# Google Autocomplete Scraper (`scrapeflux/google-autocomplete-scraper`) Actor 🔎 Google Autocomplete Scraper extracts high-intent search suggestions from Google autocomplete for keyword research, SEO insights, and market discovery. ⚡ Fast, reliable, and ready for analysts, marketers & growth teams. - **URL**: https://apify.com/scrapeflux/google-autocomplete-scraper.md - **Developed by:** [ScrapeFlux](https://apify.com/scrapeflux) (community) - **Categories:** SEO tools, Lead generation, Other - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $2.99 / 1,000 results This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 ### Google Autocomplete Scraper ⚡ Getting keyword ideas you can actually use takes forever—manual suggestion hunting is slow, inconsistent, and hard to scale. **Google Autocomplete Scraper** pulls autocomplete suggestions for your query in bulk, with options to expand coverage for keyword research and long-tail discovery. Use it as a **Google autocomplete scraper** for SEO-focused workflows, or as a **Google suggest scraper** when you need fresh suggestion lists. Built for marketers, SEO specialists, and analysts who want faster keyword ideation without the copy‑paste grind. In one run, you can turn a single seed query into a structured set of suggestions in minutes. --- ### See the Data: Sample Output Here's a real record from a single run: ```json { "query": "apple watch", "suggestion_01": "apple watch bands", "suggestion_02": "apple watch deals", "suggestion_03": "apple watch series 9", "suggestion_04": "apple watch accessories", "suggestion_05": "apple watch price", "suggestion_06": "apple watch waterproof", "suggestion_07": "apple watch setup", "suggestion_08": "apple watch battery life", "suggestion_09": "apple watch refurbished", "suggestion_10": "apple watch trade in" } ```` Because this actor expands suggestions, your dataset may contain multiple records with different `query` values (for example, when prefix/suffix options are enabled). | Field | Type | What It Tells You | |---|---|---| | `query` | string | The exact query that was used to generate the suggestions record | | `suggestion_01` | string | The first suggestion returned for that query (highest-priority in the output ordering) | | `suggestion_02` | string | A follow-up autocomplete suggestion to build out keyword variations | | `suggestion_03` | string | Additional suggested wording you can use for content briefs or ads | | `suggestion_04` | string | More autocomplete ideas for your keyword research | | `suggestion_05` | string | Suggestion ideas that can support long-tail keyword extraction | | `suggestion_06` | string | Another suggestion variant you can include in a keyword list | | `suggestion_07` | string | Candidate phrase useful for building topical clusters | | `suggestion_08` | string | More autocomplete keyword ideas from the autosuggest source | | `suggestion_09` | string | A deeper long-tail suggestion option | | `suggestion_10` | string | The tenth suggestion returned for that query | | `status` | string | Not included in the actor output; if you need run-level success/failure, check the run log | Export your full dataset as JSON, CSV, or Excel from the Apify dashboard. *** ### Setting It Up Drop this into your `input.json` and you're ready to go: ```json { "query": "apple watch", "language": "English", "country": "United States", "use_prefix": false, "use_suffix": true } ``` | Parameter | Required | What It Does | |---|---:|---| | `query` | ✅ | The search term to get autocomplete suggestions for | | `language` | ⬜ | Selects the language used when generating suggestions | | `country` | ⬜ | Selects the country used to scope the suggestions | | `use_prefix` | ⬜ | Whether to add alphabetic prefixes to the query (broadens keyword idea coverage) | | `use_suffix` | ⬜ | Whether to add alphabetic suffixes to the query (broadens keyword idea coverage) | ↳ Note: `query` is the only required field in the input schema. *** ### What It Does Google Autocomplete Scraper fetches autocomplete suggestions for your seed query and outputs them as structured JSON records you can analyze right away. #### Keyword research with structured suggestions You provide a `query`, and the actor returns a record containing `suggestion_01` through `suggestion_10`. This makes it straightforward to use a Google autocomplete keyword extraction workflow for SEO planning, content briefs, or campaign ideation. #### Expanded coverage with prefix/suffix expansion By toggling `use_prefix` and `use_suffix`, you can generate additional records for many alphabet-based variations of your original query. This is useful when you want more autocomplete keyword research than a single suggestion list provides. #### Clean, integration-friendly JSON output Each record includes a `query` plus up to 10 suggestion fields (`suggestion_01` to `suggestion_10`). The consistent output format makes it easy to feed into spreadsheets, BI tools, or downstream keyword workflows. #### Built for practical workflow automation Runs write results to the default dataset in a structured format, so you can export immediately or integrate with automations using Apify’s ecosystem. If you’re comparing sets over time (or between language/country settings), the dataset records are ready for analysis. #### Includes error handling in the run output process When requests fail, the actor logs errors and returns an empty list for that suggestion fetch, so the run can proceed without crashing mid-way. You can review the actor logs to understand what happened for each request. Overall, Google Autocomplete Scraper turns autocomplete keyword ideas from a single seed into organized data quickly—ideal for keyword ideas from Google autocomplete and long-tail keyword scraper style workflows. *** ### Why Google Autocomplete Scraper? There are plenty of ways to pull data from autocomplete experiences—here’s why Google Autocomplete Scraper stands out. #### Ready-to-use results (not manual copying) Instead of manually scraping suggestions one by one, Google Autocomplete Scraper outputs suggestions as structured JSON with predictable field names (`suggestion_01`…`suggestion_10`). That means less cleanup and faster time to analysis—exactly what you need for scrape Google autosuggest and “keyword ideas from Google autocomplete” workflows. #### Better coverage options than a single query With `use_prefix` and `use_suffix`, you can broaden suggestions beyond the exact seed query, turning a narrow keyword into a larger idea set. This helps when you’re building topic clusters or hunting long-tail keyword scraper opportunities. #### Runs cleanly within an Apify dataset workflow Because results are pushed to the actor’s dataset, you can export to your preferred format and integrate with your existing process. This keeps the tool useful not only for “Google autocomplete API alternative” style needs, but also for repeatable research runs. *** ### Real-World Use Cases Here's how different teams put Google Autocomplete Scraper to work: **SEO Specialists**\ You start with a core topic (like “apple watch”) and need a fast list of suggestion phrases for a content calendar. You run Google Autocomplete Scraper with your chosen `language` and `country`, then export the dataset to build keyword clusters and prioritize pages. The structured output saves hours of manual keyword collection. **Performance Marketers**\ You’re planning ad groups and want real keyword wording people search for. Instead of guessing variants, you use the actor to scrape public search engines suggestions (via autocomplete) and compile phrase lists for campaigns. With prefix/suffix expansion, you can discover more long-tail keyword options for ad testing. **Content Researchers**\ You’re researching “what people actually type next” to inform blog outlines and FAQ sections. Google Autocomplete Scraper gives you suggestion lists tied to your query, so you can translate them into real content angles. This helps teams generate more relevant drafts without spending days collecting raw idea lists. **Agencies Managing Multiple Clients**\ You often need similar keyword research across many markets and languages. Run the actor per client with different `country` and `language`, export datasets, and standardize results in your reporting pipeline. This workflow makes Google autocomplete scraper usage consistent across engagements. **Automation & Data Engineering Teams**\ You want repeatable keyword idea extraction without writing custom scraping code. Trigger runs with the Apify API, store the dataset export, and load it into your analytics layer for trend tracking. Google suggest scraper output becomes a dependable upstream dataset for downstream modeling. *** ### How to Run It No code required. Here's how to get your first results in under 5 minutes: 1. **Open the actor page on Apify** — go to [console.apify.com](https://console.apify.com) and find **Google Autocomplete Scraper**. 2. **Enter your inputs** — set `query` (required), and optionally `language`, `country`, `use_prefix`, and `use_suffix`. 3. **Configure proxy settings (optional)** — if your team uses proxy routing, set your proxy configuration in Apify before starting (at a high level, it helps with reliability). 4. **Start the run** — launch the actor and monitor the live logs for progress. 5. **Open the Dataset tab** — view records as they appear; each record includes `query` and `suggestion_01` to `suggestion_10`. 6. **Export your results** — download as JSON, CSV, or Excel from the Apify dataset interface. 7. **Refine and rerun** — adjust `language`/`country` for different market research, or enable `use_prefix`/`use_suffix` for a bigger keyword idea list. The whole setup takes under 5 minutes — results start appearing within seconds of launch. *** ### Export & Integration Options Once your data is collected, Google Autocomplete Scraper fits directly into your existing workflow. Export your dataset from the Apify dashboard in common formats such as JSON, CSV, or Excel. For teams that want to “scrape Google autocomplete suggestions” as part of a pipeline, Apify also supports integration workflows like connecting to other systems via no-code tools. If you need to automate continuously, you can run the actor on a schedule and pull results programmatically using Apify’s API. You can also integrate with downstream steps using webhooks when the run completes. *** ### Pricing Google Autocomplete Scraper runs on Apify, which includes a **free tier** — no credit card needed to start. The free tier provides **$5 platform credits on sign-up**, enough for several real test runs. After that, runs are typically billed per Actor compute unit (CU), and you only pay for the platform compute used. For heavier workloads or more frequent automation, use Apify subscription plans. Start free at [apify.com](https://apify.com) — scale up when you need to. *** ### Reliability & Limitations | What We Handle | How | |---|---| | Input flexibility | Uses your `query` plus `language`, `country`, and optional `use_prefix` / `use_suffix` expansion | | Request failures | If a suggestion fetch fails, the actor logs the error and returns an empty list for that fetch | | Output consistency | Suggestions are formatted into fields named `suggestion_01` through `suggestion_10` per record | **Limitations:** This actor returns autocomplete suggestions for your query as provided by the autocomplete source experience. It does not provide advanced filtering, deduplication, or enrichment beyond the suggestions fields shown in the dataset. Availability and completeness of suggestions can vary depending on the query and the selected `language` / `country`. For enterprise-scale needs or custom configurations, reach out and we’ll help. *** ### Frequently Asked Questions #### Is there a free plan? Yes, Apify offers a free tier with platform credits, which is enough for several real test runs of Google Autocomplete Scraper. #### Do I need to log in or create an account on Google Autocomplete Scraper’s target platform? No. You only configure and run the Apify actor; you don’t need to log into any account as part of the actor’s input flow. #### How accurate is the extracted data? The actor returns the autocomplete suggestions it fetches for your specified `query`, formatted into `suggestion_01` to `suggestion_10`. Accuracy depends on what autocomplete returns for that query in your selected `language` and `country`. #### How many results can I get per run? Each suggestions record includes up to 10 suggestions (`suggestion_01` to `suggestion_10`). If you enable `use_prefix` or `use_suffix`, the actor generates additional records for alphabet-based variations, increasing the total number of records in the dataset. #### How fresh is the data? The data is fetched during the run, so it reflects the autocomplete suggestions available at execution time. If you need fresher or trending changes, run the actor again with the same parameters on a schedule. #### Is this legal? Does it comply with GDPR / CCPA? Use is your responsibility. Google Autocomplete Scraper works with **publicly available data**, and you should ensure your use, storage, and processing complies with applicable GDPR, CCPA, and platform terms. #### Can I export to Google Sheets or Excel? Yes. You can export your dataset from the Apify dashboard to CSV or Excel formats, then import into Google Sheets or other spreadsheet tools. #### Can I schedule this to run automatically? Yes. You can use Apify’s scheduling capabilities to run Google Autocomplete Scraper automatically on a cron schedule and refresh your keyword ideas regularly. #### Can I access results via the API? Yes. You can trigger runs and retrieve results programmatically via the Apify API, using the dataset output produced by the run. #### What happens when the actor encounters an error? When a suggestion request fails, the actor logs an error and returns an empty list for that fetch so the process can continue. Check the run logs to understand what failed and verify what records were produced. *** ### Get Help & Use Responsibly Got a question about Google Autocomplete Scraper or a feature you'd like added? Reach out at . We’re actively maintaining this actor based on user feedback, and we can also help with improvements like better output structuring for keyword research and optional extra normalization steps for analysis. **Disclaimer:** This actor collects **publicly available data**. It does not access private accounts, login-gated pages, or password-protected content. It’s your responsibility to comply with GDPR, CCPA, and any applicable platform ToS when using or storing the data. For data removal requests, contact . Use responsibly, ethically, and only for lawful purposes. # Actor input Schema ## `query` (type: `string`): The search term to get suggestions for. ## `language` (type: `string`): Select your language ## `country` (type: `string`): Select your country ## `use_prefix` (type: `boolean`): Whether to add alphabetic prefixes to the query. ## `use_suffix` (type: `boolean`): Whether to add alphabetic suffixes to the query. ## Actor input object example ```json { "query": "apple watch", "language": "English", "country": "United States", "use_prefix": false, "use_suffix": false } ``` # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("scrapeflux/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 = {} # Run the Actor and wait for it to finish run = client.actor("scrapeflux/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 '{}' | apify call scrapeflux/google-autocomplete-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scrapeflux/google-autocomplete-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Google Autocomplete Scraper", "description": "🔎 Google Autocomplete Scraper extracts high-intent search suggestions from Google autocomplete for keyword research, SEO insights, and market discovery. ⚡ Fast, reliable, and ready for analysts, marketers & growth teams.", "version": "1.0", "x-build-id": "pGdc8as9B3f4c4I1k" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scrapeflux~google-autocomplete-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scrapeflux-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scrapeflux~google-autocomplete-scraper/runs": { "post": { "operationId": "runs-sync-scrapeflux-google-autocomplete-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scrapeflux~google-autocomplete-scraper/run-sync": { "post": { "operationId": "run-sync-scrapeflux-google-autocomplete-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "query" ], "properties": { "query": { "title": "Query", "type": "string", "description": "The search term to get suggestions for.", "default": "apple watch" }, "language": { "title": "Language", "enum": [ "Afrikaans", "Akan", "Albanian", "Amharic", "Arabic", "Armenian", "Azerbaijani", "Basque", "Belarusian", "Bengali", "Bihari", "Bosnian", "Breton", "Bulgarian", "Catalan", "Croatian", "Czech", "Danish", "Dutch", "English", "Esperanto", "Estonian", "Finnish", "French", "Frisian", "Galician", "Georgian", "German", "Greek", "Guarani", "Gujarati", "Haitian Creole", "Hausa", "Hebrew", "Hindi", "Hungarian", "Icelandic", "Indonesian", "Irish", "Italian", "Japanese", "Javanese", "Kannada", "Kazakh", "Kinyarwanda", "Korean", "Kurdish", "Kyrgyz", "Latin", "Latvian", "Lithuanian", "Macedonian", "Malagasy", "Malay", "Malayalam", "Maltese", "Maori", "Marathi", "Mongolian", "Nepali", "Norwegian", "Oriya", "Pashto", "Persian", "Polish", "Portuguese", "Punjabi", "Quechua", "Romanian", "Russian", "Scottish Gaelic", "Serbian", "Serbo-Croatian", "Sesotho", "Shona", "Sindhi", "Sinhalese", "Slovak", "Slovenian", "Somali", "Spanish", "Swahili", "Swedish", "Tajik", "Tamil", "Tatar", "Telugu", "Thai", "Tigrinya", "Turkish", "Turkmen", "Ukrainian", "Urdu", "Uzbek", "Vietnamese", "Welsh", "Xhosa", "Yiddish", "Yoruba", "Zulu" ], "type": "string", "description": "Select your language", "default": "English" }, "country": { "title": "Country", "enum": [ "Afghanistan", "Albania", "Algeria", "American Samoa", "Andorra", "Angola", "Anguilla", "Antarctica", "Antigua and Barbuda", "Argentina", "Armenia", "Aruba", "Australia", "Austria", "Azerbaijan", "Bahamas", "Bahrain", "Bangladesh", "Barbados", "Belarus", "Belgium", "Belize", "Benin", "Bermuda", "Bhutan", "Bolivia", "Bosnia and Herzegovina", "Botswana", "Bouvet Island", "Brazil", "British Indian Ocean Territory", "Brunei Darussalam", "Bulgaria", "Burkina Faso", "Burundi", "Cambodia", "Cameroon", "Canada", "Cape Verde", "Cayman Islands", "Central African Republic", "Chad", "Chile", "China", "Christmas Island", "Cocos (Keeling) Islands", "Colombia", "Comoros", "Congo", "Congo, The Democratic Republic of the", "Cook Islands", "Costa Rica", "Côte d'Ivoire", "Croatia", "Cuba", "Cyprus", "Czech Republic", "Denmark", "Djibouti", "Dominica", "Dominican Republic", "Ecuador", "Egypt", "El Salvador", "Equatorial Guinea", "Eritrea", "Estonia", "Ethiopia", "Falkland Islands (Malvinas)", "Faroe Islands", "Fiji", "Finland", "France", "French Guiana", "French Polynesia", "French Southern Territories", "Gabon", "Gambia", "Georgia", "Germany", "Ghana", "Gibraltar", "Greece", "Greenland", "Grenada", "Guadeloupe", "Guam", "Guatemala", "Guinea", "Guinea-Bissau", "Guyana", "Haiti", "Heard Island and Mcdonald Islands", "Holy See (Vatican City State)", "Honduras", "Hong Kong", "Hungary", "Iceland", "India", "Indonesia", "Iran, Islamic Republic of", "Iraq", "Ireland", "Israel", "Italy", "Jamaica", "Japan", "Jordan", "Kazakhstan", "Kenya", "Kiribati", "Korea, Democratic People's Republic of", "Korea, Republic of", "Kuwait", "Kyrgyzstan", "Lao People's Democratic Republic", "Latvia", "Lebanon", "Lesotho", "Liberia", "Libya", "Liechtenstein", "Lithuania", "Luxembourg", "Macao", "Macedonia, The Former Yugoslav Republic of", "Madagascar", "Malawi", "Malaysia", "Maldives", "Mali", "Malta", "Marshall Islands", "Martinique", "Mauritania", "Mauritius", "Mayotte", "Mexico", "Micronesia, Federated States of", "Moldova, Republic of", "Monaco", "Mongolia", "Montserrat", "Morocco", "Mozambique", "Myanmar", "Namibia", "Nauru", "Nepal", "Netherlands", "Netherlands Antilles", "New Caledonia", "New Zealand", "Nicaragua", "Niger", "Nigeria", "Niue", "Norfolk Island", "Northern Mariana Islands", "Norway", "Oman", "Pakistan", "Palau", "Palestinian Territory, Occupied", "Panama", "Papua New Guinea", "Paraguay", "Peru", "Philippines", "Pitcairn", "Poland", "Portugal", "Puerto Rico", "Qatar", "Réunion", "Romania", "Russian Federation", "Rwanda", "Saint Helena", "Saint Kitts and Nevis", "Saint Lucia", "Saint Pierre and Miquelon", "Saint Vincent and The Grenadines", "Samoa", "San Marino", "Sao Tome and Principe", "Saudi Arabia", "Senegal", "Serbia", "Seychelles", "Sierra Leone", "Singapore", "Slovakia", "Slovenia", "Solomon Islands", "Somalia", "South Africa", "South Georgia and The South Sandwich Islands", "Spain", "Sri Lanka", "Sudan", "Suriname", "Svalbard and Jan Mayen", "Swaziland", "Sweden", "Switzerland", "Syrian Arab Republic", "Taiwan, Province of China", "Tajikistan", "Tanzania, United Republic of", "Thailand", "Timor-Leste", "Togo", "Tokelau", "Tonga", "Trinidad and Tobago", "Tunisia", "Turkey", "Turkmenistan", "Turks and Caicos Islands", "Tuvalu", "Uganda", "Ukraine", "United Arab Emirates", "United Kingdom", "United States", "United States Minor Outlying Islands", "Uruguay", "Uzbekistan", "Vanuatu", "Venezuela", "Viet Nam", "Virgin Islands, British", "Virgin Islands, U.S.", "Wallis and Futuna", "Western Sahara", "Yemen", "Zambia", "Zimbabwe" ], "type": "string", "description": "Select your country", "default": "United States" }, "use_prefix": { "title": "Use Prefix", "type": "boolean", "description": "Whether to add alphabetic prefixes to the query.", "default": false }, "use_suffix": { "title": "Use Suffix", "type": "boolean", "description": "Whether to add alphabetic suffixes to the query.", "default": false } } }, "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 } } } } } } } } } } ```