# PagesJaunes Business Leads Scraper (`jungle_synthesizer/pagesjaunes-leads-scraper`) Actor Scrape business listings from PagesJaunes.fr (French Yellow Pages) by keyword and location. Extracts business name, address, phone, email, website, opening hours, rating, and geolocation. Supports category-based search across any city, department, or region in France. - **URL**: https://apify.com/jungle\_synthesizer/pagesjaunes-leads-scraper.md - **Developed by:** [BowTiedRaccoon](https://apify.com/jungle_synthesizer) (community) - **Categories:** Lead generation, Business - **Stats:** 2 total users, 1 monthly users, 33.3% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per event 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 ## PagesJaunes Business Leads Scraper Extract business leads from PagesJaunes.fr — France's Yellow Pages directory — with full contact details, location data, and business metadata. Search by keyword and city across the entire French market. ### What you get Each record includes: - Business name, category, and PagesJaunes listing URL - Street address, city, and postal code - Phone number and email address (when listed) - Website URL - Rating and review count - Opening hours (structured by day) - GPS coordinates (latitude / longitude) ### How it works Provide one or more search keywords (e.g., "restaurant", "plombier", "avocat") and one or more French city or department names. The scraper searches each keyword–location pair, collects all matching business cards from the listing pages, then fetches each business's detail page for the full contact profile. Results are deduplicated and capped at your `maxItems` limit. PagesJaunes is protected by Cloudflare Turnstile. This actor uses Bright Data's Web Unlocker to clear the CF challenge server-side — no browser on your end, no CAPTCHA solving, just clean HTML and structured data. ### Input | Field | Type | Default | Description | |-------|------|---------|-------------| | `searchTerms` | string[] | `["restaurant"]` | Business keywords/categories to search (e.g. `"plombier"`, `"dentiste"`, `"boulangerie"`) | | `locations` | string[] | `["Paris"]` | French city names, departments, or regions (e.g. `"Lyon"`, `"75"`, `"Île-de-France"`) | | `maxItems` | integer | `20` | Maximum number of business records to return across all searches | ### Output ```json { "name": "Le Relais de l'Entrecôte", "address": "15 rue Marbeuf", "city": "Paris", "postal_code": "75008", "phone": "01 49 52 07 17", "email": null, "website": "https://www.relaisentrecote.fr", "rating": 4.2, "review_count": 312, "opening_hours": { "http://schema.org/Monday": "12:00 - 14:30", "http://schema.org/Tuesday": "12:00 - 14:30" }, "latitude": 48.8699, "longitude": 2.3039, "category": "Restaurant", "pagesjaunes_url": "https://www.pagesjaunes.fr/pros/...", "search_term": "restaurant", "search_location": "Paris", "scraped_at": "2026-06-24T10:15:00.000Z" } ```` ### Pricing This actor runs on PPE (pay-per-event) pricing. You pay per business record returned, not per run. Powered by Bright Data Web Unlocker — billed at the standard price coefficient for CF-protected French directories. ### Common use cases - Sales prospecting and lead generation for the French market - Building B2B contact lists by industry and region - Market research and competitor analysis across French cities - Local business directory enrichment and verification # Actor input Schema ## `sp_intended_usage` (type: `string`): Please describe how you plan to use the data extracted by this crawler. ## `sp_improvement_suggestions` (type: `string`): Provide any feedback or suggestions for improvements. ## `sp_contact` (type: `string`): Provide your email address so we can get in touch with you. ## `resumeCursor` (type: `string`): Leave empty for a fresh crawl. To CONTINUE a previous run where it stopped — without paying again for records you already received — paste the `resumeCursor` value from that run's Output (the run's OUTPUT key). Resume promptly: the previous run's data expires with your account's retention window (free tier: your ~10 most recent runs). ## `searchTerms` (type: `array`): List of business keywords/categories to search for (e.g. "restaurant", "plombier"). Each term will be searched across all specified locations. ## `locations` (type: `array`): List of locations to search in (city name, department, or region, e.g. "Paris", "Lyon", "Bordeaux", "75"). Each keyword is searched in each location. ## `maxItems` (type: `integer`): Maximum number of business records to return (across all searches). ## Actor input object example ```json { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "searchTerms": [ "restaurant" ], "locations": [ "Paris" ], "maxItems": 20 } ``` # Actor output Schema ## `results` (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 = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "searchTerms": [ "restaurant" ], "locations": [ "Paris" ], "maxItems": 20 }; // Run the Actor and wait for it to finish const run = await client.actor("jungle_synthesizer/pagesjaunes-leads-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 = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "searchTerms": ["restaurant"], "locations": ["Paris"], "maxItems": 20, } # Run the Actor and wait for it to finish run = client.actor("jungle_synthesizer/pagesjaunes-leads-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 '{ "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "searchTerms": [ "restaurant" ], "locations": [ "Paris" ], "maxItems": 20 }' | apify call jungle_synthesizer/pagesjaunes-leads-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jungle_synthesizer/pagesjaunes-leads-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "PagesJaunes Business Leads Scraper", "description": "Scrape business listings from PagesJaunes.fr (French Yellow Pages) by keyword and location. Extracts business name, address, phone, email, website, opening hours, rating, and geolocation. Supports category-based search across any city, department, or region in France.\n", "version": "0.1", "x-build-id": "sqj3tijipqAQ7WeWh" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jungle_synthesizer~pagesjaunes-leads-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jungle_synthesizer-pagesjaunes-leads-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/jungle_synthesizer~pagesjaunes-leads-scraper/runs": { "post": { "operationId": "runs-sync-jungle_synthesizer-pagesjaunes-leads-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/jungle_synthesizer~pagesjaunes-leads-scraper/run-sync": { "post": { "operationId": "run-sync-jungle_synthesizer-pagesjaunes-leads-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": [ "searchTerms", "locations", "maxItems" ], "properties": { "sp_intended_usage": { "title": "What is the intended usage of this data?", "minLength": 1, "type": "string", "description": "Please describe how you plan to use the data extracted by this crawler." }, "sp_improvement_suggestions": { "title": "How can we improve this crawler for you?", "minLength": 1, "type": "string", "description": "Provide any feedback or suggestions for improvements." }, "sp_contact": { "title": "Contact Email", "minLength": 1, "type": "string", "description": "Provide your email address so we can get in touch with you." }, "resumeCursor": { "title": "Resume cursor (continue a previous run)", "type": "string", "description": "Leave empty for a fresh crawl. To CONTINUE a previous run where it stopped — without paying again for records you already received — paste the `resumeCursor` value from that run's Output (the run's OUTPUT key). Resume promptly: the previous run's data expires with your account's retention window (free tier: your ~10 most recent runs)." }, "searchTerms": { "title": "Search Terms (keywords)", "type": "array", "description": "List of business keywords/categories to search for (e.g. \"restaurant\", \"plombier\"). Each term will be searched across all specified locations.\n", "items": { "type": "string" } }, "locations": { "title": "Locations", "type": "array", "description": "List of locations to search in (city name, department, or region, e.g. \"Paris\", \"Lyon\", \"Bordeaux\", \"75\"). Each keyword is searched in each location.\n", "items": { "type": "string" } }, "maxItems": { "title": "Max Items", "type": "integer", "description": "Maximum number of business records to return (across all searches)." } } }, "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 } } } } } } } } } } ```