Browser Use
Browser agents and stealth cloud browsers for AI: run web tasks, fill forms, extract data.
作者による説明(英語の原文)
Give any LLM its own computer — Docker sandboxes with bash, browser, docs, and sub-agents
この3つを1つのスコアにまとめることはしません。意見、測定、人気はそれぞれ別のものだからです。
このサーバーのやさしい解説はまだ書いていません。以下はレジストリから読み取った事実です。
使っているAIアプリを選んで、設定をコピーしてください。
作者はレジストリにインストール情報を載せていません。下にある作者のREADMEをご覧ください。
パソコンで動くサーバーです。インストールすると、ツール一覧がAIアプリに表示されます。
誰でも見られる情報をチェックしており、コードは読んでいません。当てはまらない項目は数に入れていません。 · 2026年11月6日にチェック
レジストリ名は、リポジトリを所有するGitHubアカウント Yambr で確認済みです
このリポジトリはまだGitHubから読み取っていません
このリポジトリはまだGitHubから読み取っていません
これはパソコンで動くため、インターネット上のエンドポイントはありません
パソコンで動き、代わりにローカルの設定を使います
パソコンで動くサーバーです。ツール一覧を読み取れるのは、インターネット上のエンドポイントがある場合だけです
レジストリでこのカテゴリにある有効なすべてのサーバーと比較しています。スターは、公開者がリポジトリの所有者である場合だけ数えます。
当てはまる基本チェック1項目のうち1項目に合格しました。コードは読んでいません。キーやトークンを追加する前に、サーバーがどんなアクセスを求めているかを確認し、必要な権限だけを持つキーを使ってください。
レジストリには必須のキーは載っていません。ただし、最初に使うときにサインインを求めるサーバーもあります。
あなたのパソコンで動きます。必要なときにAIアプリが起動し、データは作者のサーバーを通りません(サーバー自体がオンラインサービスを呼び出す場合を除きます)。
GitHubリポジトリの最終更新は–、最新バージョン(0.8.12.6)がレジストリに届いたのは7 か月前です。
Claude Desktop、Claude Code、Cursor、VS CodeなどのMCP対応アプリで使えます。
同じカテゴリには:Browser Use, Chrome DevTools MCP, MCP Server Browser
同じカテゴリで、ほかの作者が作ったサーバー。スターの多い順。
カードの見方:★ GitHubスターi3つの柱:レビュー · チェック · 利用状況i更新=最新バージョンをレジストリに公開した日i
Browser agents and stealth cloud browsers for AI: run web tasks, fill forms, extract data.
MCP server for Chrome DevTools
MCP server for browser use access
作者のリポジトリから書かれたとおりにコピーしたものです。PopNx MCPは内容を確認・推奨していません。HTML、画像、スクリプトは取り除いています。リンクはこのサイトの外へ移動します。
MCP server that gives any LLM its own computer — managed Docker workspaces with live browser, terminal, code execution, document skills, and autonomous sub-agents. Self-hosted, open-source, pluggable into any model.
ARCHIVED. This repository is no longer developed or maintained. It receives no fixes, no security updates and no dependency updates, and issues and pull requests are closed. Do not run it in production.
Open Computer Use was a proof of concept for giving an LLM a real computer. Its successor is Wide Moat: Open WebUI with Open Terminal, a browser, and a Kubernetes sandbox isolated with gVisor, with ingress sidecars and egress through a MITM proxy. The isolation and credential-handling problems listed under Known limitations are addressed in that architecture, not in this codebase. Try it at ai.lab.widemoat.ai.
The code stays available under FSL-1.1-Apache-2.0: use it, fork it, self-host it. Each release converts to Apache-2.0 two years after publication.
An MCP server that gives any LLM a fully-equipped Ubuntu sandbox with isolated Docker containers. Think of it as your AI's computer — it can do everything a developer can do:
Built for production multi-user deployments. Tested with 1,000+ MAU. Each chat session runs in its own isolated Docker container — the AI can install packages, create files, run servers, and nothing leaks between users. Works seamlessly across MCP clients: start with Open WebUI today, switch to Claude Desktop or n8n tomorrow — same backend, no migration.
| Feature | Open Computer Use | Claude.ai (Claude Code web) | open-terminal | OpenAI Operator |
|---|---|---|---|---|
| Self-hosted | Yes | No | Yes | No |
| Any LLM | Yes (OpenAI-compatible) | Claude only | Any (via Open WebUI) | GPT only |
| Code execution | Full Linux sandbox | Sandbox (Claude Code web) | Sandbox / bare metal | No |
| Live browser | CDP streaming (shared, interactive) | Screenshot-based | No | Screenshot-based |
| Terminal + Claude Code | ttyd + tmux + Claude Code CLI | Claude Code web (built-in) | PTY + WebSocket | N/A |
| Skills system | 13 built-in (auto-injected) + custom | Built-in skills + custom instructions | Open WebUI native (text-only) | N/A |
| Container isolation | Docker (runc), per chat | Docker (gVisor) | Shared container (OS-level users) | N/A |
Works with any MCP-compatible client: Open WebUI, Claude Desktop, LiteLLM, n8n, or your own integration. See docs/COMPARISON.md for a detailed comparison with alternatives.
See docs/FEATURES.md for architecture details and docs/SCREENSHOTS.md for all screenshots.
Pro tip: Create skills with Claude Code in the terminal, then use them with any model in the chat. Skills are model-agnostic — write once, use everywhere.
Multi-CLI sub-agent runtime (v0.9.2.1+): The sub-agent dispatch supports Claude Code (default), OpenAI Codex, and OpenCode (with OpenRouter / qwen / DeepSeek / 75+ providers). Flip
SUBAGENT_CLI=claude|codex|opencodein.env— see docs/multi-cli.md for the worked OpenCode + qwen3-coder + OpenRouter recipe.
docs/future-architecture/ holds design notes for a Kubernetes architecture that was not built in this repository. Docker Compose is the only path this code supports.
| Path | URL | What you need | Best for |
|---|---|---|---|
| Free online demo — Open WebUI + Computer Use, models included | [ai.lab.widemoat.ai](https://ai.lab.widemoat.ai) | GitHub or Google sign-in | Trying it end-to-end in 30 seconds |
| Self-host | Quick Start below | Docker, ~15 min first build | Full control, air-gapped, heavy use |
OAuth only — no email/password, no SMS. On ai.lab.widemoat.ai models are bundled as a free convenience. The hosted MCP endpoint is offline; see docs/CLOUD.md.
git clone https://github.com/Wide-Moat/open-computer-use.git
cd open-computer-use cp .env.example .env
docker compose up --build
docker compose -f docker-compose.webui.yml up --build
Open http://localhost:3000 — Open WebUI with Computer Use ready to go.
Note: Two separate docker-compose files:
docker-compose.yml(Computer Use Server) anddocker-compose.webui.yml(Open WebUI). They communicate vialocalhost:8081. This mirrors real deployments where the server and UI run on different hosts.
After adding a model in Open WebUI, go to Model Settings and set:
| Setting | Value | Why |
|---|---|---|
| Function Calling | Native | Required for Computer Use tools to work |
| Stream Chat Response | On | Enables real-time output streaming |
Without Function Calling: Native, the model won't invoke Computer Use tools.
| Category | Tools |
|---|---|
| Languages | Python 3.12, Node.js 22, Java 21, Bun |
| Documents | LibreOffice, Pandoc, python-docx, python-pptx, openpyxl |
| pypdf, pdf-lib, reportlab, tabula-py, ghostscript | |
| Images | Pillow, OpenCV, ImageMagick, sharp, librsvg |
| Web | Playwright (Chromium), Mermaid CLI |
| AI | Claude Code CLI, Playwright MCP |
| OCR | Tesseract (configurable languages) |
| Media | FFmpeg |
| Diagrams | Graphviz, Mermaid |
| Dev | TypeScript, tsx, git |
13 built-in public skills + 14 examples:
| Skill | Description |
|---|---|
| pptx | Create/edit PowerPoint presentations with html2pptx |
| docx | Create/edit Word documents with tracked changes |
| xlsx | Create/edit Excel spreadsheets with formulas |
| Create, fill forms, extract, merge PDFs | |
| sub-agent | Delegate complex tasks to Claude Code |
| playwright-cli | Browser automation and web scraping |
| describe-image | Vision API image analysis |
| frontend-design | Build production-grade UIs |
| webapp-testing | Test web applications with Playwright |
| doc-coauthoring | Structured document co-authoring workflow |
| test-driven-development | TDD methodology enforcement |
| skill-creator | Create custom skills |
| gitlab-explorer | Explore GitLab repositories |
14 example skills: web-artifacts-builder, copy-editing, social-content, canvas-design, algorithmic-art, theme-factory, mcp-builder, and more.
See docs/SKILLS.md for details.
The server speaks standard MCP over Streamable HTTP. Point any MCP client at your own deployment.
http://localhost:8081/mcp. Quick sanity check: curl -X POST http://localhost:8081/mcp \
-H "Content-Type: application/json" \ -H "X-Chat-Id: test" \ -d '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2024-11-05","capabilities":{},"clientInfo":{"name":"test","version":"1.0"}}}'
Full self-host integration guide (LiteLLM, Claude Desktop, custom clients): [docs/MCP.md](docs/MCP.md). The per-chat system prompt rides **six redundant MCP-native channels** (tool descriptions, `/home/assistant/README.md` in the sandbox, `InitializeResult.instructions`, `resources/list` for uploaded files, plus an HTTP `/system-prompt` endpoint for legacy integrations) — full map in [docs/system-prompt.md](docs/system-prompt.md).
All settings via .env:
| Variable | Default | Description |
|---|---|---|
OPENAI_API_KEY | — | LLM API key (any OpenAI-compatible) |
OPENAI_API_BASE_URL | — | Custom API base URL (OpenRouter, etc.) |
MCP_API_KEY | — | Bearer token for MCP endpoint |
DOCKER_IMAGE | open-computer-use:latest | Sandbox container image |
COMMAND_TIMEOUT | 120 | Bash tool timeout (seconds) |
SUB_AGENT_TIMEOUT | 3600 | Sub-agent timeout (seconds) |
SINGLE_USER_MODE | — | true = one container, no chat ID needed; false = require X-Chat-Id; unset = lenient |
PUBLIC_BASE_URL | http://computer-use-server:8081 | Browser-reachable URL of the Computer Use server. Baked into /system-prompt and returned to the Open WebUI filter in the X-Public-Base-URL response header — single source of truth for the public URL. Open WebUI filter URL requirements. |
CHAT_RESPONSE_MAX_TOOL_CALL_ITERATIONS, ORCHESTRATOR_URL, TOOL_RESULT_MAX_CHARS, TOOL_RESULT_PREVIEW_CHARS | — | Settings on the `open-webui` container (not CU-server). Required when embedding — see Required setup when embedding Open WebUI. |
| `POSTGRES_
冒頭だけを表示しています。続きはリポジトリで読めます