Browserbash

Name: Browserbash
Author: PramodDutta

by PramodDuttaversion 1.5.1i★ –iRuns on your computer

Registry nameio.github.PramodDutta/browserbash

Install

Overview

Description from the maker (original English)

Plain-English browser tests for AI agents: real Chrome, deterministic verdicts. Free, no API keys.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Web & browsers
You need to fill in: ANTHROPIC_API_KEY

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
You fill in yourself: ANTHROPIC_API_KEY

Values marked secret are keys to your account. Never share them or paste them into a chat.

You need

npmNode.js installed (for the npx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "browserbash": {
      "command": "npx",
      "args": [
        "-y",
        "browserbash-cli"
      ],
      "env": {
        "ANTHROPIC_API_KEY": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add browserbash --env ANTHROPIC_API_KEY=your-value -- npx -y browserbash-cli

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "browserbash": {
      "command": "npx",
      "args": [
        "-y",
        "browserbash-cli"
      ],
      "env": {
        "ANTHROPIC_API_KEY": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "browserbash": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "browserbash-cli"
      ],
      "env": {
        "ANTHROPIC_API_KEY": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

ANTHROPIC_API_KEY secret env
Anthropic API key. Optional: without it BrowserBash auto-resolves a free local Ollama model.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account PramodDutta, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Web & browsersi

More stars than 0% of the other 875 servers in this category
Published to the registry more recently than 21% of the other 875 servers in this category
Basic checks equal to or better than 100% of the other 875 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Browserbash safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

You need to fill in: ANTHROPIC_API_KEY

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (1.5.1) reached the registry 4 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.

What can I use instead?

In the same category: Browser Use, Chrome DevTools MCP, MCP Server Browser

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

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Lets AI do web tasks for you, such as filling forms, searching and extracting data, in the maker’s cloud browsers or on your computer.

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by ChromeDevTools

Lets AI use Chrome’s developer tools to inspect pages, find what makes them slow and spot errors. Made by the Chrome DevTools team.

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MCP Server Browser

by bytedance

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Runs on your computer★ 39,550Apache-2.0Updated 14 months ago

Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

BrowserBash

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.

Engines (who interprets the English)

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

Providers (where the browser runs)

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`

LLM backends (who does the thinking) — open source first

Default model is auto, resolved in this order:

Ollama running locally → ollama/ — free, open source, no keys
ANTHROPIC_API_KEY set → claude-opus-4-8
OPENAI_API_KEY set → openai/gpt-4.1
otherwise: error with setup guidance

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)

Fully free / open-source stack (the default)

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.

Install

npm install

npm run build npm link # exposes the browserbash command

Requires Node ≥ 18 and Google Chrome stable (for the local provider).

Quick start

export ANTHROPIC_API_KEY=sk-ant-...

One-shot objective, local browser, Stagehand engine (default)

browserbash run "Open https://news.ycombinator.com and store the top story title as 'top_story'"

Browserbase cloud (Stagehand native)

export BROWSERBASE_API_KEY=... BROWSERBASE_PROJECT_ID=... browserbash run "..." --provider browserbase

Cloud grid (auto-switches to builtin engine)

export LT_USERNAME=... LT_ACCESS_KEY=... browserbash run "..." --provider lambdatest --headless

Attach to an existing browser (CDP / Playwright MCP)

browserbash run "..." --cdp-endpoint ws://localhost:9222/devtools/browser/

Force the builtin engine

browserbash run "..." --engine builtin

Agent mode (for AI coding tools & CI)

--agent switches stdout to NDJSON — one JSON object per line, stable schema:

browserbash run "<objective>" --agent --headless --timeout 120

Progress events: {"type":"step","step":1,"status":"passed","action":"navigate","remark":"...","cached":false}
Terminal event: {"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.

MCP server (agents consume BrowserBash natively)

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

Cursor / Windsurf / Zed / Codex: command "browserbash", args ["mcp"]

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.

Dashboards

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.

Replay cache (warm runs skip the model)

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.

Parallel suites (`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

Concurrency is auto-derived from CPU and free memory (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.
Each test runs as an isolated child process with its own 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.
Outputs: a merged NDJSON stream (--events, add --agent to also stream on stdout), JUnit XML (--junit), and a RunAll-Result.md with a flaky column.
Run history in .browserbash/memory/history.json orders the next run (previously-failed first, then slowest first) and flags flaky tests. --no-memory opts out.
Sharding: --shard 2/4 runs a deterministic slice, computed on sorted discovery order so parallel CI machines agree without coordination.
Viewport matrix: --matrix-viewport 1280x720,390x844 runs every test once per viewport; cells are labeled in events, JUnit and results. Single runs take --viewport WxH too.
Budgets: --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 .
Webhooks: --notify POSTs the suite verdict when it ends (Slack URLs get Slack formatting).
Exit code: 0 all passed · 1 any failed · 2 infra error or budget stop · 3 suite timeout.

Cheap-model routing

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 files (`*_test.md`)

Committable, reviewable Markdown tests:

# Login flow

Open {{base_url}}/l

Only the beginning is shown; read the rest in the repo

Browserbash

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

Values you fill in

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Web & browsersi

Common questions

Alternatives

Browser Use

Chrome DevTools MCP

MCP Server Browser

README from the maker

BrowserBash

Engines (who interprets the English)

Providers (where the browser runs)

LLM backends (who does the thinking) — open source first

Fully free / open-source stack (the default)

Install

Quick start

One-shot objective, local browser, Stagehand engine (default)

Browserbase cloud (Stagehand native)

Cloud grid (auto-switches to builtin engine)

Attach to an existing browser (CDP / Playwright MCP)

Force the builtin engine

Agent mode (for AI coding tools & CI)

MCP server (agents consume BrowserBash natively)

Cursor / Windsurf / Zed / Codex: command "browserbash", args ["mcp"]

Dashboards

Replay cache (warm runs skip the model)

Parallel suites (run-all)

Cheap-model routing

Test files (*_test.md)

Parallel suites (`run-all`)

Test files (`*_test.md`)