BrowserAct: AI Agent Browser Automation CLI

What BrowserAct Offers for AI Agents

BrowserAct is a CLI that gives AI agents real, controllable Chrome instances with anti-bot defenses built in. It exposes a command set for opening pages, clicking by index, typing into fields, and extracting content — designed for LLM output efficiency rather than human readability. Browser modes include local Chrome profile reuse, fresh-fingerprint stealth for batch scraping, and fixed-identity stealth for logged-in multi-account work. The Skills system lets agents discover available commands and browser state in a single call.

Core Capabilities for Web Interaction

• ✓Three browser modes: local Chrome profile reuse, fresh-fingerprint stealth for frictionless batch scraping, and fixed-identity stealth with stable IP for logged-in multi-account sessions
• ✓Three-layer anti-bot stack: environment spoofing (fingerprint + TLS rotation + proxy), execution-layer CAPTCHA solving and stealth page extraction, and remote human handoff as a last resort
• ✓Zero-interference concurrency: independent cookies, fingerprints, and proxies across parallel sessions so sites cannot correlate them
• ✓Indexed interaction model: `state` returns an indexed element list; agents click by number (`click 3`) or type by index (`input 2 "text"`) — no DOM parsing required
• ✓Compact text output: indexed plain text format instead of raw HTML or verbose JSON, reducing token cost per page
• ✓Skills system for agent discovery: `get-skills core` returns environment state, available browsers, and full command reference in one call
• ✓Security confirmation gating: sensitive operations (browser create/delete, profile import, proxy changes) require explicit approval each time — enforced at the Skill layer, not a config toggle
• ✓Skill Forge and 30+ pre-built solutions: auto-generates reusable scraping Skills by exploring a site once, then reuses the stable path for high-volume extraction

Getting Started with BrowserAct Skills

Tell your AI agent to install BrowserAct using its Skill source: `https://github.com/browser-act/skills/tree/main/browser-act`. The agent runs `get-skills core --skill-version 2.0.2` to discover environment state and available commands. Local installation requires Chrome (or Chromium) on Windows, macOS, or Linux; the cloud-managed mode requires no local setup at all. After installation, verify with a simple command like `browser-act --version` or a basic `browser-act stealth-extract` call on a known URL.

Automating Tasks with BrowserAct Commands

BrowserAct commands follow a session-based pattern: `browser-act --session <name> <command>`. Core commands include `browser open <id> <url>` to open a page, `state` to list clickable elements by index, `click <n>` to interact by number, `input <n> "text"` to type into fields, and `stealth-extract <url>` for single-page protected content without a full session. For parallel tasks, run multiple sessions with different names — each gets independent cookies, fingerprints, and proxies. The `remote-assist` command generates a live URL for human takeover when the agent is stuck. Skill Forge users describe the data they need and the agent generates a reusable Skill package that the agent can run on demand.

Other Tools for AI Agent Web Interaction

Frequently Asked Questions

The problem it solves

Standard browser automation tools were built for human operators, not for AI agents that need compact outputs, indexed interactions, and zero cross-contamination between parallel tasks. Most scrapers fail at sites using fingerprinting or CAPTCHA gates, and existing headless browsers get flagged as bots the moment they visit anything sophisticated. Agents also need a clean handoff path when they hit something they cannot resolve autonomously.

Best use cases

• •Web data extraction from sites with anti-bot protection, including authenticated pages requiring login
• •Parallel scraping or automation across multiple independent accounts without cross-contamination
• •AI agent workflows that need real-browser interaction instead of API calls — for sites without public APIs or with rate-limited endpoints
• •Running repeatable scraping jobs at scale via Skill Forge-generated Skills without writing custom scraper code
• •Human-agent handoff scenarios where the AI handles routine steps and escalates to a human for CAPTCHAs or account alerts
• •Batch data collection from e-commerce, social, or job platforms using pre-built or custom Skills

Who should try it — and who should skip

Try BrowserAct if you are building AI agent workflows that need to interact with the live web — scraping authenticated pages, automating account-bound tasks, or extracting data from sites that actively resist scraping. It is particularly useful when agents need to work in parallel without cross-contamination, or when you want a human handoff path for edge cases. Skip it if your scraping targets have clean public APIs, or if you are already satisfied with Playwright plus custom anti-detect code — BrowserAct adds the most value when anti-bot resistance and agent-native UX matter more than raw customization.

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