This demo requires browser-use v0.7.7+.

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Features

  1. Agent visits any news website automatically
  2. Finds and clicks the most recent headline article
  3. Extracts title, URL, posting time, and full content
  4. Generates short/long summaries with sentiment analysis
  5. Persistent deduplication across monitoring sessions

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Setup

Make sure the newest version of browser-use is installed: 
pip install -U browser-use
Export your Gemini API key, get it from: Google AI Studio 
export GOOGLE_API_KEY='your-google-api-key-here'
Clone the repo, cd to the app 
git clone https://github.com/browser-use/browser-use.git
cd browser-use/examples/apps/news-use

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Usage Examples

# One-time extraction - Get the latest article and exit
python news_monitor.py --once

# Monitor Bloomberg continuously (default)
python news_monitor.py

# Monitor TechCrunch every 60 seconds
python news_monitor.py --url https://techcrunch.com --interval 60

# Debug mode - See browser in action
python news_monitor.py --once --debug

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Output Format

Articles are displayed with timestamp, sentiment emoji, and summary: 
[2025-09-11 02:49:21] - 🟢 - Klarna's IPO raises $1.4B, benefiting existing investors
[2025-09-11 02:54:15] - 🔴 - Tech layoffs continue as major firms cut workforce
[2025-09-11 02:59:33] - 🟡 - Federal Reserve maintains interest rates unchanged
Sentiment Indicators:
  • 🟢 Positive - Good news, growth, success stories
  • 🟡 Neutral - Factual reporting, announcements, updates
  • 🔴 Negative - Challenges, losses, negative events

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Data Persistence

All extracted articles are saved to news_data.json with complete metadata: 
{
  "hash": "a1b2c3d4...",
  "pulled_at": "2025-09-11T02:49:21Z",
  "data": {
    "title": "Klarna's IPO pops, raising $1.4B",
    "url": "https://techcrunch.com/2025/09/11/klarna-ipo/",
    "posting_time": "12:11 PM PDT · September 10, 2025",
    "short_summary": "Klarna's IPO raises $1.4B, benefiting existing investors like Sequoia.",
    "long_summary": "Fintech Klarna successfully IPO'd on the NYSE...",
    "sentiment": "positive"
  }
}

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Programmatic Usage

import asyncio
from news_monitor import extract_latest_article

async def main():
    # Extract latest article from any news site
    result = await extract_latest_article(
        site_url="https://techcrunch.com",
        debug=False
    )
    
    if result["status"] == "success":
        article = result["data"]
        print(f"📰 {article['title']}")
        print(f"😊 Sentiment: {article['sentiment']}")
        print(f"📝 Summary: {article['short_summary']}")

asyncio.run(main())

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Advanced Configuration

# Custom monitoring with filters
async def monitor_with_filters():
    while True:
        result = await extract_latest_article("https://bloomberg.com")
        if result["status"] == "success":
            article = result["data"]
            # Only alert on negative market news
            if article["sentiment"] == "negative" and "market" in article["title"].lower():
                send_alert(article)
        await asyncio.sleep(300)  # Check every 5 minutes

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Source Code

Full implementation: https://github.com/browser-use/browser-use/tree/main/examples/apps/news-use