WebsiteBench

Given a production-level website they can explore through a browser, agents must reverse-engineer a full-stack version that matches its appearance and behavior as closely as possible.

This dataset holds the 70 WebsiteBench tasks in the native task format of the Harbor framework (version 0.25.0). The same task contents are published on Harbor Hub as websitebench/WebsiteBench@v1; every task has the same Harbor content hash in both places. Code, documentation, and the evaluation commands live on GitHub.

• Project page: website-bench.com
• Code: TIGER-AI-Lab/WebsiteBench

Download and run

No login is needed. Install the websitebench package, which pins Harbor 0.25.0, and download with it. It checks every task against its pinned Harbor content hash and restores the executable permissions that Hugging Face does not store:

pip install websitebench
websitebench download --source hf --task medium
harbor run --path ./local-tasks/medium --agent oracle

Or from a clone of the GitHub repository:

git clone https://github.com/TIGER-AI-Lab/WebsiteBench.git
cd WebsiteBench
uv sync
uv run python scripts/download_tasks.py --source hf --task medium

Omit --task to download all 70 tasks (9.7 GB) into local-tasks/. Hugging Face rate-limits anonymous downloads, so a full download can pause for a few minutes at a time; logging in with hf auth login, or setting HF_TOKEN, raises the limit.

Most tasks request 8 CPUs, 8 GiB of memory and 20 GiB of disk; see the GitHub README for requirements. The GitHub documentation describes coding-agent runs, batch runs, and results.

Contents

Path	Contents
tasks/<task>/instruction.md	The reconstruction task given to the agent
tasks/<task>/task.toml	Harbor configuration, resources, and network policies
tasks/<task>/environment/	Agent environment, browser gateway, mailbox, and the reference website
tasks/<task>/solution/	The bundled oracle implementation installed by solve.sh
tasks/<task>/tests/	The separate verifier, its oracle runtime, and the hidden test suite
manifest.json	SHA-256, size, and mode of every file, and each task's Harbor content hash

In four tasks with very large static sites (backcountry, columbia, eatthismuch, warbyparker), each application copy stores its clone/static/ and clone/backend/content/ files in one websitebench-static.tar. The task's Dockerfiles, tests/test.sh and solution/solve.sh unpack it, so the files inside the containers are the same as in an unpacked copy.

Scoring

Each task has 302 to 2,016 hidden test cases, 46,916 in total. A case passes when its functional checks pass and every visual checkpoint's flat-baseline-normalized RGB SSIM reaches 0.99. A task's reward is the number of passed cases divided by the number of cases.

License and terms

The benchmark code, task definitions, and verifier are released under the Apache-2.0 license. The reference websites reproduce third-party sites for evaluation; their content remains the property of its owners and is provided for non-commercial research only.

The task definitions carry a harbor-canary GUID. Please exclude documents containing it from training data.

Citation

@misc{zhang2026websitebench,
  title  = {WebsiteBench: Can AI Agents Rebuild Websites through Browser Exploration?},
  author = {{WebsiteBench Authors}},
  year   = {2026},
  url    = {https://github.com/TIGER-AI-Lab/WebsiteBench},
}