Agentic Browser is an agent-first Python browser built on Playwright/Chromium so LLMs can drive the web with compact observations, stable element refs, and outcome-verified actions ? not raw HTML dumps.
Why it exists
Traditional scrapers hand models 100k+ tokens of markup. Agents need:
- Small structured observations (roles, labels, refs)
- Actions that mean success (URL/DOM outcomes)
- A plug-in for any host (MCP + OpenAI/Anthropic tool schemas)
Measured token efficiency
| Scenario | Raw HTML | Compact observation | Reduction |
|---|---|---|---|
| Quotes scrape | ~2.8k?6.2k | ~0.45k?1.3k | ~78?84% |
| Rockstar GTA VI landing | ~225,000 | ~1,300 | ~99.4% |
GitHub vercel/next.js
|
~110,000 | ~1,900 | ~98.3% |
Features
- Stable refs + scoped grounding
- Outcome verification (e.g. Issues click only OK if URL is
/issues) - Page gates for challenges (detect & report ? not a bypass tool)
- MCP server for Cursor / Claude Desktop
-
tools_as_openai()/tools_as_anthropic() - 118 automated tests; milestones M1?M10
Install
pip install agent-browser
playwright install chromium
agent-browser --help
# MCP
python -m agent_browser.mcp
Links
- GitHub: https://github.com/applejuice093/Agentic-browser
- Release: https://github.com/applejuice093/Agentic-browser/releases/tag/v0.4.0
MIT ? Python 3.11+
Top comments (1)
Compact observations plus outcome-verified actions are the right pair. Token reduction alone can hide an information-loss problem, so I would love to see the benchmark extended from observation size to task completion: total tokens per successful task, retries caused by stale refs, and missed targets on virtualized lists, iframes, Shadow DOM, canvas-heavy pages, and delayed hydration. A compact tree that saves 98% but needs three recovery turns may be more expensive than it looks. The explicit page-gate behavior is also a strong design choice: reporting uncertainty is much safer than pretending an action succeeded.