# Amazon Search Autocomplete Api (`scrapers-hub/amazon-search-autocomplete-api`) Actor 🔎 Amazon Search Autocomplete API fetches real-time autocomplete suggestions to power smarter search, SEO, and product discovery. 🚀 Improve rankings, boost conversions, and enhance UX with fast, reliable query insights. - **URL**: https://apify.com/scrapers-hub/amazon-search-autocomplete-api.md - **Developed by:** [Scrapers Hub](https://apify.com/scrapers-hub) (community) - **Categories:** SEO tools, E-commerce, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $0.01 / 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 ### Amazon Autocomplete Scraper 🔍 **Amazon Autocomplete Scraper** is a focused tool that scrapes **Amazon autocomplete suggestions** for a given query—helping you quickly uncover keyword ideas for Amazon keyword research and search autocomplete keyword extraction at scale. Whether you're a marketer, researcher, or data analyst, this actor supports the Amazon search autocomplete scraper workflow by turning one starting phrase into many related suggestions. It’s a great option for anyone looking to scrape Amazon autocomplete suggestions efficiently, saving you hours of manual work. --- ### Why choose Amazon Autocomplete Scraper? | Feature | Benefit | |---|---| | ✅ **Autocomplete keyword scraping for a given query** | Quickly get Amazon autocomplete keyword results for your seed term | | ✅ **Configurable result size (`max_results`)** | Control how many suggestions you collect per query | | ✅ **Optional prefix/suffix expansion (`use_prefix`, `use_suffix`)** | Generate broader coverage for an Amazon keyword suggestion scraper workflow | | ✅ **Built-in proxy support for reliable scraping** | Improves consistency when scraping public web data | | ✅ **Structured dataset output** | Produces clean JSON records you can analyze or export | | ✅ **Scales across many generated queries in one run** | Save time by collecting suggestions for the whole expanded query set | --- ### Key features - 🔎 **Autocomplete suggestions extraction**: Collects suggestion values for each query you provide - 🎛️ **Flexible controls for coverage**: Use `use_prefix` and/or `use_suffix` to expand your search space - 📊 **Result limiting with `max_results`**: Returns up to the number of suggestions you request per query - 🧩 **Query-aware records**: Each dataset record includes the `query` and a numbered set of suggestions (`suggestion_01`, `suggestion_02`, …) - 🛡️ **Resilience for real-world scraping**: Includes robustness patterns so runs complete reliably over time - 💾 **Easy-to-export JSON dataset**: Outputs directly into the default dataset in a consistent structure - 🤖 **Automation-ready**: Designed for repeatable runs—ideal for building an Amazon autocomplete data scraper pipeline --- ### Input Provide input via an `input.json` file. Example structure: ```json { "query": "apple watch", "max_results": 10, "use_prefix": false, "use_suffix": false } ```` #### Input Fields | Field | Required | Description | |---|---|---| | query | Yes | The search term to get suggestions for (your starting point for scraping Amazon autocomplete suggestions). | | max\_results | No | The maximum number of suggestions to return for each query. | | use\_prefix | No | Whether to add alphabetic prefixes to the query to expand suggestion coverage. | | use\_suffix | No | Whether to add alphabetic suffixes to the query to expand suggestion coverage. | *** ### Output The actor saves the collected results into the default dataset as a JSON array. ```json [ { "query": "apple watch", "suggestion_01": "apple watch series 10", "suggestion_02": "apple watch deals", "suggestion_03": "apple watch bands", "suggestion_04": "apple watch waterproof", "suggestion_05": "apple watch apple care" } ] ``` #### Output Fields | Field | Type | Description | |---|---|---| | query | string | The query term used to fetch autocomplete suggestions (original and any generated prefix/suffix queries). | | suggestion\_01 | string | The first suggestion value returned for that query. | | suggestion\_02 | string | The second suggestion value returned for that query. | | suggestion\_03 | string | The third suggestion value returned for that query. | | suggestion\_04 | string | The fourth suggestion value returned for that query. | | suggestion\_05 | string | The fifth suggestion value returned for that query. | | suggestion\_06 | string | The sixth suggestion value returned for that query (present when requested by `max_results`). | | suggestion\_07 | string | The seventh suggestion value returned for that query (present when requested by `max_results`). | | suggestion\_08 | string | The eighth suggestion value returned for that query (present when requested by `max_results`). | | suggestion\_09 | string | The ninth suggestion value returned for that query (present when requested by `max_results`). | | suggestion\_10 | string | The tenth suggestion value returned for that query (present when requested by `max_results`). | | error\_message | ❌ | Not returned by this actor as no failure object fields are pushed to the dataset. | > Note: The dataset records include `suggestion_01` through `suggestion_XX` where `XX` depends on `max_results`. *** ### How to use Amazon Autocomplete Scraper (via Apify Console) 1. **Open Apify Console**\ Log in at [console.apify.com](https://console.apify.com) and open the **Actors** section. 2. **Find Amazon Autocomplete Scraper**\ Search for **Amazon Autocomplete Scraper** and open the actor page. 3. **Go to the INPUT panel**\ Use the built-in form to set your starting `query` (required). 4. **Set your collection limits and expansion options**\ Enter `max_results` (default is 10). If you want broader coverage like an Amazon autocomplete keyword research tool workflow, enable `use_prefix` and/or `use_suffix`. 5. **(Recommended) Use proxy support**\ Enable the actor’s proxy configuration as needed to keep scraping stable when pulling from public web data. 6. **Run the actor**\ Click **Run**. Watch the logs to confirm that suggestions are being fetched and records are being written. 7. **Open the OUTPUT dataset**\ After completion, open the default dataset to view the JSON records and numbered suggestions. 8. **Export for analysis**\ Export the results to your preferred format (commonly JSON/CSV) and plug them into your keyword research and Amazon autocomplete scraping software workflow. No coding required—get actionable Amazon autocomplete keyword data in minutes. *** ### Advanced features & SEO optimization - 🚀 **Engineered for Amazon autocomplete keyword research tool workflows**: The actor is specifically built for scraping Amazon autocomplete suggestions, turning one seed query into structured, analyzable output. - 🔤 **Broad coverage with prefix and suffix expansion**: Use `use_prefix` and `use_suffix` to expand suggestion extraction for an Amazon autosuggest keyword extractor approach. - 🧾 **Consistent numbered fields for easy import**: Suggestions are stored as `suggestion_01`, `suggestion_02`, etc., making it simple to map into spreadsheets or databases. - 🧠 **Log-friendly visibility during runs**: The actor logs suggestion sets for each query, helping you validate results quickly. *** ### Best use cases - 📈 **SEO and PPC researchers**: Generate fresh Amazon keyword ideas by scraping Amazon search autocomplete suggestions from a single starting term. - 🛒 **Amazon sellers**: Build lists of relevant product and category keywords using an Amazon autosuggest scraper approach. - 🧠 **Content strategists**: Expand topic coverage by finding related search phrases (great for an Amazon autocomplete keyword scraper workflow). - 📊 **Data analysts**: Load structured suggestions into your pipeline to compare keyword variations across runs. - 🧰 **Keyword tooling teams**: Automate enrichment as part of an Amazon autocomplete data scraper pipeline feeding reporting dashboards. - 💡 **Growth marketers**: Rapidly test keyword direction by collecting suggestions for multiple query variations in one run. *** ### Technical specifications - **Supported Input Formats** - ✅ JSON input with: `query`, `max_results`, `use_prefix`, `use_suffix` - **Proxy Support** - ✅ Built-in proxy support for reliable scraping of publicly available data - **Retry Mechanism** - ✅ Includes resilience patterns for scraping stability (details handled internally) - **Dataset Structure** - ✅ Default dataset with JSON records containing `query` and `suggestion_01` … `suggestion_XX` - **Rate Limits & Performance** - ✅ Performance depends on network conditions and the size of the generated query set - **Limitations** - ❌ Only suggestions for the provided `query` (and optional generated prefix/suffix variations) are returned - ❌ No dataset field for `error_message` is pushed by this actor *** ### FAQ #### What does Amazon Autocomplete Scraper return? ✅ It returns a JSON dataset where each record includes your `query` plus numbered suggestion fields like `suggestion_01`, `suggestion_02`, and so on (up to `max_results`). This is what you use as an Amazon autocomplete keyword extractor output. #### Do I need to provide multiple queries? ❌ No. You provide a single required `query`, and optionally enable `use_prefix` and/or `use_suffix` to generate additional queries automatically. #### How do I control how many suggestions I get? You set `max_results`. The actor uses it to limit how many suggestion values it stores per query record. #### Can I use this for Amazon keyword suggestion scraper workflows? ✅ Yes. This actor is designed to scrape Amazon autocomplete suggestions and output them in a structured way that’s easy to incorporate into keyword research and keyword expansion processes. #### Can I integrate it into a larger automation pipeline? ✅ Yes. Because the output is a consistent JSON dataset, it’s straightforward to integrate into reporting, database imports, and other automation steps used in an Amazon autocomplete API workflow. #### Do I need special authentication or login? ❌ This actor is intended to scrape suggestions from publicly available sources and does not require you to provide credentials via the input schema. #### Is this compliant for legal and policy use? ✅ The actor only works with publicly accessible data. Still, you should ensure your usage complies with applicable laws (for example GDPR/CCPA), platform terms, and your internal compliance requirements. #### What if a query returns fewer suggestions? The actor will store whatever suggestion values it receives up to your requested `max_results`. If none are returned for a query, the suggestion fields will be absent for that record. *** ### Support & feature requests If you’re using **Amazon Autocomplete Scraper** and want enhancements or run into issues, share feedback—your ideas help improve the actor. - 💡 **Feature Requests**: Consider upgrades like CSV-friendly output formatting, support for additional expansion strategies, or more granular control over how results are structured for tools that need an Amazon autocomplete scraping script workflow. - 📧 **Contact**: Reach out at for support and feature requests. Thanks in advance—feature requests and real-world feedback shape the roadmap. *** ### Closing CTA / Final thoughts *If you’re looking for an SEO-optimized Amazon Autocomplete Scraper that turns autocomplete research into clean, export-ready data, this is the fastest path from seed keyword to structured suggestions.* *** ### Disclaimer **This tool only accesses publicly accessible sources.** It does not access private profiles, authenticated data, or password-protected content. You are responsible for ensuring your use complies with applicable laws (including GDPR/CCPA where relevant), spam regulations, and the platforms’ terms of service. For any data-removal requests, contact . Use **Amazon Autocomplete Scraper** responsibly, ethically, and for legitimate purposes only. # Actor input Schema ## `query` (type: `string`): The search term to get suggestions for. ## `max_results` (type: `integer`): The maximum number of suggestions to return for each query. ## `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", "max_results": 10, "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("scrapers-hub/amazon-search-autocomplete-api").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("scrapers-hub/amazon-search-autocomplete-api").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 scrapers-hub/amazon-search-autocomplete-api --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scrapers-hub/amazon-search-autocomplete-api", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Amazon Search Autocomplete Api", "description": "🔎 Amazon Search Autocomplete API fetches real-time autocomplete suggestions to power smarter search, SEO, and product discovery. 🚀 Improve rankings, boost conversions, and enhance UX with fast, reliable query insights.", "version": "1.0", "x-build-id": "60loDSH59et4rAmH9" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scrapers-hub~amazon-search-autocomplete-api/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scrapers-hub-amazon-search-autocomplete-api", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scrapers-hub~amazon-search-autocomplete-api/runs": { "post": { "operationId": "runs-sync-scrapers-hub-amazon-search-autocomplete-api", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scrapers-hub~amazon-search-autocomplete-api/run-sync": { "post": { "operationId": "run-sync-scrapers-hub-amazon-search-autocomplete-api", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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" }, "max_results": { "title": "Max Results", "type": "integer", "description": "The maximum number of suggestions to return for each query.", "default": 10 }, "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 } } } } } } } } } } ```