GitHub, and prior versions stay
available, so results remain reproducible. **Please report the benchmark version alongside your
results.** Issues can be reported via the
[templates](https://github.com/alexgill321/KNOWS-benchmark/issues/new/choose).
This dataset corresponds to benchmark **[v1.1.0](https://github.com/alexgill321/KNOWS-benchmark/releases/tag/v1.1.0)**.
## Licence
Apache-2.0, matching the code repository.
## Citation
```bibtex
@inproceedings{gill2026knows,
title = {The Hard Part Comes After Search: Benchmarking Web Agents on
Synthesizing, Organizing, and Displaying Knowledge},
author = {Gill, Alexander and Ishmam, Md Farhan and Nguyen, Xuyen and
Bhat, Neha and DeYoung, Parker Henry and
Hashemi Chaleshtori, Fateme and Stringham, Nathan and
Marino, Kenneth and Marasovi\'{c}, Ana},
booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2026},
year = {2026},
url = {https://arxiv.org/abs/2609.30604},
eprint = {2609.30604},
archivePrefix = {arXiv}
}
```
Please also state which benchmark version you ran (above).
|