Browser Automation
Browser-Use, Skyvern, Stagehand, and the Chrome extensions agents actually drive. Pick one and ship a scraper today.
What's in this pack
This pack collects the seven browser automation assets that come up over and over again when serious agent setups are reverse-engineered from public configs and Twitter screenshots. Three are the headline open-source frameworks. Three are scraping templates that wrap them. One is the operational MCP fix that keeps Chrome from breaking when multiple agents share the same browser.
| # | Asset | Type | What it does |
|---|---|---|---|
| 1 | Browser-Use | Python framework | Agent-driven Playwright wrapper, LLM-native API |
| 2 | Skyvern | Python service | Vision + LLM browser automation, runs as a server |
| 3 | Stagehand | TypeScript framework | Production-grade Browserbase SDK |
| 4 | Chrome MCP popup fix | MCP server | Stops modal/cookie popups from blocking agents |
| 5 | Headless scraper template | scraping recipe | Skyvern + cron starter |
| 6 | Login-walled scraper | scraping recipe | OAuth + session reuse pattern |
| 7 | Web reader for agents | MCP server | Markdown-friendly fetch for Claude Code / Cursor |
Why this pack exists
Most "AI scraper" tutorials stop at "open this page and read the text." Real browser automation has to deal with login walls, cookie banners, modal popups, infinite scroll, and the fact that multiple agents trying to drive the same Chrome session will fight each other. This pack picks the seven assets that solve those problems specifically — not the 200 starred-but-abandoned alternatives.
The three headline frameworks each have a niche:
- Browser-Use is the easiest entry point. Python-native, ships with a Playwright wrapper that an LLM can talk to in plain English. Best for one-off automations and prototypes.
- Skyvern runs as a server and uses vision + LLM together. Best when the page changes structure between visits and selectors break. It's slower than Browser-Use but more resilient.
- Stagehand is the production option. TypeScript-first, made by the Browserbase team, designed to run thousands of agent-driven sessions. If you're shipping a customer-facing product, this is the one.
The Chrome MCP popup fix is included because every multi-agent Chrome setup eventually hits the bug where one agent's modal blocks another agent's click. The included MCP server intercepts the most common popup patterns and clears them automatically.
Install in one command
# Install the entire pack
tokrepo install pack/browser-automation
# Or pick the framework you actually need
tokrepo install browser-use
tokrepo install skyvern
tokrepo install stagehand
The TokRepo CLI installs each asset into the correct location for your AI tool — Claude Code subagents into .claude/agents/, Cursor rules into .cursor/rules/, AGENTS.md entries for Codex CLI, and so on. Pull whichever subset matches your stack.
Watch out for
- Don't run Browser-Use and Stagehand on the same Chrome profile simultaneously. They'll race for the active tab. Use separate user-data-dirs or a remote Browserbase session.
- Skyvern needs a GPU or a cheap vision model fallback. The default config calls GPT-4o for every screen — that adds up fast on long-running scrapes.
- Login-walled scraping breaks reCAPTCHA Terms of Service. Stick to your own dashboards or sites that explicitly allow automation. The pack ships with a "session export from real browser" pattern so you don't need to log in via the agent.
- Cookie banners are not all GDPR. Many sites use them as anti-bot tarpits. The popup-fix MCP solves the common ones; the rest you'll have to add manually.
- Headless detection is real. Sites like Cloudflare and DataDome will fingerprint you. If you need to bypass, use Stagehand with Browserbase — they handle the stealth layer.
When this pack alone isn't enough
If your goal is large-scale crawling of a single high-value site (job boards, real estate listings, e-commerce), a dedicated scraper like Apify or a custom Playwright cluster will outperform agent-driven automation by 5-10x in cost. Browser automation agents are best when:
- You don't know in advance which fields you need
- The page layout varies between visits
- You want to glue scraping into a larger agent task (research, lead gen, QA)
For pure throughput, drop the LLM and write the selectors yourself. For flexibility and the ability to recover from layout changes, this pack wins.
7 assets in this pack
Frequently asked questions
Is Browser-Use free?
Yes. Browser-Use is MIT-licensed and you only pay for the LLM tokens it consumes (any Claude / GPT-4 / open-weights model works). The library itself runs anywhere Python and Playwright run. Skyvern and Stagehand are similarly OSS — Stagehand has a paid hosted option via Browserbase if you want managed Chrome.
Will this work in Codex CLI or Cursor instead of Claude Code?
All seven assets are tool-agnostic — they're Python/TypeScript libraries, not Claude Code-specific subagents. The TokRepo CLI installs the right wiring for your tool of choice (subagents for Claude Code, rules for Cursor, AGENTS.md for Codex CLI). Browser-Use even has a built-in OpenAI/Anthropic switch so you can pair it with whichever model your CLI is talking to.
How does Skyvern compare to Browser-Use?
Skyvern uses vision (a screenshot + LLM) to find elements, so it survives layout changes that break selector-based scrapers. Browser-Use is selector-driven via Playwright, which is faster and cheaper but more brittle. Rule of thumb: prototype with Browser-Use, ship with Skyvern or Stagehand depending on whether you need vision (Skyvern) or scale (Stagehand).
What's the difference vs the MCP Server Stack pack?
MCP Server Stack focuses on protocol-level connectors — browser, database, filesystem MCP servers — that any agent can plug into. Browser Automation is opinionated about which browser tools to use and how to scrape with them. The Chrome MCP popup fix appears in both packs because it's load-bearing for any agent that touches a real browser.
When should I NOT use agent-driven browser automation?
When throughput matters more than flexibility. If you're scraping the same 100 product pages every hour and the layout never changes, write Playwright selectors directly — you'll cut your cost by 10x and remove a flaky LLM dependency. Use the agent layer when the task is exploratory or the page is unknown.
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