AIエージェント
OSWorld-V2
Benchmark computer-use agents on long, real-world desktop tasks.
★ 353 スターApache-2.0
ガイドを見る →SWE-bench_Pro-osは、scaleapiが公開しているオープンソースのAIエージェントプロジェクトです（GitHub: scaleapi/SWE-bench_Pro-os）。GitHubスターは539、フォークは101で、MITライセンスの下で公開されています。GitHubでの最終更新は2026年9月22日です。
A benchmark testing whether agents can solve long-horizon coding tasks.
Decide what you want the project to do before installing anything.
Grab the source and skim the README for setup requirements.
Install dependencies and add any API keys or settings it needs.
Launch it and follow the example workflow from the docs.
Tasks beyond quick fixes.
Realistic software work.
Builds on a trusted benchmark.
From a leading data lab.
MIT licensed, inspectable.
Measures agents, not just models.
Download the source and move into the project folder.
git clone https://github.com/scaleapi/SWE-bench_Pro-os.git
cd SWE-bench_Pro-osCreate a virtual environment and install the requirements.
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txtTreat these as starting points - the README documents the exact commands.
python examples/quickstart.pyIt is an open-source project at scaleapi/SWE-bench_Pro-os with 101 forks. Check the repo's contributors and recent commits to judge how active maintenance is.
Yes - it is MIT licensed, so you can use, modify, and distribute it freely, including in commercial projects.
Running the evaluation is free; the model under test may use your own paid API keys.
Both test coding agents; SWE-bench Pro emphasizes long-horizon software engineering tasks in the SWE-bench tradition. Run both for a fuller picture.
Clone the repo, read the README's quickstart, and run the provided example - most projects get you to a first result in minutes.
Benchmark computer-use agents on long, real-world desktop tasks.
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