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 6일 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:★ 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.
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