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MCP服务器演示

MCP Server 是 c/ua 项目中的一个核心模块,主要用于与 Claude Desktop和其他 MCP 客户端进行交互。它通过 Docker 容器化的方式,使 AI 代理能够在虚拟环境中控制完整的操作系统,支持本地或云端的部署。MCP Server 提供了一种简单的方式来启动和管理计算机使用代理,帮助用户自动化桌面任务。它支持多种代理循环模式,如 UITARS、OpenAI、Anthropic 和 OmniParser,适用于不同的视觉模型和任务需求。通过 MCP Server,用户可以轻松实现桌面操作的自动化,提升工作效率。

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README

Cua logo

Build, benchmark, and deploy agents that use computers

cua.ai Discord Twitter Documentation
trycua%2Fcua | Trendshift

CuaBot - Co-op computer-use for any agent

cuabot gives any coding agent a seamless sandbox for computer-use. Individual windows appear natively on your desktop with H.265, shared clipboard, and audio.

npx cuabot                 # Setup onboarding
# Run any agent in a sandbox
cuabot claude              # Claude Code
cuabot openclaw            # OpenClaw in the sandbox

# Run any GUI workflow in a sandbox
cuabot chromium
cuabot --screenshot
cuabot --type "hello"
cuabot --click <x> <y> [button]

Get Started | Installation | First spotted at ClawCon

cuabot screenshot
cuaXclawdbot_nbg

Beyond cuabot — explore our SDKs, benchmarks, and sandboxes for your own computer-use agents.

Choose Your Path

Cua Cua-Bench Lume

Cua - Agentic UI Automation & Code Execution

Build agents that see screens, click buttons, and complete tasks autonomously. Run isolated code execution environments for AI coding assistants like Claude Code, Codex CLI, or OpenCode.

# Requires Python 3.12 or 3.13
from computer import Computer
from agent import ComputerAgent

computer = Computer(os_type="linux", provider_type="cloud")
agent = ComputerAgent(model="anthropic/claude-sonnet-4-5-20250929", computer=computer)

async for result in agent.run([{"role": "user", "content": "Open Firefox and search for Cua"}]):
    print(result)

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.

# Install and create base image
cd 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://raw.githubusercontent.com/trycua/cua/main/libs/lume/scripts/install.sh)"

# Pull & start a macOS VM
lume run macos-sequoia-vanilla:latest

Get Started | FAQ | CLI Reference


Packages

| Package | Description | | --------------------------------------------------------------------- | ---------------------------------------------------------- | | cuabot | Multi-agent computer-use sandbox CLI | | cua-agent | AI agent framework for computer-use tasks | | cua-computer | SDK for controlling desktop environments | | 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

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.


Stargazers over time

Thank you to all our GitHub Sponsors!

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help

运行方式说明

cloud

托管运行

托管运行通常表示这个 MCP Server 由服务方环境承载,用户一般按页面提供的连接方式或授权流程接入,不需要在本地长期启动一个 MCP 进程

  1. 打开服务方连接页
  2. 完成授权或复制端点
  3. 在 MCP 客户端中连接
terminal

本地运行 / 其它方式

本地运行通常需要用户在自己的电脑或服务器上安装依赖,把 server_config 复制到 MCP 客户端,并按 env_schema 补齐环境变量、密钥或其它配置

  1. 复制 server_config
  2. 安装所需依赖
  3. 补齐环境变量后重启客户端