Google News Search - Python client

Collect Google News headlines, publishers, publication dates and article links.

This client starts the hosted Apify Actor, waits for completion and downloads Dataset records. It also reads SUMMARY and ERRORS when available. Source coverage and result fields depend on the Actor.

Install

pip install apivault-google-news-search

Run

Set APIFY_TOKEN in your environment. Replace the example values with your own valid inputs from the Actor input form.

from apivault_google_news_search import Client, WaitTimeout

client = Client()
actor_input = {
    "queries": [
        "OpenAI"
    ],
    "maxResults": 5
}
result = client.run(actor_input, max_total_charge_usd=0.05, max_rows=100)
print(result.run_id, len(result.rows), result.truncated)
print(result.summary)

An Apify account and token are required. The package has no subscription of its own; hosted runs are billed by Apify under the Actor pricing. The event charge cap may not include platform usage. Review the current price before changing it. max_rows limits the download, not the Actor collection or its charges; configure an Actor result limit too.

Resume and diagnostics

client.start(input) returns a run ID; client.wait(run_id) and client.result(run_id) resume an existing run. A WaitTimeout exposes run_id. Starting a run is never automatically retried. Use an existing run ID after a connection or waiting failure to avoid duplicate paid runs. Failed and aborted runs raise ActorError. No-match and partial results require inspecting summary and errors; diagnostic rows must not be mistaken for useful results.

Rows can be serialized as JSON or used in your own CSV/Excel pipeline. Native Dataset exports are available in Apify. Optional integrations and credentials depend on the Actor.

Hosted Actor | Source | PyPI

Related integration

Python client · PyPI · n8n source and workflow · npm

Source coverage and limits

News feeds are bounded snapshots. Daily mode and optional article text use the hosted Actor settings and can increase resources. Neither mode guarantees a complete archive. Repeat runs can bill the same delivered article again. Keep your own articleId-based history for monitoring.