Daily AI Insight
数据源洞察报告

免责声明

本站内容由 AI 自动聚合、分析与生成,仅供信息参考与学习交流,不构成投资、法律、医疗或其他重大决策建议。请结合原始信源独立判断,作者不对因使用本站内容而产生的任何后果承担责任。

关于本项目

个人开源实验项目,由 @sqliang 构建与维护。

在 GitHub 查看源码

© 2026 Daily AI Insight Engine · Built with AI-assisted pipelines

← 返回 GitHub Trending
Scout·清单github.com

Article Intelligence

trycua/cua

打开原文 ↗

先看结论

Scale computer-use 2.0 with open-source drivers, cross-OS fleets, and benchmarks for training, evaluation, and data generation. Scale computer-use 2.0 with open-source drivers, cross-OS fleets, and benchmarks for training, evaluation, and data generation. Choose Your Path Building your own agent? Start with Cua · Giving a coding agent a computer? Cua Drivers · Evaluating or training models? Cua Bench · Need macOS VMs? Lume Cua Drivers - Background computer-use on macOS, Windows, and Linux Drive native desktop apps in the background. Agents click, type, and verify without stealing the cursor or focus. Use the same CLI and MCP server on macOS, Windows, and Linux from Claude Code, Cursor, Codex, OpenClaw, and custom clients. Linux supports X11 and compositor-specific Wayland routes with explicit limits for raw background input. macOS / Linux /bin/bash -c "$(curl -fsSL https://cua.ai/driver/install.sh)" Windows (PowerShell) irm https://cua.ai/driver/install.ps1 | iex Then follow the post-install instructions. Drive your first app | Installation | CLI Reference Source documentation, architecture notes, and the optional agent skill pack live in libs/cua-driver/README.md. Cua - Agent-Ready Sandboxes for Any OS Build agents that see screens, click buttons, and complete tasks autonomously. One API for any VM or container image — cloud or local. pip install cua # Requires Python 3.11 or later from cua import Sandbox, Image # Same API regardless of OS or runtime async with Sandbox.ephemeral(Image.linux()) as sb: # or .macos() .windows() .android() result = await sb.shell.run("echo hello") screenshot = await sb.screenshot() await sb.mouse.click(100, 200) await sb.keyboard.type("Hello from Cua!") await sb.mobile.gesture((100, 500), (100, 200)) # multi-touch gestures Linux container Linux VM macOS Windows Android BYOI (.qcow2, .iso) Cloud (cua.ai) ✅ ✅ ✅ ✅ ✅ 🔜 soon Local (QEMU) ✅ ✅ ✅ ✅ ✅ ✅ Get Started | Examples | API Reference Cua-Bench - Benchmarks & RL Environments Evaluate computer-use agents on OSWorld, ScreenSpot, Windows Arena, and custom tasks. Export trajectories for training. # Clone, install, and create base image git clone https://github.com/trycua/cua && cd cua/cua-bench uv tool install -e . && cb image create linux-docker # Run benchmark with agent cb run dataset datasets/cua-bench-basic --agent cua-agent --max-parallel 4 Get Started | Partner With Us | Registry | CLI Reference Lume - macOS Virtualization Create and manage macOS/Linux VMs with near-native performance on Apple Silicon using Apple's Virtualization.Framework. # Install Lume /bin/bash -c "$(curl -fsSL https://cua.ai/lume/install.sh)" # Create and start a vanilla macOS VM from an Apple restore image curl -L "$(lume ipsw | tail -n 1)" -o ~/Downloads/macos-tahoe.ipsw lume create macos-tahoe --ipsw ~/Downloads/macos-tahoe.ipsw --unattended tahoe lume run macos-tahoe The --unattended option prepares the installed guest offline. The built-in sequoia and tahoe presets create the lume user, enable SSH, configure autologin, and disable sleep and screen locking. The default credentials are lume / lume. The Tahoe flow is E2E verified. Sequoia may still open the Accessibility step of Setup Assistant on its first display boot; see issue #2155. Get Started | FAQ | CLI Reference Packages Package Description cua-driver Background computer-use agent for macOS, Windows, and Linux cua-agent AI agent framework for computer-use tasks cua-sandbox SDK for creating and controlling sandboxes cua-computer-server Driver for UI interactions and code execution in sandboxes cua-bench Benchmarks and RL environments for computer-use lume macOS/Linux VM management on Apple Silicon lumier Docker-compatible interface for Lume VMs Resources Documentation — Guides, examples, and API reference Blog — Tutorials, updates, and research Discord — Community support and discussions GitHub Issues — Bug reports and feature requests Citation If Cua supports your research, please cite the software: @software{cua2025, author = {{Cua AI, Inc.}}, title = {Cua}, year = {2025}, url = {https://github.com/trycua/cua}, license = {MIT} } For reproducibility, include the Cua release or commit used in your experiments. Citation metadata is also available in CITATION.cff. Contributing We welcome contributions! See our Contributing Guidelines for details. License MIT License — see LICENSE for details. Third-party components have their own licenses: Kasm (MIT) OmniParser (CC-BY-4.0) Optional cua-agent[omni] includes ultralytics (AGPL-3.0) Trademarks Apple, macOS, Ubuntu, Canonical, and Microsoft are trademarks of their respective owners. This project is not affiliated with or endorsed by these companies. Thank you to all our GitHub Sponsors!

当前文章只有 scout 基础信息,尚无提取或分析指标。
01

提取事实

结构化事实、实体识别与逻辑链

这篇文章尚未完成 extraction,当前只展示 manifest 中的基础信息。

02

影响与价值

技术/商业影响、关注焦点与受影响对象

这篇文章尚未完成 analyze,暂无影响与价值判断。

03

风险、机会与行动

结合机会清单和风险矩阵判断后续关注重点

这篇文章尚未完成 analyze,暂无风险与行动判断。

阅读导览

点击跳转到正文区块

NAV
01提取事实↘
02影响与价值
↘
03风险、机会与行动↘

同源文章

上一篇1jehuang/jcode下一篇MoonshotAI/kimi-cli