Browser Use
Browser agents and stealth cloud browsers for AI: run web tasks, fill forms, extract data.
제작자가 쓴 설명(영어 원문)
Give any LLM its own computer — Docker sandboxes with bash, browser, docs, and sub-agents
이 세 가지는 절대 하나의 점수로 합치지 않아요. 의견, 측정, 인기는 서로 다른 것이니까요.
이 서버는 아직 쉬운 말로 된 요약을 쓰지 못했어요. 아래는 레지스트리에서 읽어 온 사실이에요.
쓰고 있는 AI 앱을 고른 다음 설정을 복사하세요.
제작자가 레지스트리에 설치 정보를 올리지 않았어요. 아래 제작자의 README를 확인하세요.
내 컴퓨터에서 실행돼요. 설치하면 AI 앱에 도구 목록이 나타나요.
누구나 볼 수 있는 정보를 점검했고, 코드는 읽지 않았어요. 해당하지 않는 항목은 개수에서 뺐어요. · 2026년 11월 6일 점검
레지스트리 이름이 GitHub 계정 Yambr 기준으로 확인되었고, 이 계정이 저장소를 소유하고 있어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
내 컴퓨터에서 실행되는 서버라 인터넷 엔드포인트가 없어요
내 컴퓨터에서 실행되며, 대신 로컬 설정을 사용해요
내 컴퓨터에서 실행돼요. 도구 목록은 인터넷 엔드포인트가 있는 서버에서만 읽을 수 있어요
레지스트리에서 이 카테고리의 활성 서버 전체와 비교했어요. 스타는 게시자가 저장소를 소유한 경우에만 세요.
해당하는 기본 점검 1개 중 1개를 통과했어요. 코드는 읽지 않았어요. 키나 토큰을 넣기 전에 서버가 어떤 권한을 요구하는지 확인하고, 꼭 필요한 권한만 있는 키를 쓰세요.
레지스트리에 필수 키가 나와 있지 않아요. 다만 처음 쓸 때 로그인을 요구하는 서버도 있어요.
내 컴퓨터에서 실행돼요. AI 앱이 필요할 때 실행하고, 데이터는 제작자의 서버를 거치지 않아요(서버 자체가 온라인 서비스를 호출하는 경우는 제외).
GitHub 저장소는 마지막으로 – 변경됐고, 최신 버전(0.8.12.6)은 7개월 전 레지스트리에 올라왔어요.
Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요.
같은 카테고리에는: Browser Use, Chrome DevTools MCP, MCP Server Browser
같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.
카드 보는 법:★ GitHub 스타i세 기둥: 리뷰 · 점검 · 사용 정도i업데이트 = 최신 버전을 레지스트리에 공개한 날i
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
제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.
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_
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