# Google Autocomplete Scraper — Suggestions (`scrape.badger/google-autocomplete-scraper`) Actor
Scrape Google's autocomplete / suggestions endpoint at scale. Returns up to 10 suggestions per seed, enriched with Knowledge Graph entity\_name + thumbnail when Google surfaces them. Supports bulk seeds (one run = many queries). Ideal for SEO keyword research, long-tail discovery, and entity mapping.
- **URL**: https://apify.com/scrape.badger/google-autocomplete-scraper.md
- **Developed by:** [Scrape Badger](https://apify.com/scrape.badger) (community)
- **Categories:** SEO tools, Developer tools, Automation
- **Stats:** 2 total users, 1 monthly users, 50.0% runs succeeded, NaN bookmarks
- **User rating**: No ratings yet
## Pricing
from $0.50 / 1,000 autocomplete calls
This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event
## What's an Apify Actor?
Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".
## How to integrate an Actor?
If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.
In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md):
```bash
npm install apify-client
```
In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md):
```bash
pip install apify-client
```
In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md):
````bash
# MacOS / Linux
curl -fsSL https://apify.com/install-cli.sh | bash
# Windows
irm https://apify.com/install-cli.ps1 | iex
```bash
In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md).
If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md).
For usage examples, see the [API](#api) section below.
For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).
# README
### What does Google Autocomplete Scraper do?
Scrape Google's autocomplete / suggestions endpoint at scale — up to 10 suggestions per seed, enriched with Knowledge Graph `entity_name` and `thumbnail` when Google surfaces them. Perfect for SEO long-tail keyword discovery.
### Why use Google Autocomplete Scraper?
- **Bulk seeds.** Paste many seeds — each runs one autocomplete call.
- **Alphabet expand.** Fan each seed into 36 child queries (`seed a`, `seed b`, …) — up to 360 long-tail ideas per seed.
- **Knowledge Graph enrichment.** Suggestions for companies / people / movies / stocks carry entity_name + thumbnail.
- **200+ country domains.** Local-market long-tail discovery with `gl`.
- **Dirt cheap.** $0.50 / 1k calls.
### What data can Google Autocomplete Scraper extract?
| Field | Type | Description |
|---|---|---|
| seed | string | Input query that produced this suggestion |
| query | string | Actual query sent to Google (= seed unless alphabet-expanded) |
| value | string | Suggestion text |
| entity_name | string | Knowledge Graph entity name (when surfaced) |
| thumbnail | string | KG thumbnail URL (when surfaced) |
| position | number | Rank within the seed's suggestion list |
### How to scrape Google Autocomplete
1. Click **Try for free**.
2. Paste one or many seed queries into **Seed queries** (one per line).
3. Optional: toggle `alphabet_expand` for 36× fan-out per seed.
4. Set `gl` / `hl` for the country + language.
5. Click **Start** — suggestions stream into the dataset, tagged with source seed.
### How much will it cost?
**$0.0005 per call (≈ $0.50 per 1,000 calls).** Without `alphabet_expand`: 1 seed = 1 call. With it: 1 seed = 36 calls. Budget accordingly — 100 seeds × alphabet_expand = 3,600 calls ≈ $1.80.
#### Competitor benchmark
| Actor | Author | Price | Notes |
|---|---|---|---|
| apify/google-search-scraper | Apify | ~$3.50 / 1k pages | Autocomplete not natively supported |
| Keyword-research SaaS (Ahrefs, Semrush) | — | $99-$449 /mo subscription | Subscription model |
| **scrape-badger/google-autocomplete-scraper** | **ScrapeBadger** | **$0.50 / 1k calls** | **Pay-per-use, KG enrichment** |
### Input
Configure the run in the **Input** tab above, or pass a JSON object matching the fields below when calling the Actor via the Apify API.
| Field | Required | Description |
|---|---|---|
| seeds | ✅ | One seed per line, or comma-separated. |
| alphabet_expand | — | Fan each seed into 36 child queries (default `false`). |
| gl | — | Country code (default `us`). |
| hl | — | Language code (default `en`). |
### Output
Every successful run streams records into the run's dataset. Download as JSON, CSV, XML, Excel, or HTML from the **Dataset** tab; consume programmatically via the Apify API or webhooks.
Example record:
```json
[
{
"seed": "apple",
"query": "apple",
"value": "apple stock",
"position": 1
},
{
"seed": "apple",
"query": "apple",
"value": "apple store",
"entity_name": "Apple Inc.",
"thumbnail": "https://\u2026",
"position": 2
},
{
"seed": "apple",
"query": "apple",
"value": "apple tv",
"position": 3
}
]
````
### Tips / Advanced options
- **Alphabet expand for SEO research.** `seed = best running shoes` → fan out into 36 → 360 long-tail keyword ideas. Beats Ahrefs for breadth.
- **Knowledge Graph entities = brand queries.** Suggestions with `entity_name` are brand/product/person searches — higher commercial intent.
- **Cron for trend tracking.** Suggestions shift hourly — daily runs capture what's trending in your niche.
- **Pair with `google-search-scraper`.** Expand seeds → rank each suggestion's SERP → find content gaps.
### FAQ, Disclaimers, Support
#### What's `alphabet_expand`?
Google's autocomplete returns different suggestions based on what you've typed so far. `alphabet_expand` queries `seed a`, `seed b`, …, `seed z`, `seed 0`, …, `seed 9` — 36 child queries per seed — so you get up to 360 distinct suggestions.
#### Why the Knowledge Graph enrichment?
Google's search box auto-surfaces entity info (company logos, movie posters) when you type a known entity. This actor captures it.
#### Can I autocomplete in different verticals (images, news)?
Not via this actor — the underlying endpoint returns web-search suggestions only.
#### Is this cheaper than Ahrefs?
Significantly — Ahrefs starts at $99/mo flat. This actor is pay-per-use from $0.0005 per call.
#### Disclaimer
This Actor scrapes public Google data only. You're responsible for compliance with Google's Terms of Service and any applicable data-protection laws (GDPR, CCPA, etc.) in your jurisdiction. ScrapeBadger does not store the scraped results — they are delivered directly to your Apify dataset.
#### Support
Something not working? Open a ticket in the **Issues** tab above — we triage within one business day. Full API reference: [docs.scrapebadger.com](https://docs.scrapebadger.com).
#### Related Actors
- [`google-search-scraper`](https://apify.com/scrape-badger/google-search-scraper) — Rank the SERPs for each suggestion
- [`google-trends-scraper`](https://apify.com/scrape-badger/google-trends-scraper) — Track search-interest over time for each suggestion
#### Powered by
[ScrapeBadger](https://scrapebadger.com) — Google-optimised residential proxy pool + browser-farm fallback, 99.7% uptime, unmetered bandwidth. No CAPTCHAs reach you.
# Actor input Schema
## `seeds` (type: `string`):
One seed per line, or comma-separated. Example: apple, best running shoes. Each seed runs one autocomplete call (up to 10 suggestions back).
## `alphabet_expand` (type: `boolean`):
When true, fan each seed out into 36 child queries (seed a, seed b, …, seed z, seed 0, …, seed 9). Produces ~360 suggestions per seed — useful for SEO long-tail discovery.
## `gl` (type: `string`):
ISO 3166-1 alpha-2 country code (lowercase).
## `hl` (type: `string`):
Language code for the response (BCP-47, e.g. 'en', 'en-GB', 'de'). Affects result language and locale-specific labels.
## Actor input object example
```json
{
"seeds": "apple\nbest vpn\nhow to",
"alphabet_expand": false,
"gl": "us",
"hl": "en"
}
```
# Actor output Schema
## `dataset` (type: `string`):
No description
# API
You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.
## JavaScript example
```javascript
import { ApifyClient } from 'apify-client';
// Initialize the ApifyClient with your Apify API token
// Replace the '' with your token
const client = new ApifyClient({
token: '',
});
// Prepare Actor input
const input = {
"seeds": `apple
best vpn
how to`
};
// Run the Actor and wait for it to finish
const run = await client.actor("scrape.badger/google-autocomplete-scraper").call(input);
// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
console.dir(item);
});
// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs
```
## Python example
```python
from apify_client import ApifyClient
# Initialize the ApifyClient with your Apify API token
# Replace '' with your token.
client = ApifyClient("")
# Prepare the Actor input
run_input = { "seeds": """apple
best vpn
how to""" }
# Run the Actor and wait for it to finish
run = client.actor("scrape.badger/google-autocomplete-scraper").call(run_input=run_input)
# Fetch and print Actor results from the run's dataset (if there are any)
print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
print(item)
# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start
```
## CLI example
```bash
echo '{
"seeds": "apple\\nbest vpn\\nhow to"
}' |
apify call scrape.badger/google-autocomplete-scraper --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=scrape.badger/google-autocomplete-scraper",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "Google Autocomplete Scraper — Suggestions",
"description": "Scrape Google's autocomplete / suggestions endpoint at scale. Returns up to 10 suggestions per seed, enriched with Knowledge Graph entity_name + thumbnail when Google surfaces them. Supports bulk seeds (one run = many queries). Ideal for SEO keyword research, long-tail discovery, and entity mapping.",
"version": "1.0",
"x-build-id": "iN0matoH0cS9Vgpv6"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/scrape.badger~google-autocomplete-scraper/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-scrape.badger-google-autocomplete-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/scrape.badger~google-autocomplete-scraper/runs": {
"post": {
"operationId": "runs-sync-scrape.badger-google-autocomplete-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/scrape.badger~google-autocomplete-scraper/run-sync": {
"post": {
"operationId": "run-sync-scrape.badger-google-autocomplete-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
}
},
"components": {
"schemas": {
"inputSchema": {
"type": "object",
"required": [
"seeds"
],
"properties": {
"seeds": {
"title": "Seed queries",
"type": "string",
"description": "One seed per line, or comma-separated. Example: apple, best running shoes. Each seed runs one autocomplete call (up to 10 suggestions back)."
},
"alphabet_expand": {
"title": "Alphabet expand",
"type": "boolean",
"description": "When true, fan each seed out into 36 child queries (seed a, seed b, …, seed z, seed 0, …, seed 9). Produces ~360 suggestions per seed — useful for SEO long-tail discovery.",
"default": false
},
"gl": {
"title": "Country",
"type": "string",
"description": "ISO 3166-1 alpha-2 country code (lowercase).",
"default": "us"
},
"hl": {
"title": "Language",
"type": "string",
"description": "Language code for the response (BCP-47, e.g. 'en', 'en-GB', 'de'). Affects result language and locale-specific labels.",
"default": "en"
}
}
},
"runsResponseSchema": {
"type": "object",
"properties": {
"data": {
"type": "object",
"properties": {
"id": {
"type": "string"
},
"actId": {
"type": "string"
},
"userId": {
"type": "string"
},
"startedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"finishedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"status": {
"type": "string",
"example": "READY"
},
"meta": {
"type": "object",
"properties": {
"origin": {
"type": "string",
"example": "API"
},
"userAgent": {
"type": "string"
}
}
},
"stats": {
"type": "object",
"properties": {
"inputBodyLen": {
"type": "integer",
"example": 2000
},
"rebootCount": {
"type": "integer",
"example": 0
},
"restartCount": {
"type": "integer",
"example": 0
},
"resurrectCount": {
"type": "integer",
"example": 0
},
"computeUnits": {
"type": "integer",
"example": 0
}
}
},
"options": {
"type": "object",
"properties": {
"build": {
"type": "string",
"example": "latest"
},
"timeoutSecs": {
"type": "integer",
"example": 300
},
"memoryMbytes": {
"type": "integer",
"example": 1024
},
"diskMbytes": {
"type": "integer",
"example": 2048
}
}
},
"buildId": {
"type": "string"
},
"defaultKeyValueStoreId": {
"type": "string"
},
"defaultDatasetId": {
"type": "string"
},
"defaultRequestQueueId": {
"type": "string"
},
"buildNumber": {
"type": "string",
"example": "1.0.0"
},
"containerUrl": {
"type": "string"
},
"usage": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "integer",
"example": 1
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
},
"usageTotalUsd": {
"type": "number",
"example": 0.00005
},
"usageUsd": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "number",
"example": 0.00005
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
}
}
}
}
}
}
}
}
```