# La Centrale Scraper (`muhammetakkurtt/lacentrale-scraper`) Actor Data extraction tool for LaCentrale. Scrape used car listings, vehicle specs, market prices, and seller contacts in France. - **URL**: https://apify.com/muhammetakkurtt/lacentrale-scraper.md - **Developed by:** [Muhammet Akkurt](https://apify.com/muhammetakkurtt) (community) - **Categories:** Lead generation, Automation, Developer tools - **Stats:** 3 total users, 2 monthly users, 100.0% runs succeeded, 2 bookmarks - **User rating**: 5.00 out of 5 stars ## Pricing from $2.00 / 1,000 vehicle listings scrapeds This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. 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 ![La Centrale Listings Scraper](https://i.postimg.cc/8CStnYJZ/la-centrale-banner.png) ## La Centrale Scraper: Extract Vehicle Listings from France's Top Marketplace Supercharge your automotive market research and inventory tracking with the most powerful **La Centrale scraper** available on Apify! This robust web scraping tool is designed to efficiently extract detailed used car, motorcycle, and utility vehicle listings directly from **La Centrale** ([lacentrale.fr](https://www.lacentrale.fr/)). Whether you are an automotive professional, market analyst, dealership owner, or an individual buyer looking for the best car deals in France, this reliable **La Centrale data extractor** provides you with structured, real-time automotive datasets in JSON, CSV, or Excel formats. ### πŸš€ Why Use This La Centrale Scraper? - **Comprehensive Automotive Data**: Extract highly detailed vehicle attributes, actual pricing, dealer contact information, and high-resolution images. - **Advanced Search & Filtering**: Target your data extraction by vehicle type (cars, motorcycles, utilities), sorting options, listing age, and photo availability. - **Accurate & Up-to-Date Listings**: Get real-time, high-quality data directly from France's leading used vehicle marketplace. - **Cost-Effective Scalability**: Pay only for the vehicle listings you extract. Built with smart retry mechanisms and efficient pagination for high-volume jobs. - **Reliable Data Pipeline**: Built-in automatic proxy integration, and state persistence to ensure your scraping jobs never fail mid-way. ### πŸ“Š Scraper Use Cases & Applications #### 1. Market Research & Automotive Pricing Strategy Analyze the current market prices for specific car makes, models, versions, and years. Track how vehicle prices compare and understand French automotive market trends across different regions. #### 2. Used Car Inventory Alert System Find newly listed vehicles faster. Configure the scraper to extract listings posted within the last 24 hours to spot the best automotive deals and keystone prices before anyone else. #### 3. Dealership Competitor Analysis Keep track of rival dealerships' inventory. Discover what vehicles they are selling, monitor their pricing strategies, and see how quickly their listings rotate. #### 4. Lead Generation for B2B & Sales Capture contact information (phone numbers, dealership names) for sellers or dealerships to reach out directly with purchase offers, B2B software pitches, or partnership opportunities. ### πŸ” What Data Does It Extract? Our La Centrale scraping tool extracts vast amounts of details for every single listing: - **Vehicle Specifications**: Make, model, exact version, mileage, gearbox, color, fuel type (energy), and manufacturing year. - **Pricing Metrics**: Current price, "Good Deal" verification badges, and limit dates for price drops. - **Extensive Media**: Multiple high-resolution photo URLs and 360-degree image flags. - **Dealer/Seller Info**: Contact phones (`displayPhone1`, `displayPhone2`), normalized locations, seller type (PRO/Private). #### Flexible Scraper Inputs: - **Vehicle Types**: `carsAndUtilities`, `utilities`, `motorcycles`. - **Sorting**: `newestListings`, `oldestListings`, `newestModelYear`, `oldestModelYear`, `lowestMileage`, `lowestPrice`, `highestPrice`. - **Other Criteria**: Filter by listings posted `lessThan24Hours` or `lessThan7Days`. - **Only With Photos**: Exclude listings without pictures to ensure high data quality. ### πŸ“ How to Scrape La Centrale (Usage) #### Input Parameters Configure the Apify actor using the following input parameters: - **sortPreset**: Determines the order of the results (default: `newestListings`). - **vehicleType**: Select the category of vehicles to scrape (default: `carsAndUtilities`). - **otherCriteria**: Additional time-based filters (e.g., `lessThan24Hours`). Leave empty for no filter. - **onlyWithPhotos**: Set to `true` to only get listings that have at least one photo (default: `false`). - **yearMin / yearMax**: Optional minimum and maximum vehicle year filters. - **mileageMin / mileageMax**: Optional mileage range filters in kilometers. - **priceMin / priceMax**: Optional price range filters in euros. - **limit**: Maximum number of listings to collect (default: `25`). #### Example Scraper Input ```json { "sortPreset": "lowestPrice", "vehicleType": "carsAndUtilities", "otherCriteria": "lessThan24Hours", "onlyWithPhotos": true, "yearMin": 2000, "yearMax": 2025, "mileageMin": 10, "mileageMax": 100000, "priceMin": 10, "priceMax": 100000000, "limit": 100 } ```` ### πŸ“Š Output Data Format The scraped vehicle listings are securely saved to the Apify dataset. You can download the results in your preferred format (JSON, CSV, Excel, XML). Each item represents a highly detailed vehicle listing natively extracted from La Centrale, complete with contact information. ### Example Output JSON ```json { "picturesPhotosphere": false, "customerFamilyCode": "INTERMEDIAIRE", "goodDealBadge": "VERY_GOOD_DEAL", "manufacturerWarrantyDuration": 3, "publicationOptions": [ "CALLTRACKING", "PHOTO_360", "PHOTO_LISTING", "PRIORISATION", "PHOTO_WEB", "WEB_IMMEDIAT", "DELIVERY", "STRENGTHS", "MULTI_PHOTOS" ], "vehicle": { "externalColor": "gris", "year": 2015, "detailedModel": "CITROEN C3 II phase 2", "version": "II (2) 1.6 E-HDI 90 EXCLUSIVE", "doors": 5, "trimLevel": "EXCLUSIVE", "model": "C3 PICASSO", "gearbox": "MANUAL", "category": "CITADINE", "family": "AUTO", "make": "CITROEN", "motorization": "1.6 E-HDI 92", "energy": "DIESEL", "mileage": 149500, "commercialName": "C3 (2E GENERATION)" }, "priceDropLimitDate": "2026-03-21T12:14:32Z", "reference": "E118240021", "picturesCount": 50, "customerType": "PRO", "ownerCorrelationId": "C056306", "price": 5490, "customerReference": "C056306", "lastUpdate": 1773557429, "picturesCountDate": 1771242886, "location": { "country": "FR", "visitPlace": "91" }, "contacts": { "nomPublie": "TRANSAKAUTO WISSOUS", "displayPhone1": "123456789", "displayPhone2": "987654321" }, "firstOnlineDate": "2026-02-14", "autoviza": true, "pictures360Exterieur": false, "score": 625.5447, "photoUrl": "https://pictures.lacentrale.fr/classifieds/E118240021_STANDARD_0.jpg?format=webp&size=352x264&watermark=lc&signature=1c2039874873bf1455543ea2b9a772389285cfa3418d906120f4c381", "photoUrlMobile": [ "https://pictures.lacentrale.fr/classifieds/E118240021_STANDARD_0.jpg?format=webp&size=285x214&watermark=lc&signature=639071adacd9528c215e8872ccb22969a1a7e6ac2b3a853bb4d2d935", "https://pictures.lacentrale.fr/classifieds/E118240021_STANDARD_1.jpg?format=webp&size=140x105&watermark=lc&signature=a7aec3d26decefda5cf888170c580e86bd6bcca67f2d779a39eddf4d", "https://pictures.lacentrale.fr/classifieds/E118240021_STANDARD_2.jpg?format=webp&size=140x105&watermark=lc&signature=a7aec3d26decefda5cf888170c580e86bd6bcca67f2d779a39eddf4d" ], "isNew": false } ``` This example output shows the structure of a single vehicle listing. The actual output will be a list of similar objects for all scraped listings. ### πŸ’‘ Recommended Scraping Strategies #### Fast Deal Snipping (Kelepir AraΓ§ Yakalama) For the best results in finding underpriced vehicles quickly: - Set `sortPreset` to `newestListings` and `otherCriteria` to `lessThan24Hours`. - Depending on the target category, filtering by `lowestPrice` on specific brands might also yield fast flip opportunities. #### High-Volume Automotive Extraction If you want to pull a large volume of historical or aggregated market data: - Increase the `limit` parameter to cover a wider breadth of the database. - Utilize State Persistence: The scraper automatically saves its progress. If a large run is interrupted, it will resume exactly where it left off, avoiding duplicate data processing. #### API Integration & Automation Integrate the scraper into your existing B2B systems or custom dashboards: - Set up Apify Webhooks to push new listings directly to your CRM, database, or Slack/Discord alerting channels. - Schedule the scraper to run automatically (e.g., hourly) using Apify's scheduling rules to continuously keep tabs on fresh listings. # Actor input Schema ## `vehicleType` (type: `string`): Choose which vehicle category to collect. ## `sortPreset` (type: `string`): Choose how listings should be ordered before pagination starts. ## `otherCriteria` (type: `string`): Optional additional time filter. ## `onlyWithPhotos` (type: `boolean`): When this feature is enabled, only listings that include photos are collected. ## `yearMin` (type: `integer`): Optional minimum vehicle year. ## `yearMax` (type: `integer`): Optional maximum vehicle year. ## `mileageMin` (type: `integer`): Optional minimum mileage in kilometers. ## `mileageMax` (type: `integer`): Optional maximum mileage in kilometers. ## `priceMin` (type: `integer`): Optional minimum price in euros. ## `priceMax` (type: `integer`): Optional maximum price in euros. ## `limit` (type: `integer`): Maximum number of listing items to push to the dataset across all fetched pages. ## Actor input object example ```json { "vehicleType": "carsAndUtilities", "sortPreset": "newestListings", "otherCriteria": "", "onlyWithPhotos": false, "limit": 20 } ``` # Actor output Schema ## `listings` (type: `string`): Dataset containing the collected listing items. # 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("muhammetakkurtt/lacentrale-scraper").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 = {} # Run the Actor and wait for it to finish run = client.actor("muhammetakkurtt/lacentrale-scraper").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 '{}' | apify call muhammetakkurtt/lacentrale-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=muhammetakkurtt/lacentrale-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "La Centrale Scraper", "description": "Data extraction tool for LaCentrale. Scrape used car listings, vehicle specs, market prices, and seller contacts in France.", "version": "0.0", "x-build-id": "jhwIHCozMSEsU1ZeL" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/muhammetakkurtt~lacentrale-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-muhammetakkurtt-lacentrale-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/muhammetakkurtt~lacentrale-scraper/runs": { "post": { "operationId": "runs-sync-muhammetakkurtt-lacentrale-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/muhammetakkurtt~lacentrale-scraper/run-sync": { "post": { "operationId": "run-sync-muhammetakkurtt-lacentrale-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": { "vehicleType": { "title": "Vehicle type", "enum": [ "carsAndUtilities", "utilities", "motorcycles" ], "type": "string", "description": "Choose which vehicle category to collect.", "default": "carsAndUtilities" }, "sortPreset": { "title": "Sort preset", "enum": [ "newestListings", "oldestListings", "newestModelYear", "oldestModelYear", "lowestMileage", "lowestPrice", "highestPrice" ], "type": "string", "description": "Choose how listings should be ordered before pagination starts.", "default": "newestListings" }, "otherCriteria": { "title": "Other criteria", "enum": [ "", "lessThan24Hours", "lessThan7Days" ], "type": "string", "description": "Optional additional time filter.", "default": "" }, "onlyWithPhotos": { "title": "Only listings with photos", "type": "boolean", "description": "When this feature is enabled, only listings that include photos are collected.", "default": false }, "yearMin": { "title": "Minimum year", "minimum": 1900, "maximum": 2026, "type": "integer", "description": "Optional minimum vehicle year." }, "yearMax": { "title": "Maximum year", "minimum": 1900, "maximum": 2026, "type": "integer", "description": "Optional maximum vehicle year." }, "mileageMin": { "title": "Minimum mileage", "minimum": 0, "type": "integer", "description": "Optional minimum mileage in kilometers." }, "mileageMax": { "title": "Maximum mileage", "minimum": 1, "type": "integer", "description": "Optional maximum mileage in kilometers." }, "priceMin": { "title": "Minimum price", "minimum": 0, "type": "integer", "description": "Optional minimum price in euros." }, "priceMax": { "title": "Maximum price", "minimum": 1, "type": "integer", "description": "Optional maximum price in euros." }, "limit": { "title": "Maximum listings", "minimum": 1, "type": "integer", "description": "Maximum number of listing items to push to the dataset across all fetched pages.", "default": 20 } } }, "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 } } } } } } } } } } ```