Google Autocomplete Scraper

Collect Google autocomplete keyword suggestions at scale for SEO, content, PPC, product, and market research workflows.

What does Google Autocomplete Scraper do?

Google Autocomplete Scraper turns seed keywords into suggestion datasets you can export, enrich, and monitor. Use it to collect long-tail search phrases from Google autocomplete by language and country. Each dataset row contains the original seed, suggestion text, rank, locale, source URL, and fetch time.

Who is it for?

🧑‍💼 SEO teams building keyword maps for content calendars. 📈 Growth marketers researching search intent before launching campaigns. 🏢 Agencies producing repeatable keyword reports for clients. 🛒 Ecommerce teams discovering product modifiers and category questions. 🧪 Product researchers validating how users phrase problems.

Why use this actor?

✅ Batch many seed keywords in one run. ✅ Localize suggestions with language and country settings. ✅ Export clean rows instead of manually copying dropdown suggestions. ✅ Schedule recurring runs to spot changes in search demand. ✅ Use optional proxy settings for large batches.

What data can you extract?

The actor returns keyword suggestion rows. The table below summarizes the output fields.

Output data table

How to use Google Autocomplete Scraper

1. Add one or more seed keywords.
2. Choose a language such as en, es, de, or fr.
3. Choose a country such as us, gb, de, or br.
4. Keep the default limit of 10 suggestions per keyword for normal runs.
5. Start the actor and export the dataset as JSON, CSV, Excel, or via API.

Input settings

queries is the only required input. Add keywords such as best crm, running shoes, or apify. language controls the Google interface language. country controls the market hint. maxSuggestionsPerQuery caps saved suggestions per seed keyword. requestDelayMs adds a pause between requests for polite larger batches. useProxy is optional and normally disabled for small runs.

Example input

{
  "queries": ["apify", "web scraping", "keyword research"],
  "language": "en",
  "country": "us",
  "maxSuggestionsPerQuery": 10
}

Example output

{
  "query": "apify",
  "suggestion": "apify api",
  "rank": 2,
  "language": "en",
  "country": "us",
  "client": "firefox",
  "sourceUrl": "https://suggestqueries.google.com/complete/search?...",
  "fetchedAt": "2026-06-20T06:20:00.000Z"
}

Tips for better keyword research

Start with broad head terms, then re-run promising suggestions as new seeds. Run separate datasets for different countries instead of mixing markets in one export. Use consistent language and country codes when comparing month-over-month changes. Keep seed lists focused by topic so downstream clustering is easier.

Integrations

Send the dataset to Google Sheets for editorial review. Export CSV files into SEO tools or BI dashboards. Schedule weekly runs and compare new suggestions with previous datasets. Use webhooks to trigger downstream keyword clustering after each successful run.

API usage with Node.js

import { ApifyClient } from "apify-client";
const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const run = await client.actor("fetch_cat/google-autocomplete-scraper").call({
  queries: ["content marketing"],
  language: "en",
  country: "us"
});
console.log(run.defaultDatasetId);

API usage with Python

from apify_client import ApifyClient
client = ApifyClient("<APIFY_TOKEN>")
run = client.actor("fetch_cat/google-autocomplete-scraper").call(run_input={
    "queries": ["content marketing"],
    "language": "en",
    "country": "us",
})
print(run["defaultDatasetId"])

API usage with cURL

curl -X POST "https://api.apify.com/v2/acts/fetch_cat~google-autocomplete-scraper/runs?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"queries":["content marketing"],"language":"en","country":"us"}'

MCP usage

Use Apify MCP tools to run this actor from Claude Code or Claude Desktop. MCP URL: https://mcp.apify.com?tools=fetch_cat/google-autocomplete-scraper

Add the server in Claude Code:

claude mcp add apify-google-autocomplete "https://mcp.apify.com?tools=fetch_cat/google-autocomplete-scraper"

Claude Desktop JSON config example:

{
  "mcpServers": {
    "apify-google-autocomplete": {
      "url": "https://mcp.apify.com?tools=fetch_cat/google-autocomplete-scraper"
    }
  }
}

Example prompts:

• “Run Google Autocomplete Scraper for SaaS onboarding keywords in the US.”
• “Collect autocomplete suggestions for these ecommerce category seeds and summarize patterns.”

Scheduling and monitoring

Create a schedule in Apify Console for weekly or monthly keyword tracking. Use stable seed lists to detect new, removed, or reordered suggestions over time. Export each run and compare by query, suggestion, and rank.

Proxy guidance

Most small runs do not need a proxy. Enable Apify Proxy for large batches, unusual geographies, or repeated automated monitoring. Increase request delay before increasing proxy spend.

Limits and caveats

Google suggestions can change by time, location, language, and personalization experiments. Autocomplete is a discovery signal, not a search volume metric. Some seed keywords may return fewer than the requested maximum suggestions.

Legality and responsible use

This actor collects publicly available autocomplete suggestions. Use the data responsibly and respect applicable laws, platform terms, and privacy obligations. Do not use keyword data to target protected classes or sensitive personal attributes.

Troubleshooting

Why did a keyword return no suggestions? Google may not have suggestions for the term, or it may temporarily limit automated requests. Why do results differ by country? Autocomplete is localized, so market hints can change suggestion text and rank. How can I reduce costs? Keep seed lists focused, use the default 10-suggestion cap, and avoid unnecessary proxy use.

Support

If a run fails, open the run log and include the run URL when reporting the issue. For best support, share your input JSON and expected locale behavior.

Keyword workflow ideas

1. Build a seed list from product categories, customer questions, internal site search terms, or competitor topics.
2. Run localized batches for each target market so language and country differences stay easy to compare.
3. Re-run high-value suggestions as new seeds to expand long-tail keyword coverage.
4. Group suggestions by modifier, intent, product category, and question pattern.
5. Separate brand and non-brand phrases before passing terms to SEO or paid search teams.
6. Compare recurring exports to detect seasonal shifts, emerging topics, and disappearing suggestions.
7. Export CSV or Excel files for editorial briefs, content calendars, and client reporting.
8. Use Apify API, webhooks, or schedules to plug recurring keyword discovery into your existing workflow.