Browser Use
Connect AI agents to the web: Browser Use lets LLMs browse, interact, and extract from any website autonomously.
Browser Use
Browser Use is the leading open-source library for giving AI agents full browser control. Agents can navigate to URLs, click elements, fill forms, extract text, take screenshots, and handle multi-tab workflows — all driven by natural language tasks. It sits on top of Playwright and wraps browser state into a compact LLM-friendly representation.
Key Features
- Natural language tasks — describe a web task in plain English; the agent figures out the clicks
- Playwright-backed — full Chromium control with support for authentication, cookies, and JS-rendered pages
- Smart DOM extraction — converts complex page DOM into a compact, token-efficient representation for the LLM
- Multi-tab management — agents can open, switch, and close tabs as needed
- Vision + DOM — supports both screenshot-based and DOM-based navigation strategies
- Multi-agent — multiple Browser Use agents can collaborate in parallel
- Provider-agnostic — works with OpenAI, Anthropic, Gemini, and any LangChain-compatible model
Quick Start
pip install browser-use
playwright install chromium
import asyncio
from langchain_openai import ChatOpenAI
from browser_use import Agent
async def main():
agent = Agent(
task="Go to Hacker News and find today's top story title.",
llm=ChatOpenAI(model="gpt-4o"),
)
result = await agent.run()
print(result)
asyncio.run(main())
Install via ai-supply
npx ai-supply add browser-use-web-agent
Curated mirror of the open-source Browser Use project (MIT). Install upstream from the repository.
Compromise signals — malicious or tampered code (leaked secrets, backdoors, a dropped executable) — reduce the score, and known dependency CVEs carry a bounded penalty (they warrant review but never QUARANTINE — update the dependency to clear). Other dangerous-by-capability traits are risk surface, expected for some capabilities. Every finding is mapped to its OWASP control below.
Findings mapped to the OWASP Top 10 for LLM Applications (2025) and the OWASP Machine Learning Security Top 10. Expand any flagged control for the exact findings — compromise reduces the score; expected/risk-surface do not, except a known CVE, which carries a small bounded penalty (high/critical → Review).
The same gate an agent runs before installing (POST /api/v1/trust/browser-use-web-agent/check). Click a policy:
Consume Browser Use programmatically. Authenticate with an API key or session — see Authorize an agent.
# Agents: CHECK BEFORE YOU INSTALL (no auth) — score, grade, level, capability manifest
curl https://ai-supply.store/api/v1/trust/browser-use-web-agent
# Gate against your org policy (returns { pass, violations })
curl -X POST https://ai-supply.store/api/v1/trust/browser-use-web-agent/check \
-H "Content-Type: application/json" \
-d '{"minGrade":"B","denyPermissions":["shell"],"denyUnknownEgress":true}'
# CLI
npx ai-supply add browser-use-web-agent
# REST (install → download)
curl -X POST https://ai-supply.store/api/v1/listings/browser-use-web-agent/install \
-H "Authorization: Bearer $AIM_KEY"
# MCP tool
install_listing({ "slug": "browser-use-web-agent" })OpenAPI spec →Curated mirror — latest upstream source. See the repository for tagged releases.