Google News Scraper API: Real Article URLs & History

Get Google News articles by keyword, topic or city in seconds - with the real publisher URL of every article, not Google's redirect link.

No headless browser: the Actor reads Google News directly, so it is fast (100 articles with their real URLs in about a minute, 10 seconds without URL decoding), cheap and reliable. Searches that fail are listed separately and never charged.

What data can you get?

Data	Description
🔎 Search	Any keyword, with Google News operators: "exact phrase", OR, -exclude, site:reuters.com, intitle:
🔗 Real article URL	Google News links are redirects (news.google.com/rss/articles/...). The Actor decodes them and returns the publisher's URL in url
📅 History	Past hour, day, week, month, year - or any dates, years back (dateFrom / dateTo)
📈 Up to 1,000 articles per search	Google stops at 100 articles per feed; the Actor goes day by day to collect more
🗞️ Topics	Today's headlines: Top stories, World, Nation, Business, Technology, Entertainment, Sports, Science, Health
📍 Local news	Headlines for any city or region: Austin, Texas, Lyon, Bavaria...
🌍 60 editions	United States, United Kingdom, France, Germany, Spain, Brazil, India, Japan... each in its own language
🧩 Related coverage	For topics and local news: the other outlets covering the same story
♻️ No duplicates	An article found by several searches is returned (and charged) once

How to use it

1. Enter one or more search queries (one per line), and/or pick topics or local news places.
2. Pick the edition (country and language) and the time range, or set exact dates.
3. Set Max articles per input (up to 1,000).
4. Click Start. Download the results as JSON, CSV, Excel or HTML, or get them through the API.

Example input

{
    "queries": ["artificial intelligence", "\"OpenAI\" site:reuters.com"],
    "topics": ["TECHNOLOGY"],
    "edition": "US:en",
    "timeRange": "7d",
    "maxArticles": 100
}

Example output

{
    "type": "search",
    "input": "tesla",
    "edition": "US:en",
    "rank": 1,
    "title": "Tesla's China-made EV sales quicken in September",
    "source": "Reuters",
    "url": "https://www.reuters.com/business/autos-transportation/teslas-china-made-ev-sales-quicken-september-2026-10-09/",
    "publishedAt": "2026-10-09T10:17:44Z",
    "sourceUrl": "https://www.reuters.com",
    "googleNewsUrl": "https://news.google.com/rss/articles/CBMirwFBVV95cUxO...",
    "relatedCoverage": [],
    "scrapedAt": "2026-10-12T17:07:14Z"
}

Topic and local news items also have relatedCoverage: a list of { title, source, googleNewsUrl } for the other articles about the same story.

Use cases

• Brand & PR monitoring - schedule a search for your brand and competitors every hour and send new articles to Slack, email or a spreadsheet.
• Market & investment research - follow companies, tickers and industries across countries and languages.
• SEO & content - see which outlets cover a topic and what angles get published.
• AI agents & RAG - give your agent fresh news with real URLs it can open; works as a tool through the Apify MCP server.
• Media analysis & research - build datasets of articles over months or years.

Integrations

Run it from n8n, Make, Zapier, Google Sheets, the Apify API (Python / JavaScript clients) or as a tool for AI agents via the Apify MCP server. Schedule it to monitor a topic.

Call it from your code

from apify_client import ApifyClient  # pip install apify-client

client = ApifyClient("<YOUR_APIFY_TOKEN>")
run = client.actor("inter974/google-news-scraper").call(run_input={
    "queries": ["nvidia"],
    "timeRange": "1d",
    "maxArticles": 20,
})
for article in client.dataset(run.default_dataset_id).iterate_items():
    print(article["publishedAt"], article["source"], article["title"], article["url"])

Or with a single HTTP request (returns the articles directly):

curl -X POST "https://api.apify.com/v2/acts/inter974~google-news-scraper/run-sync-get-dataset-items" \
  -H "Authorization: Bearer <YOUR_APIFY_TOKEN>" -H "Content-Type: application/json" \
  -d '{"queries": ["nvidia"], "timeRange": "1d", "maxArticles": 20}'

Pricing

Pay per article: you are charged for each article in the dataset. Searches that fail or return nothing are free, and duplicates are removed before you pay.

Apify plan	Price per 1,000 articles
Free	$2.00
Bronze	$1.50
Silver	$1.00
Gold	$0.80

Examples on the Free plan:

• The 20 latest articles about your brand: $0.04
• Your brand checked every hour (past hour, up to 5 articles per check): $7 a month at most
• 1,000 articles about a topic over a year: $2

Apify's free plan includes monthly credits, enough for thousands of articles. See the Pricing tab for the current rates.

Tips & limits

• Google returns at most 100 articles per feed. With a time range of 7 days or more (or dates), the Actor collects up to 1,000 by going day by day. With Past hour or Past 24 hours, the maximum is 100.
• Search results follow Google's relevance order. When the Actor goes day by day, results are sorted from newest to oldest.
• Dates are in UTC. dateTo is included.
• url is null in the rare case where Google refuses to decode a link; googleNewsUrl always works in a browser.
• Topics and local news are the headlines Google shows today; time range and dates apply to searches only.
• The Actor returns headlines and metadata only, not the text of the articles.

FAQ

Why decode the links? Google News RSS links point to news.google.com, not to the article. They need a browser to redirect, so they are useless in a spreadsheet, a database or for an AI agent. This Actor returns the publisher URL directly.

Is there an official Google News API? No. Google shut down its News API years ago. This Actor gives programmatic access to the public Google News results.

Is it legal? The Actor collects public headlines, publisher names, dates and links - no personal data and no article text. Google's terms restrict the use of its news feeds; you are responsible for using the data in line with applicable laws and Google's terms.

Something doesn't work? Open an issue in the Issues tab with your run ID and we'll look into it.