Browser Use
Browser agents and stealth cloud browsers for AI: run web tasks, fill forms, extract data.
제작자가 쓴 설명(영어 원문)
Plain-English browser tests for AI agents: real Chrome, deterministic verdicts. Free, no API keys.
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.
ANTHROPIC_API_KEY비밀로 표시된 값은 내 계정의 열쇠예요. 절대 다른 사람과 공유하거나 채팅에 붙여 넣지 마세요.
필요한 것
어떤 앱을 쓰세요?
위에서 앱을 고르세요. 기억해 두었다가 다음부터는 버튼에 “…에 설치”라고 표시해요.
Claude Desktop → Settings → Developer → Edit Config를 열고 claude_desktop_config.json에 이 내용을 붙여 넣으세요
{
"mcpServers": {
"browserbash": {
"command": "npx",
"args": [
"-y",
"browserbash-cli"
],
"env": {
"ANTHROPIC_API_KEY": "your-value"
}
}
}
}터미널에서 이 명령어를 실행하세요
claude mcp add browserbash --env ANTHROPIC_API_KEY=your-value -- npx -y browserbash-cli실행하기 전에 빈 값을 채워 주세요.
프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"browserbash": {
"command": "npx",
"args": [
"-y",
"browserbash-cli"
],
"env": {
"ANTHROPIC_API_KEY": "your-value"
}
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
프로젝트의 .vscode/mcp.json에 붙여 넣으세요
{
"servers": {
"browserbash": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"browserbash-cli"
],
"env": {
"ANTHROPIC_API_KEY": "your-value"
}
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
이 서버는 내 컴퓨터에서 실행되는데, ChatGPT는 인터넷에 있는 서버에만 연결할 수 있어요. 다른 앱을 고르세요.
선택한 앱은 이 기기의 쿠키에만 저장돼요. 계정은 필요 없어요.
이 설정은 제작자의 레지스트리 정보로 만들었어요. README와 다르면 README를 따르세요.
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 8일 점검
레지스트리 이름이 GitHub 계정 PramodDutta 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.
입력해야 할 값: ANTHROPIC_API_KEY
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(1.5.1)은 4개월 전 레지스트리에 올라왔어요.
Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. 내 컴퓨터에 Node.js도 있어야 해요.
같은 카테고리에는: Browser Use, Chrome DevTools MCP, MCP Server Browser
같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.
카드 보는 법:↓ 주간 다운로드i★ 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.
제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.
Plain-English browser automation. No selectors. Free and open-source.
You write a plain-English objective. An AI agent drives a real Chrome browser step by step — no selectors, no scripts. Ollama-first, so it runs on free local models with no API keys and nothing ever leaves your machine.
npm install -g browserbash-cli
browserbash run "Open news.ycombinator.com and store the top story title as 'top_story'"
[Website](https://browserbash.com) · [Docs / Learn](https://browserbash.com/learn) · [Tutorials](https://browserbash.com/tutorials) · [npm](https://www.npmjs.com/package/browserbash-cli)
Both layers are swappable: the engine interprets the English, the provider runs the browser.
| Engine | What it is | License |
|---|---|---|
stagehand (default) | Stagehand — open-source AI browser automation framework by Browserbase. act/extract/observe/agent primitives, self-healing, supports Anthropic/OpenAI/Google models. | MIT |
builtin | In-repo Anthropic tool-use loop driving Playwright. Used automatically for grids Stagehand can't attach to (LambdaTest, BrowserStack). | Apache-2.0 |
| Provider | Where the browser runs | Engine | Auth |
|---|---|---|---|
local (default) | Chromium/Chrome on this machine | stagehand or builtin | none |
cdp | Any Chrome DevTools Protocol endpoint (your grid, docker, Playwright MCP-managed browser) | stagehand or builtin | none |
browserbase | Browserbase cloud browsers | stagehand only | BROWSERBASE_API_KEY / BROWSERBASE_PROJECT_ID |
lambdatest | LambdaTest / TestMu AI cloud grid | builtin (auto) | LT_USERNAME / LT_ACCESS_KEY |
browserstack | BrowserStack Automate cloud grid | builtin (auto) | BROWSERSTACK_USERNAME / BROWSERSTACK_ACCESS_KEY |
Default model is auto, resolved in this order:
ollama/ — free, open source, no keysANTHROPIC_API_KEY set → claude-opus-4-8OPENAI_API_KEY set → openai/gpt-4.1| Backend | Model flag | Needs |
|---|---|---|
| Ollama — local, free, OSS (preferred) | auto or ollama/ e.g. ollama/qwen3 | Ollama running; OLLAMA_BASE_URL to override http://localhost:11434/v1, OLLAMA_MODEL to pin auto-detection. Same flag works for any OpenAI-compatible server (vLLM, LM Studio, llama.cpp). |
| Anthropic | claude-opus-4-8 | ANTHROPIC_API_KEY |
| OpenAI / Google | openai/gpt-4.1, google/gemini-2.5-flash | provider key (Stagehand engine) |
| OpenRouter — hundreds of models, one key | openrouter/ / e.g. openrouter/anthropic/claude-sonnet-4-6, openrouter/meta-llama/llama-3.3-70b-instruct | OPENROUTER_API_KEY (https://openrouter.ai/keys); override endpoint with OPENROUTER_BASE_URL |
| Anthropic-compatible gateway | claude-* + ANTHROPIC_BASE_URL | builtin engine routes through any Anthropic-compatible endpoint (e.g. a LiteLLM proxy fronting local models) |
ollama pull qwen3 # or any tool-capable local model
browserbash run "Open https://example.com and store the heading as 'h1'"
Stagehand engine (MIT) + local Chromium + Ollama (MIT) — zero cloud cost, no API keys. Tip: small models (≤8B) are flaky on multi-step objectives; Qwen3 / Llama 3.3 70B class works best.
Note: cloud-grid providers (lambdatest, browserstack) use the builtin engine, which speaks the Anthropic API — pair them with ANTHROPIC_API_KEY or an ANTHROPIC_BASE_URL gateway.
npm install
npm run build npm link # exposes the browserbash command
Requires Node ≥ 18 and Google Chrome stable (for the local provider).
export ANTHROPIC_API_KEY=sk-ant-...
browserbash run "Open https://news.ycombinator.com and store the top story title as 'top_story'"
export BROWSERBASE_API_KEY=... BROWSERBASE_PROJECT_ID=... browserbash run "..." --provider browserbase
export LT_USERNAME=... LT_ACCESS_KEY=... browserbash run "..." --provider lambdatest --headless
browserbash run "..." --cdp-endpoint ws://localhost:9222/devtools/browser/
browserbash run "..." --engine builtin
--agent switches stdout to NDJSON — one JSON object per line, stable schema:
browserbash run "<objective>" --agent --headless --timeout 120
{"type":"step","step":1,"status":"passed","action":"navigate","remark":"...","cached":false}{"type":"run_end","status":"passed|failed|error|timeout","summary":"...","final_state":{...},"duration_ms":...,"provider":"local","cache":"hit|miss|off","tokens_in":...,"tokens_out":...,"test_url":"..."}Exit codes: 0 passed · 1 failed · 2 error · 3 timeout. cached, cache, tokens_in/tokens_out are additive fields (present when relevant), so existing consumers are unaffected.
Full agent integration guide: docs/agents.md.
BrowserBash is a validation layer for AI agents: your coding agent builds a feature, BrowserBash proves it works in a real browser. One line plugs it into any MCP host:
claude mcp add browserbash -- browserbash mcp # Claude Code
Tools exposed: run_objective (one plain-English objective), run_test_file (a *_test.md), run_suite (a folder, parallel). Each returns the structured verdict JSON: status, summary, final_state, assertions, cost_usd, duration_ms. A failed test is a successful validation, so the tool call succeeds and the agent reads the verdict. No extra dependencies, stdio only, nothing leaves your machine.
Every run is kept in a private on-disk store (~/.browserbash/runs, secrets masked, capped at 200). Two ways to see them:
Local dashboard — free, no account, fully local:
browserbash dashboard # serve http://localhost:4477 and open it
browserbash run "..." --record --dashboard # run, then open the dashboard on this run browserbash dashboard --clear # wipe the local store
Left panel lists your runs; the main pane shows the verdict, extracted values and the recording — with --record you get a screenshot plus a session video (stagehand engine, video needs ffmpeg, bundled) or a native Playwright trace you can open at trace.playwright.dev (builtin engine). Nothing leaves your machine.
Cloud dashboard — optional, opt-in per run: a hosted dashboard at browserbash.com/dashboard with run history across machines and shareable per-run pages.
browserbash connect --key bb_... # one-time, key from browserbash.com/dashboard
browserbash run "..." --record --upload # push THIS run (verdict + recording) to the cloud
Without --upload nothing is sent to the cloud. BrowserBash is free and open source; cloud runs are kept 15 days.
A green run records the actions it took. The next identical run replays them with zero model calls, and the agent only steps back in when the page actually changed. Steady-state suites run at close to script speed and cost.
browserbash testmd run ./checkout_test.md # run 1: records the journal
browserbash testmd run ./checkout_test.md # run 2: replays, no model browserbash testmd run ./checkout_test.md --no-cache # ignore the cache for this run browserbash testmd run ./checkout_test.md --refresh-cache # wipe this test's entry, re-record
run_end.cache reports hit / miss / off. On by default; config set cache.enabled false to disable, cache.dir to relocate (default .browserbash/cache, gitignored by init). Secrets never enter the cache: values arrive through the variables channel (Stagehand) or are re-templatized to {{name}} tokens (builtin), and any cached action that types a secret is origin-pinned — replaying it on a different origin fails closed. Builtin journals are also HMAC-signed with a per-machine key (~/.browserbash/cache.key); an edited or foreign journal is ignored and simply re-recorded. CI fleets that want to share committed caches can set the same BROWSERBASH_CACHE_KEY (64 hex chars) on every runner.
run-all)Run a whole folder of *_test.md files at once with memory-aware scheduling:
browserbash run-all .browserbash/tests --concurrency 8 --junit out/junit.xml
min(requested, cpus, floor((mem - 2GB) / budget))), so big suites do not thrash the machine. Override with --concurrency, tune the estimate with --memory-budget . A hard watchdog also kills any test whose whole process tree (Node + Chromium) exceeds --memory-cap (default 2x the budget, 0 disables); the test is reported as an infra error with a test_kill event, and retried per --retries.Result.md; a failure never leaks state to the next test.--retries retries infra errors only (not real failures), --max-failures stops early, --stagger softens burst load.--events, add --agent to also stream on stdout), JUnit XML (--junit), and a RunAll-Result.md with a flaky column..browserbash/memory/history.json orders the next run (previously-failed first, then slowest first) and flags flaky tests. --no-memory opts out.--shard 2/4 runs a deterministic slice, computed on sorted discovery order so parallel CI machines agree without coordination.--matrix-viewport 1280x720,390x844 runs every test once per viewport; cells are labeled in events, JUnit and results. Single runs take --viewport WxH too.--budget-usd 2.50 (or --budget-tokens) stops launching new tests once estimated spend crosses the budget; the rest are reported skipped and the suite exits 2. Spend lands in RunAll-Result.md and JUnit .--notify POSTs the suite verdict when it ends (Slack URLs get Slack formatting).0 all passed · 1 any failed · 2 infra error or budget stop · 3 suite timeout.Plan on a strong model, execute on a cheap one, escalate back automatically after a failed step:
browserbash run "..." --model claude-opus-4-8 --model-exec claude-haiku-4-5
run_end reports tokens_in / tokens_out (builtin engine) so you can see what a run costs, plus a cost_usd estimate from a bundled per-model price table (override at ~/.browserbash/pricing.json; unknown models get no estimate rather than a wrong one). Set persistently with config set routing.executionModel .
*_test.md)Committable, reviewable Markdown tests:
# Login flow
앞부분만 보여 드려요. 나머지는 저장소에서 읽어 보세요