How to use Google News Scraper

Step by step: in the Apify Store, over the API, from JavaScript or Python, in n8n, and from AI agents through MCP or LangChain.

$3.60 per 1,000 articles on the Free plan, down to $0.90 on higher plans (live Apify price on 2026-10-31).

1. In the Apify Store (no code)

1. Open arcadium/google-news and sign in to Apify (a free account works).
2. Fill in the input form. The fields below are the ones the example uses.
3. Set a maximum cost per run in the run options if you want a hard cap.
4. Start the run. Rows appear in the Output tab as they arrive.
5. Export the dataset as JSON, CSV or Excel, or connect an integration.

Example input:

{
  "queries": [
    "artificial intelligence"
  ],
  "language": "en",
  "country": "US",
  "maxArticlesPerQuery": 20
}

2. Over the Apify API

The synchronous endpoint runs the actor and returns its rows in one call (for runs up to about five minutes; use the asynchronous run endpoint for longer ones).

curl -X POST "https://api.apify.com/v2/acts/arcadium~google-news/run-sync-get-dataset-items" \
  -H "Authorization: Bearer $APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"queries":["artificial intelligence"],"language":"en","country":"US","maxArticlesPerQuery":20}'

3. From JavaScript

// npm install apify-client
import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const run = await client.actor('arcadium/google-news').call({
  "queries": [
    "artificial intelligence"
  ],
  "language": "en",
  "country": "US",
  "maxArticlesPerQuery": 20
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

4. From Python

# pip install apify-client
import os
from apify_client import ApifyClient

client = ApifyClient(os.environ["APIFY_TOKEN"])
run = client.actor("arcadium/google-news").call(run_input={
    "queries": ["artificial intelligence"],
    "language": "en",
    "country": "US",
    "maxArticlesPerQuery": 20,
})
items = client.dataset(run["defaultDatasetId"]).list_items().items
print(items)

5. In n8n

Paste this node into a workflow (it is a plain HTTP Request node). Create a Header Auth credential with name Authorization and value Bearer <APIFY_TOKEN>; each returned row becomes one item for the next node.

{
  "nodes": [
    {
      "name": "Run arcadium/google-news",
      "type": "n8n-nodes-base.httpRequest",
      "typeVersion": 4,
      "position": [
        0,
        0
      ],
      "parameters": {
        "method": "POST",
        "url": "https://api.apify.com/v2/acts/arcadium~google-news/run-sync-get-dataset-items",
        "authentication": "genericCredentialType",
        "genericAuthType": "httpHeaderAuth",
        "sendBody": true,
        "specifyBody": "json",
        "jsonBody": "{\"queries\":[\"artificial intelligence\"],\"language\":\"en\",\"country\":\"US\",\"maxArticlesPerQuery\":20}",
        "options": {
          "timeout": 300000
        }
      }
    }
  ],
  "connections": {}
}

6. From an AI assistant (MCP)

Add Apify’s MCP server to Claude Desktop, Cursor, VS Code or any MCP client, limited to this actor:

{
  "mcpServers": {
    "apify": {
      "url": "https://mcp.apify.com/?tools=arcadium/google-news",
      "headers": {
        "Authorization": "Bearer <APIFY_TOKEN>"
      }
    }
  }
}

More on the MCP page.

7. From LangChain

# pip install langchain-apify   (reads APIFY_API_TOKEN from the environment)
from langchain_apify import ApifyActorsTool

tool = ApifyActorsTool("arcadium/google-news")
rows = tool.invoke({"run_input": {
    "queries": ["artificial intelligence"],
    "language": "en",
    "country": "US",
    "maxArticlesPerQuery": 20,
}})
# or give `tool` to an agent: create_react_agent(llm, [tool])

Sample output

One row, from the example in the actor’s README. Long text is shortened and lists are cut to two items.

{
  "rowType": "article",
  "platform": "google-news",
  "articleId": "CBMiggFBVV95cUxPYXI4MDQ5UDdXZDNuUUpwYkVwY3RYM05lZzhqcFhlb0NfeDVacEFfYkNQWkRuNDZPM05pRndYcjZ2QXBxeHA1ZnpaSVZjNEhXRnZvdkhTZTR3aFppbVlxUXJpeHRhSXRvV0R1VFRuUmhNe...",
  "title": "Southwest Airlines beats estimates as fuel costs ease",
  "publisher": "CNBC",
  "publisherDomain": "cnbc.com",
  "publisherUrl": "https://www.cnbc.com",
  "url": "https://www.cnbc.com/2026/10/21/southwest-airlines-luv-3q-2026-earnings.html",
  "urlDecoded": true,
  "googleNewsUrl": "https://news.google.com/rss/articles/CBMiggFBVV95cUxPYXI4MDQ5UDdX...?oc=5",
  "publishedAt": "2026-10-21T11:02:00.000Z",
  "snippet": "Southwest Airlines reported third-quarter results that topped expectations ...",
  "thumbnailUrl": "https://image.cnbcfm.com/api/v1/image/108444221-1792627087371.jpg",
  "storyId": "3f9a1c0d52e8b7a4",
  "isStoryLead": true,
  "relatedCount": 4,
  "relatedCoverage": [
    {
      "articleId": "CBMie0FVX3lxTE5aVW9GbzJYU2tyRUZaZXhuYVZLUzNKaURYMF96UUxGelZE",
      "title": "Southwest earnings top forecasts",
      "publisher": "Reuters",
      "googleNewsUrl": "https://news.google.com/rss/articles/CBMie0FVX3lxTE5aVW9GbzJYU2tyRUZaZXhuYVZLUzNKaURYMF96UUxGelZE?oc=5"
    }
  ],
  "isPaywalled": false,
  "textStatus": "extracted",
  "text": "Southwest Airlines on Wednesday reported third-quarter earnings ...",
  "wordCount": 812,
  "pageLanguage": "en",
  "source": "BUSINESS",
  "sourceType": "section",
  "position": 1,
  "edition": "US:en",
  "scrapedAt": "2026-10-22T08:00:00.000Z"
}

Schedule it

In Apify Console, create a Task from your input and add a Schedule (for example daily). Each run writes a new dataset; integrations can push the rows to Google Sheets, Slack, a webhook or your database.