---
license: apache-2.0
task_categories:
  - text-generation
  - question-answering
  - other
language:
  - en
tags:
  - arxiv:2604.08523
  - benchmark
  - leaderboard
  - agent-benchmark
  - llm-benchmark
  - web-agents
  - browser-agent
  - browser-automation
  - ai-agent
  - evaluation
  - real-world-tasks
  - web-navigation
  - task-completion
  - clawbench
  - multimodal
pretty_name: 'ClawBench: Web Agent Benchmark'
size_categories:
  - n<1K
configs:
  - config_name: default
    data_files:
      - split: test
        path: data/train-00000-of-00001.parquet
arxiv: "2604.08523"
viewer: true
leaderboard: TIGER-Lab/ClawBench
---

# ClawBench Dataset

ClawBench is an open **benchmark** for AI web agents — the systems that drive a real browser to complete a user's task end-to-end. It scores agents on real, everyday online tasks (booking flights, ordering groceries, submitting job applications) across live websites.

|[**💻 Github**](https://github.com/reacher-z/ClawBench) | [**🏆 Leaderboard**](https://huggingface.co/spaces/TIGER-Lab/ClawBench) | [**📖 Paper**](https://arxiv.org/abs/2604.08523) | [**🌐 Website**](https://claw-bench.com) |


## 🚀 What's New

- **[2026.05.12]** Added the **V2 corpus** (130 newer tasks across 63 platforms) and 7 new models judged with `deepseek/deepseek-v4-pro` — see snapshot below. Companion V2 traces released at [`TIGER-Lab/ClawBenchV2Trace`](https://huggingface.co/datasets/TIGER-Lab/ClawBenchV2Trace).
- **[2026.05.04]** Reorganized to the `clawbench-eval` package. Single command for both V1 and V2: `clawbench run --corpus v2 --model <m> --harness hermes`.
- **[2026.04.18]** Published [`NAIL-Group/ClawBenchV1Trace`](https://huggingface.co/datasets/NAIL-Group/ClawBenchV1Trace) — full 5-layer execution traces (recording, actions, HTTP, agent messages, interception) for every V1 run.
- **[2026.04.06]** Paper preprint up: [arXiv:2604.08523](https://arxiv.org/abs/2604.08523) — *Can AI Agents Complete Everyday Online Tasks?*


## 🏆 Leaderboard

Live results — pulled from [`leaderboard/results.csv`](https://huggingface.co/datasets/TIGER-Lab/ClawBench/blob/main/leaderboard/results.csv) in this repo. Filter by corpus (v1 / v2 / all) and submit your model in the interactive Space:

[![Open the live ClawBench Leaderboard ↗](https://img.shields.io/badge/%F0%9F%8F%86%20Open%20the%20live%20Leaderboard-TIGER--Lab%2FClawBench-FFD21E?style=for-the-badge&logo=huggingface&logoColor=000)](https://huggingface.co/spaces/TIGER-Lab/ClawBench)

**V2 snapshot — refreshed 2026-05-12** (full scoring logic: [`eval/scoring.md`](https://github.com/reacher-z/ClawBench/blob/main/eval/scoring.md))

| Rank | Model | Harness | Intercepted | Reward | Pass / Total |
|------|-------|---------|-------------|--------|--------------|
| 1 | `claude-opus-4-7` *(partial)* | hermes | **54.7%** | 13.3% | 10 / 75 |
| 2 | `glm-5.1` | hermes | **48.5%** | 18.5% | 24 / 130 |
| 3 | `gpt-5.5` *(partial)* | hermes | **48.1%** | 11.1% | 9 / 81 |
| 4 | `deepseek-v4-pro` | hermes | **43.8%** | 10.0% | 13 / 130 |
| 5 | `openrouter/owl-alpha` | hermes | **14.6%** | 4.6% | 6 / 130 |
| 6 | `deepseek-v4-flash` | hermes | **3.1%** | 1.5% | 2 / 130 |

**Intercepted** (sort key) = fraction whose final HTTP request matched the per-task URL/method schema — Stage 1, deterministic, no judge. **Reward** = additionally requires an LLM judge (default `deepseek/deepseek-v4-pro`) — Stage 2. Rows ranked by Intercepted DESC, Reward as tiebreak. **V2 is Hermes-only**; alternative harnesses are evaluated separately. *Partial* = batch attempted < 130 V2 tasks; rates are over attempted, not over 130.. Companion traces in [`TIGER-Lab/ClawBenchV2Trace`](https://huggingface.co/datasets/TIGER-Lab/ClawBenchV2Trace). See [scoring.md](https://github.com/reacher-z/ClawBench/blob/main/eval/scoring.md), [live leaderboard Space](https://huggingface.co/spaces/TIGER-Lab/ClawBench).

**Submit a result** → run [`clawbench-eval`](https://pypi.org/project/clawbench-eval/) on your model and open a PR to [`leaderboard/results.csv`](https://huggingface.co/datasets/TIGER-Lab/ClawBench/blob/main/leaderboard/results.csv) — one row per (model × harness × corpus).

> **Companion datasets (raw traces):** [`NAIL-Group/ClawBenchV1Trace`](https://huggingface.co/datasets/NAIL-Group/ClawBenchV1Trace) (V1 runs) · [`TIGER-Lab/ClawBenchV2Trace`](https://huggingface.co/datasets/TIGER-Lab/ClawBenchV2Trace) (V2 runs, rolling) — `recording.mp4`, `requests.jsonl`, `actions.jsonl`, `agent-messages.jsonl`, `interception.json`, `run-meta.json` per model run.

## Dataset Structure

### Columns

| Column | Type | Description |
|--------|------|-------------|
| `task_id` | int | Unique task identifier |
| `instruction` | string | Task prompt sent to the agent |
| `metaclass` | string | High-level category (21 categories) |
| `class` | string | Fine-grained sub-category |
| `platform` | string | Target platform (144 unique platforms) |
| `sites` | list[string] | Domains involved in the task |
| `eval_schema` | string (JSON) | Request interception configuration |
| `time_limit` | int | Maximum time in minutes |
| `extra_info` | string (JSON) | Paths to additional context files |
| `shared_info` | string | Path to shared user profile |

### Additional Files

```
shared/
  alex_green_personal_info.json   # Shared dummy user profile used across all tasks
extra_info/
  004/grocery_list.json           # Task-specific context (32 tasks have extra info)
  007/meal_plan.json
  043/pet_info.json
  ...
```

- **`shared/alex_green_personal_info.json`** — A comprehensive dummy user persona (Alex Green) including personal details, address, work history, education, financial information, and preferences. All tasks share this identity.
- **`extra_info/`** — Task-specific supplementary files referenced by the `extra_info` column. 32 of 153 tasks include additional context such as grocery lists, job links, meeting details, etc.

### eval_schema

The `eval_schema` field configures the **request interceptor** — a mechanism that blocks the final HTTP request matching the specified URL pattern and method, preventing irreversible actions (checkout, form submission, etc.) from reaching the server. This allows safe evaluation on live websites.

```json
{
  "url_pattern": "taskrabbit\\.(com|ca)/(api/v\\d+/jobs|book/\\d+/confirm)",
  "method": "POST"
}
```

## Task Categories (metaclass)

| Category | Tasks | Example Platforms |
|----------|-------|-------------------|
| daily-life | 21 | Uber Eats, Instacart, Zillow |
| entertainment-hobbies | 15 | Goodreads, Eventbrite, Fandango |
| creation-init | 13 | ClickUp, Typeform, Ghost |
| office-secretary-tasks | 9 | Trello, Calendly, Purelymail |
| rating-voting | 10 | TripAdvisor, Glassdoor, Yelp |
| education-learning | 9 | Coursera, LeetCode, Blinkist |
| travel | 9 | Google Flights, Hipcamp, Airbnb |
| beauty-personal-care | 9 | TaskRabbit, Booksy, Soko Glam |
| pet-animal-care | 8 | Rover, Petfinder, Chewy |
| job-search-hr | 8 | Indeed, Greenhouse, ZipRecruiter |
| academia-research | 5 | Zotero, Overleaf, Google Scholar |
| and 10 more... | | |

## Usage

```python
from datasets import load_dataset

ds = load_dataset("TIGER-Lab/ClawBench", split="test")
print(ds[0])
```

## Citation

```bibtex
@article{zhang2026clawbench,
  title={ClawBench: Can AI Agents Complete Everyday Online Tasks?},
  author={Yuxuan Zhang and Yubo Wang and Yipeng Zhu and Penghui Du and Junwen Miao and Xuan Lu and Wendong Xu and Yunzhuo Hao and Songcheng Cai and Xiaochen Wang and Huaisong Zhang and Xian Wu and Yi Lu and Minyi Lei and Kai Zou and Huifeng Yin and Ping Nie and Liang Chen and Dongfu Jiang and Wenhu Chen and Kelsey R. Allen},
  journal={arXiv preprint arXiv:2604.08523},
  year={2026}
}
```