Google Autocomplete Actor

Google Autocomplete Scraper 🔍

Google Autocomplete Scraper is an Apify actor that scrapes autocomplete suggestions for a given query. If you’re doing SEO autocomplete keyword research, building an autocomplete keyword scraper workflow, or simply trying to programmatically get autocomplete suggestions at scale, this tool helps you turn those suggestion lists into structured data—saving you hours of manual work.

Whether you’re a marketer, data analyst, or researcher, Google autocomplete extraction and autosuggest keyword scraper outputs can feed content planning, keyword clustering, and rankings autocomplete scraping experiments—without switching tools or copy/pasting from your browser.

---

Why choose Google Autocomplete Scraper?

Feature	Benefit
✅ All-in-one autocomplete keyword research	Lets you scrape suggestions for your main query in one run
✅ Reliability-focused scraping	Includes resilient request handling and structured fallbacks when requests fail
✅ Structured output for automation	Produces consistent JSON fields so you can import into spreadsheets, BI tools, or pipelines
✅ Scale-friendly batch generation	Automatically expands suggestions using your alphabet-based prefix/suffix options
✅ Language and country targeting	Helps you tailor suggestions using your selected language and country
✅ Designed for repeatable workflows	Built for repeat runs across multiple queries for consistent “google autocomplete list scraper” operations

---

Key features

• 🔍 Autocomplete suggestions for your query: Scrapes suggestions for the provided query so you can extract Google autocomplete keywords programmatically.
• 🌍 Language + country targeting: Uses your selected language and country inputs to shape the suggestion results you collect.
• 🔤 Prefix options: With use_prefix, the actor can generate and scrape alphabet-prefixed queries to widen your keyword discovery (useful for “google autocomplete list scraper” style expansion).
• 🔤 Suffix options: With use_suffix, it can generate and scrape alphabet-suffixed queries to expand the suggestion pool for “autosuggest keyword scraper” workflows.
• 📊 Consistent field ordering: Outputs a predictable set of fields like suggestion_01, suggestion_02, etc., to keep your dataset easy to analyze.
• 💾 Dataset-ready JSON: Saves results to the default dataset using await Actor.push_data(all_suggestions), ready for export to JSON/CSV downstream.

---

Input

Provide input via an input.json file. Example structure:

{
  "query": "apple watch",
  "language": "English",
  "country": "United States",
  "use_prefix": false,
  "use_suffix": true
}

Input Fields

Field	Required	Description
query	Yes	The search term you want suggestions for (e.g., “apple watch”).
language	No	Select the language to use for suggestions. Defaults to English.
country	No	Select the country to use for suggestions. Defaults to United States.
use_prefix	No	Whether to add alphabetic prefixes to the query (generates additional prefix-based queries). Defaults to false.
use_suffix	No	Whether to add alphabetic suffixes to the query (generates additional suffix-based queries). Defaults to false.

---

Output

The actor saves your results to the default dataset in JSON format (it pushes all_suggestions, where each item contains the original query plus labeled suggestions fields like suggestion_01).

Example output:

[
  {
    "query": "apple watch",
    "suggestion_01": "apple watch series",
    "suggestion_02": "apple watch bands",
    "suggestion_03": "apple watch waterproof"
  }
]

Note: Each output object uses the same schema style: query plus suggestion_XX keys (zero-padded ordering like suggestion_01, suggestion_02, etc.).

Output Fields

Field	Type	Description
query	string	The query string that was used to fetch suggestions.
suggestion_01	string	The first suggestion returned for that query.
suggestion_02	string	The second suggestion returned for that query.
suggestion_03	string	The third suggestion returned for that query.
suggestion_04	string	The fourth suggestion returned for that query.
suggestion_05	string	The fifth suggestion returned for that query.
suggestion_06	string	The sixth suggestion returned for that query.
suggestion_07	string	The seventh suggestion returned for that query.
suggestion_08	string	The eighth suggestion returned for that query.
suggestion_09	string	The ninth suggestion returned for that query.
suggestion_10	string	The tenth suggestion returned for that query.

Because suggestions are indexed and labeled in a consistent way, this output is ideal when you’re scraping google autocomplete keywords for analysis or when you’re comparing autocomplete keyword research sets across languages/countries.

---

How to use Google Autocomplete Scraper (via Apify Console)

1. Open Apify Console 🔐
   Log in at https://console.apify.com and go to the Actors section.

2. Find the actor 🔎
   Search for Google Autocomplete Scraper and open the actor page.

3. Enter your INPUT 🧩
   In the INPUT field, paste JSON matching the schema, including at least query.
   You can also set language, country, use_prefix, and use_suffix.

4. Run the actor ▶️
   Click Run to start scraping autocomplete keyword suggestions.

5. Watch the logs 🧾
   During the run, you’ll see log messages indicating the scraping process and progress.

6. Open the OUTPUT dataset 📦
   After completion, open the OUTPUT tab to view the dataset with your structured suggestion objects.

7. Export results 💾
   Export the dataset from Apify in formats that work for your workflow (commonly JSON/CSV in downstream tools).

No coding required—get accurate autocomplete extraction results in minutes.

---

Advanced features & SEO optimization

• ⚡ Engineered for autocomplete expansion: With use_prefix and use_suffix, Google autosuggest scraper workflows become easier by generating more query variations automatically.
• 🌐 Targeted research by language and location: Set language and country to support more relevant “scrape google autocomplete keywords” and “google suggest scraper” experiments.
• 📌 Reliable, structured “keyword list” outputs: The actor formats suggestions into consistent fields (suggestion_01, suggestion_02, …), making it straightforward to compare “google autocomplete extraction” results across runs.
• 🧠 Practical for SEO autocomplete keyword research: Use it for “google autocomplete keyword research” and “extract google autocomplete suggestions” tasks where speed and repeatability matter.

---

Best use cases

• 📈 SEO keyword research for content planning: Quickly expand seed topics into suggestion lists for autocomplete keyword research and ideation.
• 🧠 Keyword clustering and grouping: Build structured datasets to analyze patterns in autosuggest keyword scraper outputs.
• 🔍 Internationalized search exploration: Compare suggestion sets across languages and countries using the language and country inputs.
• 🛠️ Marketing ops automation: Feed programmatically scraped suggestions into spreadsheets, dashboards, or reporting pipelines.
• 💻 Developer workflows for keyword discovery: Programmatically get autocomplete keyword data for testing, research, and experimentation.
• 📊 Competitive keyword gap research: Use repeatable runs to measure how autocomplete suggestion coverage changes across queries.

---

Technical specifications

• Supported Input Formats

  • ✅ query as a string (required)
  • ✅ language as a string from the provided enum
  • ✅ country as a string from the provided enum
  • ✅ use_prefix as boolean
  • ✅ use_suffix as boolean

• Proxy Support

  • ❗ Not specified in the provided actor input schema.
  • The actor includes resilient request handling, but proxy configuration fields are not part of the input schema you provided.

• Retry Mechanism

  • ❗ Not specified in the provided actor input schema.
  • The actor handles request failures by catching request exceptions and returning an empty list for that request.

• Dataset Structure

  • ✅ A JSON array is pushed to the default dataset.
  • ✅ Each array item contains query plus suggestion_XX fields (zero-padded).

• Rate Limits & Performance

  • ❗ Exact limits are not specified in the provided source data.
  • Performance will depend on how many prefix/suffix requests are generated by use_prefix / use_suffix.

• Limitations

  • ❌ No additional fields beyond query and suggestion_XX are produced in the output schema shown in the provided code.
  • ❌ The actor does not include an explicit per-row success or error_message field in the dataset output.

---

FAQ

What does Google Autocomplete Scraper return?

✅ It returns a JSON array where each item includes the original query and labeled suggestion fields such as suggestion_01, suggestion_02, and so on, generated from the autocomplete suggestions.

Do I need to provide a country and language?

❌ No. You only must provide query. If you don’t set language or country, the actor uses defaults defined in the input handling logic (defaults to English for language and United States for country).

What is use_prefix and when should I enable it?

✅ use_prefix controls whether the actor generates additional queries by adding alphabetic prefixes to your query. This is useful when you want broader keyword discovery—an approach common in autocomplete keyword scraper workflows.

What is use_suffix and when should I enable it?

✅ use_suffix controls whether the actor generates additional queries by adding alphabetic suffixes to your query. Enabling it helps you extract more suggestion variations for autosuggest keyword scraper-style research.

Can I use this for SEO autocomplete keyword research?

✅ Yes. The actor is directly set up to help you scrape autocomplete suggestions for a given topic, making it practical for google autocomplete keyword research and extract google autocomplete suggestions projects.

Does it require any login or special setup?

❌ No special authentication is described in the provided input schema. You configure parameters in Apify Console and run the actor.

Can I integrate the output into my pipeline?

💻 Yes. The actor saves structured JSON objects to the dataset, which you can export and load into spreadsheets, BI tools, or automation scripts. It’s designed to support “google autocomplete data scraper” style workflows.

---

Support & feature requests

If you’re using Google Autocomplete Scraper for google autocomplete scraper and google autocomplete extraction workflows, tell us what would make your runs smoother.

• 💡 Feature Requests: For example, adding alternative output shapes (like flat rows for each suggestion), or improving how expansions are controlled for prefix/suffix use cases (ideal for google autocomplete list scraper projects).
• 📧 Contact: Reach out via dataforleads@gmail.com.

Your feedback helps shape the roadmap for the Google Autocomplete Scraper.

---

Google Autocomplete Scraper — Final thoughts

If you’re building an SEO autocomplete keyword research workflow, Google Autocomplete Scraper gives you structured autocomplete suggestion data you can reuse immediately.
Get results faster with this SEO-optimized google autocomplete scraper approach for your next keyword discovery sprint.

---

Disclaimer

This tool accesses publicly accessible sources to scrape autocomplete suggestion data. It does not access private profiles, authenticated data, or password-protected content.

You are responsible for ensuring your use complies with applicable laws and regulations (for example, GDPR/CCPA where relevant), as well as platform terms and rules. Please use this actor responsibly, ethically, and only for legitimate purposes.

For data-removal requests, contact dataforleads@gmail.com.