# Fragrantica.com Scraper - Low-costπŸ’²πŸ”₯🌸🧴 (`delectable_incubator/fragrantica-com-scraper-low-cost`) Actor Scrape Fragrantica perfumes 🌸🧴 with a powerful fragrance database scraper. Extract perfume names, brands, gender, release years, review counts, ratings, and product URLs from search results. Ideal for fragrance trend analysis, brand monitoring, market research, and structured perfume datasets πŸ“ŠπŸš€ - **URL**: https://apify.com/delectable\_incubator/fragrantica-com-scraper-low-cost.md - **Developed by:** [Prime Scrape](https://apify.com/delectable_incubator) (community) - **Categories:** E-commerce, Lead generation, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $0.00005 / actor start This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. 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

Fragrantica Search Scraper

--- ## 🌸🧴 Fragrantica Search Scraper | Perfume Data Extractor | Apify Actor ### πŸš€ Extract Fragrantica Perfume Data in Seconds (No Code) The **Fragrantica Search Scraper (Apify Actor)** is a powerful, scalable and SEO-optimized perfume data extraction tool designed to collect structured fragrance listings from Fragrantica search pages. It allows you to **scrape perfume listings in bulk**, extract detailed fragrance metadata, and export it for **market research, fragrance analytics, e-commerce enrichment, AI datasets, and perfume catalog building**. --- ### πŸ”₯ Why This Fragrantica Scraper? βœ” Best Fragrantica perfume scraper on Apify βœ” Supports bulk URL scraping (multi-search mode) βœ” Fast & scalable fragrance data extraction βœ” 100% structured JSON / CSV / Excel output βœ” Ideal for fragrance market intelligence & analytics βœ” Perfect for perfume collectors, bloggers & analysts βœ” No coding required --- ### 🎯 What This Scraper Does (Fragrance Data Extraction) This Apify Actor extracts real-time perfume listings from Fragrantica search results. πŸ“Œ Core Features: - Scrape Fragrantica perfume search results - Brand / designer search support - Keyword fragrance search extraction - Extract structured perfume metadata - Auto pagination (β€œShow more results” handling) - Bulk URL processing - High-speed scraping engine - Clean structured dataset output --- ### ⚑ Input Configuration (Simple & Powerful) πŸ”₯ BULK URL MODE ```` { "urls": \[ "https://www.fragrantica.com/search/?query=oud", "https://www.fragrantica.com/search/?dizajner=Giorgio%20Armani", "https://www.fragrantica.com/search/?query=vanilla" ], "maxitems": 60 } ``` --- ### πŸ“Š Extracted Perfume Data (Structured Output) Field description: - objectId β†’ Unique perfume identifier - name β†’ Perfume name - brand β†’ Brand / designer - gender β†’ Target gender category - year β†’ Release year - reviewCount β†’ Number of reviews - thumbnailUrl β†’ Perfume image - productUrl β†’ Fragrantica product page --- ### πŸ’‘ Use Cases (High Demand SEO Keywords) This Fragrantica scraper is used for: 🌸 Perfume market research πŸ“Š Fragrance trend analysis 🧴 Perfume catalog building 🧠 AI fragrance recommendation datasets πŸ“ˆ Brand performance tracking πŸ›οΈ E-commerce enrichment (perfume stores) πŸ”Ž Competitive fragrance intelligence πŸ“š Fragrance blogging & content creation --- ### πŸš€ Key Features (Apify SEO Optimized) ⚑ Bulk URL scraping support 🌍 Global fragrance database extraction πŸ“Œ Smart pagination handling 🧠 Clean structured output πŸ“Š High-speed scraping engine πŸ” Auto retry & stability system πŸ’Ύ Export-ready datasets βš™οΈ Scalable cloud execution (Apify Actor) --- ### πŸ“€ Output Formats Supported βœ” JSON (API-ready) βœ” CSV (Excel analysis) βœ” Excel XLSX (reports) βœ” XML (data pipelines) βœ” HTML (dashboards) --- ### πŸ“¦ Example Output ``` { "productUrl": "https://www.fragrantica.com/perfume/Giorgio-Armani/Acqua-di-Gio-Parfum-81508.html", "objectId": "81508", "gender": "male", "thumbnailUrl": "https://fimgs.net/mdimg/perfume-thumbs/270x270.81508.jpg", "name": "Acqua di GiΓ² Parfum", "brand": "Giorgio Armani", "year": 2023, "reviewCount": 797 } ```` --- ### πŸ”₯ Why This is the BEST Fragrantica Scraper on Apify? βœ” Optimized for marketplace SEO ranking βœ” High-performance scraping engine βœ” Bulk URL support (rare feature) βœ” Clean structured fragrance dataset βœ” Enterprise-ready scalability βœ” Perfect for AI & data pipelines --- ### πŸ’Έ Pricing This scraper runs on a **pay-per-result pricing model**. You only pay for successfully extracted records. πŸ’³ **Price:** $0.98 / 1,000 results --- ### ❓ FAQ (SEO BOOST SECTION) #### Can I scrape multiple search URLs? Yes β€” bulk URL mode is fully supported. #### Does it support brand searches? Yes β€” designer-based scraping is included. #### Is this scraper fast? Yes β€” optimized for high-speed extraction. #### Can I export data? Yes β€” JSON, CSV, Excel, XML supported. #### Is coding required? No β€” 100% no-code Apify Actor. --- ### ⚠️ Disclaimer This tool is not affiliated with Fragrantica. It is an independent data extraction solution. --- ### 🌸 Fragrance Intelligence Use Cases πŸ’‘ Perfume trend tracking πŸ“Š Market analytics dashboards 🧴 Fragrance recommendation systems πŸ€– AI training datasets πŸ“ˆ Brand monitoring tools πŸ›οΈ E-commerce enrichment pipelines --- ### πŸ“¬ Support ⭐⭐⭐⭐⭐ Leave a 5-star review if you like this scraper πŸ“© Contact for custom scraping solutions or enterprise requests # Actor input Schema ## `urls` (type: `array`): List of Fragrantica search URLs to scrape. Results are parsed directly from the rendered HTML β€” no API interception. Examples: β€’ https://www.fragrantica.com/search/?query=Avon β€’ https://www.fragrantica.com/search/?dizajner=Giorgio%20Armani β€’ https://www.fragrantica.com/search/?query=oud ⚠️ URLs must be from fragrantica.com/search/. The scraper automatically clicks 'Show more results' until maxitems is reached or 4 consecutive clicks return no new results. ## `maxitems` (type: `integer`): Maximum number of perfume products to collect per search URL. ## Actor input object example ```json { "urls": [ "https://www.fragrantica.com/search/?query=Dior", "https://www.fragrantica.com/search/?query=Gucci" ], "maxitems": 60 } ```` # Actor output Schema ## `overview` (type: `string`): No description # 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 = { "urls": [ "https://www.fragrantica.com/search/?query=Dior", "https://www.fragrantica.com/search/?query=Gucci" ], "maxitems": 60 }; // Run the Actor and wait for it to finish const run = await client.actor("delectable_incubator/fragrantica-com-scraper-low-cost").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`πŸ’Ύ 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); }); // πŸ“š Want to learn more πŸ“–? Go to β†’ 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 = { "urls": [ "https://www.fragrantica.com/search/?query=Dior", "https://www.fragrantica.com/search/?query=Gucci", ], "maxitems": 60, } # Run the Actor and wait for it to finish run = client.actor("delectable_incubator/fragrantica-com-scraper-low-cost").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # πŸ“š Want to learn more πŸ“–? Go to β†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "urls": [ "https://www.fragrantica.com/search/?query=Dior", "https://www.fragrantica.com/search/?query=Gucci" ], "maxitems": 60 }' | apify call delectable_incubator/fragrantica-com-scraper-low-cost --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=delectable_incubator/fragrantica-com-scraper-low-cost", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Fragrantica.com Scraper - Low-costπŸ’²πŸ”₯🌸🧴", "description": "Scrape Fragrantica perfumes 🌸🧴 with a powerful fragrance database scraper. Extract perfume names, brands, gender, release years, review counts, ratings, and product URLs from search results. Ideal for fragrance trend analysis, brand monitoring, market research, and structured perfume datasets πŸ“ŠπŸš€", "version": "0.0", "x-build-id": "7ndBmm96tM4et6XF1" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/delectable_incubator~fragrantica-com-scraper-low-cost/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-delectable_incubator-fragrantica-com-scraper-low-cost", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/delectable_incubator~fragrantica-com-scraper-low-cost/runs": { "post": { "operationId": "runs-sync-delectable_incubator-fragrantica-com-scraper-low-cost", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/delectable_incubator~fragrantica-com-scraper-low-cost/run-sync": { "post": { "operationId": "run-sync-delectable_incubator-fragrantica-com-scraper-low-cost", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "urls", "maxitems" ], "properties": { "urls": { "title": "Fragrantica Search URLs 🌸", "type": "array", "description": "List of Fragrantica search URLs to scrape. Results are parsed directly from the rendered HTML β€” no API interception.\n\nExamples:\nβ€’ https://www.fragrantica.com/search/?query=Avon\nβ€’ https://www.fragrantica.com/search/?dizajner=Giorgio%20Armani\nβ€’ https://www.fragrantica.com/search/?query=oud\n\n⚠️ URLs must be from fragrantica.com/search/. The scraper automatically clicks 'Show more results' until maxitems is reached or 4 consecutive clicks return no new results.", "items": { "type": "string" } }, "maxitems": { "title": "Maximum Products per URL πŸ“„", "type": "integer", "description": "Maximum number of perfume products to collect per search URL.", "default": 60 } } }, "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 } } } } } } } } } } ```