# Google Search Autocomplete Scraper (`scrapebase/google-search-autocomplete-scraper`) Actor 🔎 Google Search Autocomplete Scraper collects real-time Google suggestions for any keyword at scale. 🌍 Set language & location, use proxies, and run A-Z prefixes. Find long-tail keywords, questions & trends for SEO, PPC, and content planning. Export CSV/JSON. 🚀 - **URL**: https://apify.com/scrapebase/google-search-autocomplete-scraper.md - **Developed by:** [ScrapeBase](https://apify.com/scrapebase) (community) - **Categories:** Automation, Lead generation, Social media - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing $19.99/month + usage To use this Actor, you pay a monthly rental fee to the developer. The rent is subtracted from your prepaid usage every month after the free trial period.You also pay for the Apify platform usage, which gets cheaper the higher Apify subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#rental-actors ## 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 ### Google Search Autocomplete Scraper The Google Search Autocomplete Scraper collects real-time Google search suggestions at scale, helping you uncover long-tail keywords, questions, and trends with ease. This Google autocomplete scraper solves the tedious task of manually expanding keyword lists by programmatically querying Google’s suggestion endpoint and returning structured results. It’s built for marketers, developers, data analysts, and researchers who need a reliable Google Suggest scraper / Google search suggestions scraper for SEO, PPC, and content planning — enabling automation-ready keyword discovery at scale. 🚀 ### What data / output can you get? The actor saves one row per processed query with ordered suggestion fields. Field names mirror the dataset exactly. | Data type | Description | Example value | | --------------- | ------------------------------------------------- | ---------------------------------------------- | | query | The processed keyword (including any prefix/suffix expansion) | "how to lose weight a" | | suggestion_01 | First autocomplete suggestion returned by Google | "how to lose weight fast" | | suggestion_02 | Second autocomplete suggestion | "how to lose weight in a week" | | suggestion_03 | Third autocomplete suggestion | "how to lose weight at home" | | suggestion_04 | Fourth autocomplete suggestion | "how to lose weight after pregnancy" | | suggestion_05 | Fifth autocomplete suggestion | "how to lose weight and gain muscle" | | suggestion_06 | Sixth autocomplete suggestion | "how to lose weight app" | | suggestion_07 | Seventh autocomplete suggestion | "how to lose weight after 40" | | suggestion_08 | Eighth autocomplete suggestion | "how to lose weight around belly" | | suggestion_09 | Ninth autocomplete suggestion | "how to lose weight and keep it off" | | suggestion_10 | Tenth autocomplete suggestion | "how to lose weight and feel great" | Notes: - The number of suggestion_NN fields varies by query. Fields are added sequentially using two-digit suffixes (e.g., suggestion_01…suggestion_10). - In the default Apify Dataset view, you’ll see query + suggestion_01 to suggestion_05 columns for quick preview. - Export your dataset as JSON or CSV, or access it via the Apify API for downstream pipelines. ### Key features - ⚡ Bold A–Z expansion controls Turn on usePrefix and/or useSuffix to prepend/append a–z to each keyword (e.g., “a apple”, “apple a”) and unlock more long-tail ideas with bulk Google autocomplete scraping. - 🌍 Language & country targeting Choose language and country to fetch localized Google search predictions. Great for international SEO and geo-specific campaigns. - 🛡️ Smart proxy fallback Automatic proxy progression none → datacenter (GOOGLE_SERP) → residential, with retries on residential to maximize completion rates and reduce blocks. - 🚀 Async batch performance An async engine (aiohttp) with tuned connection limits ensures fast, reliable collection across large keyword lists. - 📦 Clean, developer-friendly output Structured rows with query and suggestion_XX fields — perfect for building a Google autocomplete keyword scraper pipeline in Python or Node.js. - 🔁 Scale-ready workflows Integrate with Apify Datasets and API to power dashboards, keyword clustering, and automation. Ideal for a Google autosuggest scraping tool in production. - 🔎 Real-time insights Fetch live Google autocomplete data (Google autocomplete API style endpoint) to stay ahead of emerging queries, questions, and trends. ### How to use Google Search Autocomplete Scraper - step by step 1. Sign in to Apify Create a free Apify account or log in. 2. Find the actor Open the “Google Search Autocomplete Scraper” in the Apify Store. 3. Add your input data Paste your keywords into the queries field (one per line for bulk Google autocomplete scraping). 4. Configure targeting Optionally select language and country to localize results. 5. Expand coverage (optional) Toggle usePrefix and/or useSuffix to run a–z expansions before/after each keyword and discover more suggestions. 6. Set proxies (optional) Use proxyConfiguration if you plan high-volume runs or need added resilience; the actor already includes automatic fallback logic. 7. Run the actor Click Start. The job will process your queries and save rows containing query and suggestion_NN fields. 8. Download and integrate Export results as JSON or CSV from the Dataset, or fetch them programmatically via the Apify API. Pro Tip: Combine language/country targeting with prefix/suffix expansion for maximum coverage, then pipe results into your SEO stack as a Google autocomplete keyword research tool. ### Use cases | Use case name | Description | | -------------------------------------------- | ----------- | | SEO keyword research & clustering | Expand seed terms into long-tail lists with suggestion_01…NN ordering for clustering and topic mapping. | | PPC query expansion | Generate related queries to improve match types, negatives, and ad group structuring. | | Content planning for blogs & YouTube | Discover questions and intent-rich phrases to plan outlines and titles that match search demand. | | E-commerce search optimization | Unearth product- and category-level suggestions to enhance on-site search and merchandising. | | Market research & trend monitoring | Track shifting query patterns across languages and regions to identify early signals. | | Data science & NLP pipelines | Collect structured, labeled suggestions for training topic models and building semantic graphs. | | Developer APIs & automation workflows | Use the Apify Dataset API to feed downstream apps, dashboards, and ETL pipelines in Python/Node.js. | ### Why choose Google Search Autocomplete Scraper? This production-ready Google keyword suggestions extractor focuses on precision, scale, and clean outputs for real-world SEO and analytics workflows. - ✅ Accurate outputs from Google’s suggestion endpoint (client=firefox) - 🌐 Multilingual and geo targeting via language and country inputs - ⚡ Batch-friendly with asynchronous processing for speed at scale - 🧑‍💻 Developer-first JSON structure (easy to consume in Python, Node.js) - 🛡️ Automatic proxy fallback (GOOGLE_SERP → RESIDENTIAL) for resilience - 💸 Cost-effective alternative to brittle, manual, or extension-based methods - 🔗 Easy exports (CSV/JSON) and integration via Apify Datasets and API In short: a reliable Google autocomplete scraper vs. unstable browser plug-ins — built for automation, consistency, and growth. ### Is it legal / ethical to use Google Search Autocomplete Scraper? Yes — when done responsibly. This actor collects publicly available autocomplete predictions and does not access private or authenticated content. Follow these guidelines: - Respect Google’s terms of service and local laws. - Use data ethically for research, SEO, and application development. - Apply reasonable rate limits and leverage proxies for stability when needed. - Consult your legal team for region-specific compliance requirements. ### Input parameters & output format #### Example JSON input ```json { "queries": [ "how to lose weight", "Machine Learning", "web scraping" ], "language": "English", "country": "United States", "usePrefix": true, "useSuffix": false, "proxyConfiguration": { "useApifyProxy": true } } ```` Parameters - queries (array of string) — Enter keywords or phrases — one per line. Add multiple for bulk scraping. Default: none. Required: Yes. - language (string; enum) — Language of the suggestions. Default: "English". Required: No. - country (string; enum) — Target country for location-specific suggestions. Default: "United States". Required: No. - usePrefix (boolean) — Add a–z before each keyword (e.g., “a apple”, “b apple”) for more results. Default: false. Required: No. - useSuffix (boolean) — Add a–z after each keyword (e.g., “apple a”, “apple b”) for more results. Default: false. Required: No. - proxyConfiguration (object) — Optional: Use proxies for higher volume or to avoid blocks. Default: none (prefill uses Apify Proxy). Required: No. #### Example JSON output Each item represents one processed query with ordered suggestions. Only fields present in the response are saved. ```json { "query": "how to lose weight", "suggestion_01": "how to lose weight fast", "suggestion_02": "how to lose weight in a week", "suggestion_03": "how to lose weight at home", "suggestion_04": "how to lose weight with exercise", "suggestion_05": "how to lose weight and gain muscle", "suggestion_06": "how to lose weight after pregnancy", "suggestion_07": "how to lose weight without exercise", "suggestion_08": "how to lose weight in 2 weeks", "suggestion_09": "how to lose weight quickly", "suggestion_10": "how to lose weight on your face" } ``` Notes: - Fields are named suggestion\_01, suggestion\_02, … using two-digit numbering that reflects the order returned by Google. - The number of suggestion\_NN fields varies by query. - Export results as CSV/JSON or fetch via the Apify Dataset API. ### FAQ #### Is there a free trial or free tier? Yes. This actor offers trial minutes (120) so you can test it before subscribing. A flat monthly plan is available thereafter. #### Do I need proxies to run it? Not necessarily. The actor includes automatic fallback: it starts without a proxy, moves to a datacenter group (GOOGLE\_SERP), and then to RESIDENTIAL if needed. You can also pass proxyConfiguration to customize behavior. #### Can I use it with Python or Node.js? Yes. Results are stored in JSON-friendly fields (query and suggestion\_NN) that you can retrieve via the Apify API and process in Python or Node.js. It’s a great fit for “Google autocomplete scraper Python” or “Google autocomplete scraper Node.js” workflows. #### Does it support location- and language-specific suggestions? Yes. Use the language and country inputs to control hl/gl parameters and collect localized predictions. #### Can I run bulk Google autocomplete scraping? Yes. Provide multiple items in queries and optionally enable usePrefix/useSuffix to generate A–Z expansions for broader coverage. #### How many suggestions are returned per query? It varies by query and locale. The actor saves as many suggestions as Google returns, using sequential fields suggestion\_01, suggestion\_02, and so on. #### Is this the same as a Google autocomplete API? It queries Google’s suggestion endpoint and returns structured results similar to a Google autocomplete API. It’s ideal as a Google autocomplete keyword scraper or Google keyword suggestions extractor. #### What formats can I export? You can export datasets as JSON or CSV, or access them programmatically via the Apify Dataset API for automation. ### Closing CTA / Final thoughts The Google Search Autocomplete Scraper is built for fast, reliable extraction of real-time Google keyword suggestions. Use it to expand seed lists, localize research with language/country targeting, and automate bulk A–Z discovery with clean, ordered outputs. Whether you’re a marketer, developer, analyst, or researcher, this Google autosuggest scraping tool delivers structured data for SEO, PPC, and content planning. Access results via the Apify API to power Python/Node.js pipelines and start extracting smarter keyword insights today. 🌟 # Actor input Schema ## `queries` (type: `array`): Enter keywords or phrases — one per line. Add multiple for bulk scraping. ## `language` (type: `string`): Language of the suggestions ## `country` (type: `string`): Target country for location-specific suggestions ## `usePrefix` (type: `boolean`): Add a-z before each keyword (e.g. a apple, b apple) for more results ## `useSuffix` (type: `boolean`): Add a-z after each keyword (e.g. apple a, apple b) for more results ## `proxyConfiguration` (type: `object`): Optional: Use proxies for higher volume or to avoid blocks ## Actor input object example ```json { "queries": [ "how to lose weight", "best laptops 2024", "healthy breakfast ideas" ], "language": "English", "country": "United States", "usePrefix": false, "useSuffix": false, "proxyConfiguration": { "useApifyProxy": true } } ``` # 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 = { "queries": [ "how to lose weight", "best laptops 2024", "healthy breakfast ideas" ], "proxyConfiguration": { "useApifyProxy": true } }; // Run the Actor and wait for it to finish const run = await client.actor("scrapebase/google-search-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 = { "queries": [ "how to lose weight", "best laptops 2024", "healthy breakfast ideas", ], "proxyConfiguration": { "useApifyProxy": True }, } # Run the Actor and wait for it to finish run = client.actor("scrapebase/google-search-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 '{ "queries": [ "how to lose weight", "best laptops 2024", "healthy breakfast ideas" ], "proxyConfiguration": { "useApifyProxy": true } }' | apify call scrapebase/google-search-autocomplete-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scrapebase/google-search-autocomplete-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Google Search Autocomplete Scraper", "description": "🔎 Google Search Autocomplete Scraper collects real-time Google suggestions for any keyword at scale. 🌍 Set language & location, use proxies, and run A-Z prefixes. Find long-tail keywords, questions & trends for SEO, PPC, and content planning. Export CSV/JSON. 🚀", "version": "0.1", "x-build-id": "qzNPMB7twVTgw3oYG" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scrapebase~google-search-autocomplete-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scrapebase-google-search-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scrapebase~google-search-autocomplete-scraper/runs": { "post": { "operationId": "runs-sync-scrapebase-google-search-autocomplete-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scrapebase~google-search-autocomplete-scraper/run-sync": { "post": { "operationId": "run-sync-scrapebase-google-search-autocomplete-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "queries" ], "properties": { "queries": { "title": "📌 Keywords", "type": "array", "description": "Enter keywords or phrases — one per line. Add multiple for bulk scraping.", "items": { "type": "string" } }, "language": { "title": "🌐 Language", "enum": [ "English", "Spanish", "French", "German", "Italian", "Portuguese", "Russian", "Japanese", "Korean", "Chinese", "Arabic", "Hindi", "Bengali", "Turkish", "Vietnamese", "Polish", "Dutch", "Swedish", "Danish", "Norwegian", "Finnish", "Czech", "Hungarian", "Romanian", "Greek", "Hebrew", "Thai", "Indonesian", "Malay", "Filipino", "Ukrainian", "Bulgarian", "Croatian", "Slovenian", "Slovak", "Lithuanian", "Latvian", "Estonian", "Icelandic", "Irish", "Welsh", "Basque", "Catalan", "Galician", "Afrikaans", "Swahili", "Zulu", "Xhosa", "Amharic", "Somali", "Yoruba", "Hausa", "Igbo", "Persian", "Urdu", "Tamil", "Telugu", "Kannada", "Malayalam", "Marathi", "Gujarati", "Punjabi", "Nepali", "Sinhala", "Burmese", "Khmer", "Lao", "Mongolian", "Tibetan", "Georgian", "Armenian", "Azerbaijani", "Kazakh", "Uzbek", "Turkmen", "Kyrgyz", "Tajik" ], "type": "string", "description": "Language of the suggestions", "default": "English" }, "country": { "title": "📍 Country", "enum": [ "United States", "United Kingdom", "Canada", "Australia", "Germany", "France", "Spain", "Italy", "Brazil", "Mexico", "Argentina", "Chile", "Colombia", "Peru", "Russia", "Ukraine", "Poland", "Netherlands", "Belgium", "Switzerland", "Austria", "Sweden", "Norway", "Denmark", "Finland", "Portugal", "Ireland", "Czech Republic", "Hungary", "Slovakia", "Slovenia", "Croatia", "Greece", "Bulgaria", "Romania", "Turkey", "China", "Japan", "South Korea", "India", "Pakistan", "Bangladesh", "Indonesia", "Malaysia", "Thailand", "Philippines", "Vietnam", "Singapore", "New Zealand", "South Africa", "Egypt", "Nigeria", "Kenya", "Morocco", "Tunisia", "Algeria", "Saudi Arabia", "United Arab Emirates", "Israel", "Jordan", "Lebanon", "Iran", "Iraq", "Kuwait", "Qatar", "Oman", "Bahrain", "Yemen", "Syria" ], "type": "string", "description": "Target country for location-specific suggestions", "default": "United States" }, "usePrefix": { "title": "⚡ Prefix expansion", "type": "boolean", "description": "Add a-z before each keyword (e.g. a apple, b apple) for more results", "default": false }, "useSuffix": { "title": "⚡ Suffix expansion", "type": "boolean", "description": "Add a-z after each keyword (e.g. apple a, apple b) for more results", "default": false }, "proxyConfiguration": { "title": "🔒 Proxy", "type": "object", "description": "Optional: Use proxies for higher volume or to avoid blocks" } } }, "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 } } } } } } } } } } ```