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browser-use/docs/examples/apps/news-use.mdx
2025-09-11 11:41:33 -07:00

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---
title: "News-Use (News Monitor)"
description: "Monitor news websites and extract articles with sentiment analysis using browser agents and Google Gemini."
icon: "newspaper"
mode: "wide"
---
<Note>
This demo requires browser-use v0.7.7+.
</Note>
<video
controls
className="w-full aspect-video rounded-xl"
src="https://browser-use.github.io/media/demos/news_use.mp4">
</video>
## 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
## Setup
Make sure the newest version of browser-use is installed:
```bash
pip install -U browser-use
```
Export your Gemini API key, get it from: [Google AI Studio](https://makersuite.google.com/app/apikey)
```bash
export GOOGLE_API_KEY='your-google-api-key-here'
```
Clone the repo, cd to the app
```bash
git clone https://github.com/browser-use/browser-use.git
cd browser-use/examples/apps/news-use
```
## Usage Examples
```bash
# 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
```
## 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
## Data Persistence
All extracted articles are saved to `news_data.json` with complete metadata:
```json
{
"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"
}
}
```
## Programmatic Usage
```python
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())
```
## Advanced Configuration
```python
# 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
```
## Source Code
Full implementation: [https://github.com/browser-use/browser-use/tree/main/examples/apps/news-use](https://github.com/browser-use/browser-use/tree/main/examples/apps/news-use)