BrowserAct Skills: Real Browser Automation for AI Agents
Break through anti-bot walls, run parallel isolated sessions, and hand off to humans when stuck — a browser built for AI agents.
Evidence shows: README claims sensitive operations (e.g., browser create/delete, Profile import, proxy changes, security and privacy toggles) require explicit user approval, and approvals do not carry over, enforced at the Skill layer. This supports user_confirmation score 2. However, implementation details are missing, so enforcement cannot be verified. least_privilege scores 1: README mentions minimal permissions but no specific mechanism. data_flow_transparency scores 1: README mentions data flow but not detailed. sensitive_data_handling scores 1: mentions sensitive operations but not how sensitive data is handled. dependency_security scores 1: requirements.txt only lists requests and python-dotenv, no version pinning or security audit. external_effects scores 1: README mentions external effects (e.g., proxies, CAPTCHA) but not detailed. rollback scores 0: no rollback mechanism mentioned. source_attribution scores 1: README mentions BrowserAct team but unverified.
self_consistency scores 2: README is internally consistent, command examples match descriptions. dependency_availability scores 1: dependencies are simple but not version-pinned, affecting reproducibility. failure_messages scores 1: README mentions error handling but no specific error message examples.
audience_and_scenarios scores 2: README clearly identifies target users (AI agents) and scenarios (anti-bot, multi-task, multi-account). capability_boundaries scores 2: README distinguishes free vs paid features and different modes. trigger_precision scores 2: README provides specific commands and parameters. environment_fit scores 2: README lists compatible OS and agents.
information_architecture scores 2: README is well-structured with table of contents. install_notes scores 2: README provides installation instructions. naming_stability scores 1: command names are stable but no version history. examples_and_faq scores 2: README provides multiple examples. known_limitations scores 1: README mentions some limitations but not comprehensive. license scores 2: MIT license file present. versioning_changelog scores 0: no version numbers or changelog. maintenance_responsibility scores 1: README mentions community support but not explicit maintenance responsibility.
output_usability scores 2: README claims compact text output suitable for agents. marginal_value scores 2: provides unique features (anti-bot, remote assist). cost_benefit scores 2: free features are rich, paid features are clear.
claim_traceability scores 1: claims in README lack evidence. cross_source_corroboration scores 1: only README, no other sources. fact_inference_separation scores 1: README mixes facts and inferences without clear distinction.
- Security claims (e.g., user confirmation) in README are not verified in code; may not be enforced.
- Dependencies are not version-pinned, posing supply chain risks.
- No version numbers or changelog, making updates hard to track.
- Publisher identity is unverified; proceed with caution.
What does this agent do, and when should you use it?
BrowserAct Skills is a browser automation CLI designed for AI agents to perform real-browser automation, web data extraction, and account-based workflows. It features three progressive anti-blocking layers (environment, execution, human), with CAPTCHA solving, remote human takeover, and multi-session parallelism. It offers both cloud-managed and local modes, with compatibility across multiple agents (Claude Code, Cursor, Codex, Gemini CLI). Its output is compact, token-efficient, and index-based, optimized for LLM reasoning. The project is MIT-licensed, with most features free; only managed proxies and stealth browsers beyond the first 5 require payment.
BrowserAct Skills provides a CLI named browser-act for browser automation. Core commands include: stealth-extract to extract protected content; browser-act --session <id> browser open to launch a browser; state to list indexed interactive elements; click <index> to click by index; input <index> "text" to type; solve-captcha to auto-solve CAPTCHAs; remote-assist to generate a URL for human takeover. Agents initialize via browser-act get-skills core --skill-version 2.0.2 to fetch environment state and commands. It supports chrome mode (reuse local login state) and stealth mode (privacy or fixed identity). It enables cross-browser parallel, same-browser multi-session, and privacy mode for zero-interference concurrency. All operations return compact text with indexed interaction for token efficiency.
- A data extraction specialist needing to scrape anti-bot-protected sites like Amazon or LinkedIn for product details or job listings.
- A marketer managing multiple social media accounts, using isolated browsers to prevent account association.
- An AI developer integrating browser control into Claude Code or Cursor workflows, letting the agent navigate and interact with real websites.
- A user facing a CAPTCHA or complex step that the agent cannot solve; the agent generates a takeover link for human assistance.
- A business performing large-scale data collection, using Skill Forge to create reusable skills for repeated scraping of a specific website.
What are this agent's strengths and limitations?
- Multi-layer anti-blocking (fingerprint, TLS rotation, proxies) bypasses most anti-bot walls.
- Independent multi-session and cross-browser parallelism prevent account association.
- Optimized for LLMs: compact text output, indexed interaction, and semantic memory save tokens.
- Security confirmation gating for sensitive operations, approved per action.
- Seamless integration with multiple AI agents (Claude Code, Cursor, Codex, Gemini CLI).
- Advanced features (managed proxies, more than 5 stealth browsers) require payment and login.
- Remote operations depend on external cloud services; local mode requires network for some features.
- Free stealth browser limit of 5 may be restrictive for large-scale operations.
- Full documentation may not cover all edge cases, and some features like 'Skill Forge' are less documented.
- Reusing local Chrome login state requires CDP or profile import, which may have compatibility issues.
How do you install or deploy this agent?
- Ensure Node.js is installed (version not specified).
- Instruct your AI agent (e.g., Claude Code) to install the skill:
> Install browser-act. Skill source: https://github.com/browser-act/skills/tree/main/browser-act . Verify it works after installation.
The agent will run the necessary commands to download and set up the CLI.
- No signup is required for basic features, but some features (like stealth browsers) require a login.
How do you use this agent?
After installation, in your AI agent session, run:
# Extract protected page content (zero config)
browser-act stealth-extract https://example.com
# Open a browser and navigate
browser-act --session my-task browser open <id> https://example.com
# View interactive elements
browser-act --session my-task state
# Click element by index (e.g., the 3rd)
browser-act --session my-task click 3
# Type into a field
browser-act --session my-task input 2 "hi"At start of each session, run browser-act get-skills core to fetch environment state and commands.
How does this agent compare with similar options?
Alternative tools include Playwright or Puppeteer for browser automation, but they lack built-in anti-bot and human takeover features. Other scraping APIs like ScrapingBee or ScraperAPI offer proxy solutions, but may not integrate natively with AI agents.
FAQ
What does it cost to use BrowserAct?
How do I handle CAPTCHAs?
solve-captcha command to auto-solve. If that fails, use remote-assist to generate a link for a human to take over and complete the CAPTCHA.Can I use my local browser with existing login sessions?
chrome mode to attach to a local Chrome instance via profile import or CDP, reusing login state.