# Amazon Product & Review Research + AI (`lizzyyy2/amazon-product-research-ai`) Actor Feasibility probe build. - **URL**: https://apify.com/lizzyyy2/amazon-product-research-ai.md - **Developed by:** [Lizzy](https://apify.com/lizzyyy2) (community) - **Categories:** E-commerce, AI, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $2.00 / 1,000 results 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 ## Amazon Product Research + AI (Market & Price Finder) Turn an Amazon search into a clean, structured product dataset **plus** an AI market read‑out — built for sellers, dropshippers and product researchers who want to size up a niche fast. For each keyword the actor collects products from Amazon search (across pages) and returns, per product: **ASIN, title, price, list price, rating, review count, image, product URL, Prime flag and whether it's sponsored**. Then the built‑in **AI** analyzes the whole keyword's market: - **Price segments** (budget / mid / premium bands) - **Competition level** (low / medium / high) - **Opportunity gaps** — under‑served angles and weak spots - **Best‑value** and **highest‑rated** picks (by ASIN) - A **recommended price point** to enter the market - A **positioning angle** tailored to *your* product (optional) All price statistics (min / max / average / median, average rating, total reviews) are computed **in code** and handed to the AI — so the AI explains the market without ever inventing numbers. ### Input | Field | Type | Description | |-------|------|-------------| | `keywords` | array (required) | Amazon search terms / categories, one per line. | | `domain` | select | Amazon marketplace (`amazon.com`, `amazon.co.uk`, `amazon.de`, …). Proxy country + currency auto‑matched. | | `maxProductsPerKeyword` | integer | Products to collect per keyword. Default `20`, max `100`. | | `aiAnalysis` | boolean | AI market analysis. Default `true`. Off = raw extraction only (no AI charges). | | `yourProduct` | string | What you sell, so the AI tailors positioning advice. Optional. | | `aiLanguage` | string | Language for AI‑written fields. Default `English`. | | `proxyConfiguration` | object | **Residential proxy is required for Amazon.** | #### Example input ```json { "keywords": ["wireless mouse", "yoga mat"], "domain": "amazon.com", "maxProductsPerKeyword": 30, "aiAnalysis": true, "yourProduct": "ergonomic vertical mouse for designers" } ```` ### Output The dataset holds two kinds of rows (distinguished by `recordType`): **Product rows** (`recordType: "product"`) ```json { "recordType": "product", "keyword": "wireless mouse", "position": 1, "asin": "B001DHECXA", "title": "TECKNET Wireless Mouse, 2.4G USB Computer Mouse...", "url": "https://www.amazon.com/dp/B001DHECXA", "priceText": "$9.99", "price": 9.99, "listPrice": null, "rating": 4.4, "reviewsCount": 49873, "image": "https://m.media-amazon.com/images/...jpg", "sponsored": false, "prime": true, "currency": "USD" } ``` **Market analysis row** (`recordType: "market_analysis"`, one per keyword) ```json { "recordType": "market_analysis", "keyword": "wireless mouse", "productCount": 30, "priceMin": 6.99, "priceMax": 49.99, "priceAvg": 18.4, "priceMedian": 16.99, "avgRating": 4.4, "totalReviews": 812340, "aiAnalysis": { "marketSummary": "...", "priceSegments": ["Budget < $12", "Mid $12–25", "Premium > $25"], "competitionLevel": "high", "opportunityGaps": ["Quiet-click models with long battery", "..."], "bestValueAsin": "B004YAVF8I", "highestRatedAsin": "B087Z5WDJ2", "recommendedPricePoint": "...", "positioningAngle": "..." } } ``` > Tip: filter the dataset by `recordType` to separate the product table from the market summaries. ### How it works & limits - Uses **Apify Residential Proxy** with the country pinned to the marketplace — this keeps the currency correct and the block rate low. - Reads everything from Amazon **search pages** (one page ≈ 20+ products), which keeps cost low. It does not open every product detail page. - Amazon caps organic search around 7 pages (~100+ results); that's the practical ceiling per keyword. - Occasionally a page may be challenged; the actor retries with fresh proxy sessions and moves on. - Respect Amazon's Terms and applicable laws when using the data. # Actor input Schema ## `keywords` (type: `array`): Amazon search terms or product categories, one per line (e.g. "wireless mouse", "yoga mat"). Each keyword is researched separately. ## `domain` (type: `string`): Which Amazon marketplace to search. The proxy country and currency are matched automatically. ## `maxProductsPerKeyword` (type: `integer`): How many products to collect per keyword (across multiple search pages). Higher = more data but more cost. ## `aiAnalysis` (type: `boolean`): Use the built-in AI to analyze each keyword's market: price segments, competition level, opportunity gaps, best-value pick and a recommended price point. Turn off for raw product extraction only (no AI charges). ## `yourProduct` (type: `string`): Describe the product you sell or plan to sell, so the AI tailors the positioning advice (e.g. "ergonomic vertical mouse for designers"). Leave empty for generic seller advice. ## `aiLanguage` (type: `string`): Language for the AI-written fields (market summary, gaps, positioning). ## `proxyConfiguration` (type: `object`): Residential proxy is REQUIRED for Amazon. The country is auto-matched to the marketplace. ## Actor input object example ```json { "keywords": [ "standing desk", "air fryer" ], "domain": "amazon.com", "maxProductsPerKeyword": 20, "aiAnalysis": true, "aiLanguage": "English", "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } } ``` # Actor output Schema ## `results` (type: `string`): Per-product rows (price, rating, reviews, link) plus a per-keyword AI market analysis in the default dataset. # 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 = { "keywords": [ "wireless mouse", "yoga mat" ] }; // Run the Actor and wait for it to finish const run = await client.actor("lizzyyy2/amazon-product-research-ai").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 = { "keywords": [ "wireless mouse", "yoga mat", ] } # Run the Actor and wait for it to finish run = client.actor("lizzyyy2/amazon-product-research-ai").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 '{ "keywords": [ "wireless mouse", "yoga mat" ] }' | apify call lizzyyy2/amazon-product-research-ai --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=lizzyyy2/amazon-product-research-ai", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Amazon Product & Review Research + AI", "description": "Feasibility probe build.", "version": "0.0", "x-build-id": "ofzvXBUhUyJgG8Jwz" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/lizzyyy2~amazon-product-research-ai/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-lizzyyy2-amazon-product-research-ai", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/lizzyyy2~amazon-product-research-ai/runs": { "post": { "operationId": "runs-sync-lizzyyy2-amazon-product-research-ai", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/lizzyyy2~amazon-product-research-ai/run-sync": { "post": { "operationId": "run-sync-lizzyyy2-amazon-product-research-ai", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "keywords" ], "properties": { "keywords": { "title": "Search keywords", "type": "array", "description": "Amazon search terms or product categories, one per line (e.g. \"wireless mouse\", \"yoga mat\"). Each keyword is researched separately.", "items": { "type": "string" } }, "domain": { "title": "Amazon marketplace", "enum": [ "amazon.com", "amazon.co.uk", "amazon.de", "amazon.fr", "amazon.es", "amazon.it", "amazon.nl", "amazon.se", "amazon.pl", "amazon.ca", "amazon.com.mx", "amazon.com.br", "amazon.com.au", "amazon.co.jp", "amazon.in", "amazon.ae", "amazon.sa", "amazon.com.tr", "amazon.sg" ], "type": "string", "description": "Which Amazon marketplace to search. The proxy country and currency are matched automatically.", "default": "amazon.com" }, "maxProductsPerKeyword": { "title": "Max products per keyword", "minimum": 1, "maximum": 100, "type": "integer", "description": "How many products to collect per keyword (across multiple search pages). Higher = more data but more cost.", "default": 20 }, "aiAnalysis": { "title": "Enable AI market analysis", "type": "boolean", "description": "Use the built-in AI to analyze each keyword's market: price segments, competition level, opportunity gaps, best-value pick and a recommended price point. Turn off for raw product extraction only (no AI charges).", "default": true }, "yourProduct": { "title": "Your product (optional)", "type": "string", "description": "Describe the product you sell or plan to sell, so the AI tailors the positioning advice (e.g. \"ergonomic vertical mouse for designers\"). Leave empty for generic seller advice." }, "aiLanguage": { "title": "AI output language", "type": "string", "description": "Language for the AI-written fields (market summary, gaps, positioning).", "default": "English" }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Residential proxy is REQUIRED for Amazon. The country is auto-matched to the marketplace.", "default": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } } } }, "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 } } } } } } } } } } ```