# Fragrantica Scraper - Perfume Data Extractor (`plastic_warrior/fragrantica-scraper-1`) Actor 5$ per 1000 listings. Scrape fragrance data from Fragrantica.com - including perfume names, brands, notes, ratings, reviews, images and their clones. Fast and structured data extraction from largest perfume community. - **URL**: https://apify.com/plastic\_warrior/fragrantica-scraper-1.md - **Developed by:** [Faheem Ahmed](https://apify.com/plastic_warrior) (community) - **Categories:** Developer tools, E-commerce, Other - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $5.00 / 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 ## Fragrantica Scraper - Perfume Data Extractor Extract structured perfume data from Fragrantica detail pages. This Apify Actor is built for direct Fragrantica perfume URLs and returns clean JSON records with perfume names, brands, fragrance notes, main accords, ratings, images, launch year, perfumer data, and related perfume links. Use this Fragrantica scraper for perfume catalog building, fragrance market research, competitive analysis, product enrichment, recommendation systems, and structured fragrance data pipelines. --- ### Key Features - Scrapes specific Fragrantica perfume detail URLs - Extracts perfume name, brand, gender, rating, vote count, image, and launch year - Extracts top notes, middle notes, base notes, and main accords - Extracts perfumer or nose information when available - Returns one structured JSON item per input URL - Includes status and error fields for failed URLs - Supports static and browser-backed Scrapling fetchers - Optional proxy URL support for more reliable extraction - Designed for Apify Dataset output and downstream database workflows --- ### What This Actor Scrapes This Actor focuses on Fragrantica perfume detail pages, for example: ```text https://www.fragrantica.com/perfume/Chanel/Bleu-de-Chanel-9099.html ```` It does not crawl search pages, brand pages, pagination, or category listings. Provide the exact perfume URLs you want to extract. *** ### Why Use This Fragrantica Scraper? Fragrantica contains detailed perfume data, fragrance notes, ratings, accords, brand information, and community-driven perfume metadata. This Actor turns those detail pages into structured JSON that can be used for analysis, enrichment, search indexes, internal catalogs, dashboards, or AI and recommendation workflows. The scraper is intentionally direct and predictable: each input URL produces one output record, making it easier to track source URLs, retry failures, and map results back to your own product or research data. *** ### Who Is This Actor For? - Fragrance researchers analyzing perfume notes, accords, and ratings - E-commerce teams enriching perfume product catalogs - Data teams building structured fragrance datasets - Developers building perfume search or recommendation tools - Market researchers comparing brands, launches, and fragrance profiles - Collectors and fragrance communities organizing perfume metadata *** ### Input Schema The Actor expects a JSON input with one or more Fragrantica perfume detail URLs. ```json { "urls": [ "https://www.fragrantica.com/perfume/Chanel/Bleu-de-Chanel-9099.html" ], "maxConcurrency": 2, "requestDelaySecs": 2, "fetcher": "auto", "timeoutSecs": 30, "retries": 2, "proxyUrl": "", "includeRawHtml": false } ``` #### Input Parameters | Field | Type | Required | Description | | --- | --- | --- | --- | | `urls` | Array of strings | Yes | Specific Fragrantica perfume detail URLs to scrape. | | `maxConcurrency` | Number | No | Number of URLs processed at the same time. Default is `2`. | | `requestDelaySecs` | Number | No | Delay before each request, in seconds. Default is `2`. | | `fetcher` | String | No | Fetch mode: `auto`, `static`, `browser`, `dynamic`, or `stealthy`. Default is `auto`. | | `timeoutSecs` | Number | No | Request timeout in seconds. Default is `30`. | | `retries` | Number | No | Retry attempts per URL. Default is `2`. | | `proxyUrl` | String | No | Optional proxy URL passed to Scrapling. | | `includeRawHtml` | Boolean | No | Include raw fetched HTML in output records. Default is `false`. | #### Fetcher Modes | Mode | Description | | --- | --- | | `auto` | Tries static fetching first, then falls back to browser-backed fetching if needed. | | `static` | Uses Scrapling static HTTP fetching. Fastest option when the page is accessible. | | `browser` | Uses a lightweight browser fetch that waits for DOM content instead of full page load. Best fallback for heavy Fragrantica pages. | | `dynamic` | Uses browser-backed fetching for pages that need rendering. | | `stealthy` | Uses Scrapling stealthy browser fetching. Useful when static or dynamic fetching is insufficient. | #### Proxy Note Fragrantica may challenge or block some runtime IPs. If requests fail or return challenge pages, use a reliable proxy through `proxyUrl` and keep concurrency conservative. *** ### Output Schema Each dataset item represents one Fragrantica perfume URL. ```json { "sourceUrl": "https://www.fragrantica.com/perfume/Chanel/Bleu-de-Chanel-9099.html", "canonicalUrl": "https://www.fragrantica.com/perfume/Chanel/Bleu-de-Chanel-9099.html", "scrapedAt": "2026-05-24T11:09:18.203793+00:00", "fetcherUsed": "dynamic", "name": "Bleu de Chanel", "brand": "Chanel", "gender": "men", "imageUrl": "https://fimgs.net/mdimg/perfume-social-cards/en-social-9099.jpeg", "rating": 4.18, "ratingCount": 20607, "year": 2010, "perfumers": ["Jacques Polge"], "mainAccords": [ "citrus", "woody", "fresh spicy", "aromatic", "amber" ], "topNotes": ["Grapefruit", "Lemon", "Mint", "Pink Pepper"], "middleNotes": ["Ginger", "Nutmeg", "Jasmine", "Iso E Super"], "baseNotes": [ "Incense", "Cedar", "Vetiver", "Sandalwood", "Patchouli", "Labdanum", "White Musk" ], "description": "Bleu de Chanel by Chanel is a Woody Aromatic fragrance for men.", "concentration": "Cologne", "seasonVotes": {}, "longevityVotes": {}, "sillageVotes": {}, "genderVotes": {}, "priceValueVotes": {}, "similarPerfumes": [ { "name": "Dior Sauvage", "url": "https://www.fragrantica.com/perfume/Dior/Sauvage-31861.html" } ], "breadcrumbs": [], "status": "ok", "error": null } ``` #### Output Fields | Field | Description | | --- | --- | | `sourceUrl` | Original input URL. | | `canonicalUrl` | Canonical Fragrantica perfume URL when available. | | `scrapedAt` | ISO timestamp for the extraction. | | `fetcherUsed` | Scrapling fetcher that produced the accepted record. | | `name` | Perfume name. | | `brand` | Perfume brand or house. | | `gender` | Gender/category text inferred from the page. | | `imageUrl` | Main image URL. | | `rating` | Fragrantica rating value when available. | | `ratingCount` | Number of rating votes when available. | | `year` | Launch year when available. | | `perfumers` | List of perfumers or noses. | | `mainAccords` | Main fragrance accords as text labels. | | `topNotes` | Top note names. | | `middleNotes` | Middle or heart note names. | | `baseNotes` | Base note names. | | `description` | Main perfume description text. | | `concentration` | Detected concentration text where possible. | | `seasonVotes` | Season vote data when present in fetched markup. | | `longevityVotes` | Longevity vote data when present in fetched markup. | | `sillageVotes` | Sillage vote data when present in fetched markup. | | `genderVotes` | Gender vote data when present in fetched markup. | | `priceValueVotes` | Price value vote data when present in fetched markup. | | `similarPerfumes` | Related perfume links found on the detail page. | | `breadcrumbs` | Breadcrumb labels when available. | | `status` | `ok` or `failed`. | | `error` | Error message for failed URLs. | | `rawHtml` | Raw fetched HTML, only when `includeRawHtml` is `true`. | *** ### Example Use Cases - Build a perfume database from a list of Fragrantica URLs - Enrich fragrance product pages with notes, accords, ratings, and launch data - Compare perfume brands, ratings, perfumers, and fragrance profiles - Create datasets for fragrance recommendation engines - Monitor specific perfumes for rating or metadata changes - Prepare structured fragrance data for analytics, BI, or AI workflows *** ### Running Locally Install dependencies: ```bash pip install -r requirements.txt ``` Run with Apify CLI: ```bash apify run -i input.json ``` Example `input.json`: ```json { "urls": [ "https://www.fragrantica.com/perfume/Chanel/Bleu-de-Chanel-9099.html" ], "fetcher": "auto" } ``` *** ### FAQ #### Does this Actor scrape Fragrantica search results? No. This Actor is for specific Fragrantica perfume detail URLs. It does not crawl search pages or pagination. #### Can I scrape multiple perfumes at once? Yes. Add multiple perfume URLs to the `urls` array. The Actor returns one dataset item per URL. #### Does it extract perfume notes? Yes. It extracts top notes, middle notes, base notes, and main accords where available. #### Does it scrape reviews? No. Review scraping is not part of the current implementation. #### What happens when a URL fails? The Actor still pushes a dataset item with `status: "failed"` and an `error` message. #### Should I use a proxy? Use a proxy if Fragrantica blocks or challenges the runtime IP. Keep concurrency low for better reliability. # Actor input Schema ## `urls` (type: `array`): Specific Fragrantica perfume detail URLs to extract. ## `maxConcurrency` (type: `integer`): Number of URLs processed at the same time. ## `requestDelaySecs` (type: `integer`): Polite delay before each request, in seconds. ## `fetcher` (type: `string`): Auto tries static first, then browser-backed fetchers if the page is challenged or incomplete. ## `timeoutSecs` (type: `integer`): Maximum time to wait for each request before treating it as failed, in seconds. ## `retries` (type: `integer`): Number of retry attempts for each URL before returning a failed result. ## `proxyUrl` (type: `string`): Optional proxy URL passed to Scrapling fetchers, for example http://user:pass@host:port. ## `includeRawHtml` (type: `boolean`): Include fetched HTML in each successful dataset item. Useful for debugging, but increases dataset size. ## Actor input object example ```json { "urls": [ "https://www.fragrantica.com/perfume/Chanel/Bleu-de-Chanel-9099.html" ], "maxConcurrency": 2, "requestDelaySecs": 2, "fetcher": "auto", "timeoutSecs": 30, "retries": 2, "proxyUrl": "", "includeRawHtml": 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 = { "urls": [ "https://www.fragrantica.com/perfume/Chanel/Bleu-de-Chanel-9099.html" ] }; // Run the Actor and wait for it to finish const run = await client.actor("plastic_warrior/fragrantica-scraper-1").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 = { "urls": ["https://www.fragrantica.com/perfume/Chanel/Bleu-de-Chanel-9099.html"] } # Run the Actor and wait for it to finish run = client.actor("plastic_warrior/fragrantica-scraper-1").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 '{ "urls": [ "https://www.fragrantica.com/perfume/Chanel/Bleu-de-Chanel-9099.html" ] }' | apify call plastic_warrior/fragrantica-scraper-1 --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=plastic_warrior/fragrantica-scraper-1", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Fragrantica Scraper - Perfume Data Extractor", "description": "5$ per 1000 listings. Scrape fragrance data from Fragrantica.com - including perfume names, brands, notes, ratings, reviews, images and their clones. Fast and structured data extraction from largest perfume community.", "version": "0.0", "x-build-id": "39azvhlXk6WYvu6mm" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/plastic_warrior~fragrantica-scraper-1/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-plastic_warrior-fragrantica-scraper-1", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/plastic_warrior~fragrantica-scraper-1/runs": { "post": { "operationId": "runs-sync-plastic_warrior-fragrantica-scraper-1", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/plastic_warrior~fragrantica-scraper-1/run-sync": { "post": { "operationId": "run-sync-plastic_warrior-fragrantica-scraper-1", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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" ], "properties": { "urls": { "title": "Fragrantica perfume URLs", "type": "array", "description": "Specific Fragrantica perfume detail URLs to extract.", "items": { "type": "string" } }, "maxConcurrency": { "title": "Max concurrency", "minimum": 1, "maximum": 10, "type": "integer", "description": "Number of URLs processed at the same time.", "default": 2 }, "requestDelaySecs": { "title": "Delay between requests", "minimum": 0, "maximum": 60, "type": "integer", "description": "Polite delay before each request, in seconds.", "default": 2 }, "fetcher": { "title": "Scrapling fetcher", "enum": [ "auto", "static", "browser", "dynamic", "stealthy" ], "type": "string", "description": "Auto tries static first, then browser-backed fetchers if the page is challenged or incomplete.", "default": "auto" }, "timeoutSecs": { "title": "Request timeout", "minimum": 5, "maximum": 180, "type": "integer", "description": "Maximum time to wait for each request before treating it as failed, in seconds.", "default": 30 }, "retries": { "title": "Retries", "minimum": 0, "maximum": 10, "type": "integer", "description": "Number of retry attempts for each URL before returning a failed result.", "default": 2 }, "proxyUrl": { "title": "Proxy URL", "type": "string", "description": "Optional proxy URL passed to Scrapling fetchers, for example http://user:pass@host:port.", "default": "" }, "includeRawHtml": { "title": "Include raw HTML", "type": "boolean", "description": "Include fetched HTML in each successful dataset item. Useful for debugging, but increases dataset size.", "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 } } } } } } } } } } ```