BrowserBash
Summary
AI browser automation tools that require a cloud account, a credit card, and an API key before you can run a single test are tools that fail before the first commit — BrowserBash exists to remove every one of those blockers.
BrowserBash is a CLI that takes a plain-English objective and drives a real Chrome browser to completion, returning NDJSON events on stdout and a process exit code your CI pipeline can act on without parsing prose. The default stack runs entirely on local models via Ollama — no API keys, no cloud, no account required to run. A free dashboard account adds run history, video recordings, and per-run replay. The architecture is three swappable layers — browser provider, interpretation engine, and LLM — so a team using local Chromium today can route to BrowserStack tomorrow with one flag. Where the tool strains is complex multi-step conditional logic: an objective that branches on what a previous step returned still lands on a single-loop AI agent with no visual workflow editor to inspect.
Bottom line: Pick this for zero-friction browser test automation in a CI pipeline where a plain-English objective and an exit code are enough — expect to work around it when your automation requires branching on intermediate state or you need a non-technical stakeholder to own the test logic in a GUI.
Pricing Plans
- Free Tier
- 15-day cloud run history and recordings
Free
Full CLI, local dashboard, 15-day cloud history
- Plain-English automation
- All browser providers
- Free local models
- Markdown tests
- Open source
Supporter
Extended cloud retention (coming soon)
- Extended data retention beyond 15 days
- Supports ongoing development
View full pricing on browserbash.com →
Pricing may have changed since last verified. Check the official site for current plans.
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Pros
Sign in to edit- Runs on free local models via Ollama with no API keys and no account required, so a developer on a locked-down corporate network can automate and test without touching a billing page.
- Exit codes 0/1/2/3 on stdout as NDJSON, which means CI pipelines get a machine-readable verdict without a fragile scraping layer on top of prose output.
- Markdown test files with @import composition are committable artifacts, so tests live in version control alongside code and can be reviewed, diffed, and rolled back like any other file.
- Three independently swappable layers — provider, engine, LLM — so a team running local Chromium for development can point the same objective at BrowserStack for grid runs with a single flag change, without rewriting the test.
- Secrets marked in the config are masked as asterisks in every log line and summary, which means test runs against staging environments with real credentials do not leak those credentials into CI logs.
Cons
Sign in to edit- The agent loop has no conditional branching construct: if your automation needs to take different actions depending on what appeared on a previous page, you are encoding that logic in shell scripts around the exit codes — at which point you are maintaining test orchestration infrastructure outside the tool.
- There is no API surface, so embedding BrowserBash into an application that needs to trigger browser tasks programmatically at runtime is not supported; the CLI is the only integration point, and teams needing in-process browser automation switch to Playwright or Puppeteer with their own LLM layer.
- The dashboard and run retention are cloud-hosted and account-gated; teams with strict data residency requirements who also want video replay and run history cannot self-host the full stack — the CLI is self-hostable but the vendor states the dashboard is not described as self-hostable on the page.
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About
- Platforms
- CLI (npm), local Chrome, any CDP endpoint
- API Available
- No
- Self-Hosted
- Yes
- Last Updated
- 2026-06-25T18:48:59.127Z
Best For
Who it's for
- Developers and testers needing code-free browser automation
- Teams using local or free AI models
- CI/CD workflows requiring exit codes and recordings
What it does well
- Automating browser tasks from plain English
- Generating and running browser tests in CI pipelines
- Recording and replaying browser sessions
- Composing complex automations with reusable Markdown tests
Integrations
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Frequently Asked Questions
- Is BrowserBash free?
- BrowserBash has a permanent free tier alongside paid upgrades. You can keep using a baseline version indefinitely without paying.
- Is BrowserBash open source?
- No — BrowserBash is a closed-source tool. Source code is not publicly available.
- Can I self-host BrowserBash?
- Yes. BrowserBash supports self-hosting on your own infrastructure.
- What platforms does BrowserBash support?
- BrowserBash is available on: CLI (npm), local Chrome, any CDP endpoint.
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BrowserBash is a plain-English browser automation CLI built around an AI agent loop: you write one sentence describing what you want a browser to do, and the agent drives a real Chrome instance start to finish, emitting structured NDJSON events and exiting with a numeric code — 0 for pass, non-zero for failure. The vendor states the tool is Apache-2.0 open source with the full agent loop in the repository. Installation is a single npm command and the CLI runs without an account; a free dashboard account is optional, adding run history, video recordings, and per-run replay.
The differentiating architectural choice is the three-layer stack: you independently choose the browser provider (local Chrome, a CDP endpoint, BrowserBase, LambdaTest, or BrowserStack), the interpretation engine (Stagehand by default, or a built-in Anthropic tool-use loop auto-selected for cloud grids), and the LLM (Ollama locally, or Anthropic, OpenAI, Google, or any OpenAI-compatible server via flags). The fully-free default is Stagehand plus local Chromium plus Ollama — the vendor states this costs nothing and contacts no external service. Bringing your own API key is an option, not a requirement.
For CI pipelines and developer-owned test suites, the fit is direct: tests are committable Markdown files with @import composition, secrets are masked in every log line, a GitHub Actions recipe is included, and the exit code is the test result. The breakpoint comes when the automation needs to branch based on what a prior step returned — the single-agent loop has no conditional branching construct and no visual canvas to model decision trees. Teams needing that capability either script around the exit codes in their CI runner or move to a workflow tool that exposes branching as a first-class concept. There is no API, so programmatic embedding into an existing application is not a supported path — the CLI is the integration surface.