AIエージェント
OSWorld
OSWorld — benchmark multimodal agents on open-ended tasks in real computer environments.
★ 3.2k スターApache-2.0
ガイドを見る →OSWorld-V2は、xlang-ai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: xlang-ai/OSWorld-V2）。GitHubスターは353、フォークは53で、Apache-2.0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10月1日です。
Benchmark computer-use agents on long, real-world desktop 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.
Actual desktops, not mocks.
Dozens of steps, like real work.
Clicks, typing, navigation.
The software people actually use.
Standardized tasks and scoring.
Purpose-built for CUAs.
Download the source and move into the project folder.
git clone https://github.com/xlang-ai/OSWorld-V2.git
cd OSWorld-V2Isolate dependencies, then install requirements.
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txtLaunch using the entry point shown in the README.
python -m osworld_v2 # check README for the exact commandIt is an open-source project at xlang-ai/OSWorld-V2 with 53 forks. Check the contributors tab and recent commits to judge how active maintenance is.
Yes — Apache 2.0 allows commercial use and modification. Keep the license and notice files, and note it includes a patent grant.
Tasks spanning dozens of steps across real applications — not single-turn Q&A.
Virtualized OS environments per the setup docs; it's heavier than a chat benchmark.
Clone the repo and follow the README quickstart — the docs at https://osworld-v2.xlang.ai walk through setup.
OSWorld — benchmark multimodal agents on open-ended tasks in real computer environments.
Reliable computer use for any agent: drive real apps through accessibility trees, not pixel guessing.
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LibreAIカタログに掲載されたAIエージェントプロジェクトの中で最もスターの多いもの — OSWorld-V2が合わなかった場合に検討したい、現実的な候補リストです。
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カタログの中でOSWorld-V2とスター数が近いAIエージェントプロジェクト — 前後の棚もそのまま見て回れます。
Agentic memory for LLM agents, from a published research project.
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