# Car and Classic Scraper (`scrappinglatam/carandclassic-scraper`) Actor Scrape classic cars, motorcycles, and collectibles from CarAndClassic.com. Filter by category, make, and model year. Returns price, mileage, image, and listing type (auction / classified / make-an-offer). - **URL**: https://apify.com/scrappinglatam/carandclassic-scraper.md - **Developed by:** [ScrappingLatam](https://apify.com/scrappinglatam) (community) - **Categories:** E-commerce, AI, Automation - **Stats:** 2 total users, 0 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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 ## Car and Classic Scraper Extract listings from [CarAndClassic.com](https://www.carandclassic.com), the UK-based marketplace with 30,000+ classic cars, motorcycles, 4x4s, and collectibles for sale across auctions, classifieds, and "make an offer" formats. Filter by **category**, **make**, and **model year**. ### What it does Pulls live listings from CarAndClassic and returns a clean, spreadsheet-ready record for every vehicle — price, mileage, image, listing type (auction, classified, or make-an-offer) and a direct link to the ad. You can filter by a single category (Classic Cars, Motorcycles, 4x4s, Camper Vans, etc.), by make, by model-year range, or combine all three. ### Use cases - Price tracking for UK classic-car dealers and insurers - Lead generation for restoration specialists, parts vendors, and transport services - Auction intelligence (auction vs. classified vs. make-an-offer by brand) - Academic or editorial research on UK collector-car valuations - Seed data for comparison tools and marketplace aggregators ### Input | Field | Type | Description | |--------------|---------|---------------------------------------------------------------------------------------------------------| | `make` | Brand — e.g. `bmw`, `porsche`, `jaguar`, `land-rover`, `alfa-romeo`. Leave empty for all. | | `category` | Category (Classic Cars, Motorcycles, American Cars, 4x4s, Camper Vans, etc.). | | `year_min` | Earliest model year. | | `year_max` | Latest model year. | | `max_items` | Hard cap on total rows. | | `max_pages` | Optional limit on how many result pages to scan. | #### Tip: when combining filters Filtering by make alone (or make + year) is the fastest path. Combining a category with a narrow make/year filter can still return data, but takes longer because the Actor has to scan all listings inside that category and filter client-side. ### Output | Field | Description | |--------------------------------|----------------------------------------------------------------------| | `id` | Unique listing identifier | | `listing_type` | `auction` / `classified` / `make-an-offer` | | `url` | Direct link to the listing | | `title` | Listing headline | | `brand` | Make name | | `model_year` | Model year | | `price`, `price_currency` | Asking price (GBP for UK listings; auctions return the current leading bid) | | `mileage_value`, `mileage_unit`| Odometer reading and its unit (miles or km) | | `availability` | In stock / sold / reserved | | `image_url` | Primary listing image | | `category_id`, `category_name` | Populated when scanning by category | | `scraped_at` | UTC timestamp of the scrape | ### Example input ```json { "make": "porsche", "year_min": 1970, "year_max": 1989, "max_items": 50 } ```` ### Notes - Every listing appears at most once per run — duplicates across pages are filtered out automatically. - For active auctions, `price` mirrors the current high bid at the moment of scraping. - Pagination is paced to be polite to the source site. ### Legal This Actor scrapes the public CarAndClassic.com website without authentication. Users are responsible for complying with CarAndClassic's [Terms of Service](https://www.carandclassic.com/terms) and applicable data protection law. # Actor input Schema ## `make` (type: `string`): Brand slug, e.g. bmw, porsche, ferrari, jaguar, mercedes, land-rover, alfa-romeo. Leave empty for all makes. ## `category` (type: `string`): CarAndClassic category. If set, the Actor lists that category and filters make/year client-side. Leave empty to use the unified /search endpoint with server-side make/year filters (recommended for brand-based queries). ## `year_min` (type: `integer`): Earliest model year. Leave empty for no lower bound. ## `year_max` (type: `integer`): Latest model year. Leave empty for no upper bound. ## `max_items` (type: `integer`): Hard cap on total records returned. ~40–50 listings per page. ## `max_pages` (type: `integer`): Optional page-count cap. Leave empty to paginate until max\_items or end-of-results. ## Actor input object example ```json { "make": "", "category": "", "max_items": 50 } ``` # 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("scrappinglatam/carandclassic-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 = {} # Run the Actor and wait for it to finish run = client.actor("scrappinglatam/carandclassic-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 '{}' | apify call scrappinglatam/carandclassic-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scrappinglatam/carandclassic-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Car and Classic Scraper", "description": "Scrape classic cars, motorcycles, and collectibles from CarAndClassic.com. Filter by category, make, and model year. Returns price, mileage, image, and listing type (auction / classified / make-an-offer).", "version": "0.0", "x-build-id": "AKY6ciMQLN8ILhOQj" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scrappinglatam~carandclassic-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scrappinglatam-carandclassic-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/scrappinglatam~carandclassic-scraper/runs": { "post": { "operationId": "runs-sync-scrappinglatam-carandclassic-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/scrappinglatam~carandclassic-scraper/run-sync": { "post": { "operationId": "run-sync-scrappinglatam-carandclassic-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": { "make": { "title": "Make (brand)", "type": "string", "description": "Brand slug, e.g. bmw, porsche, ferrari, jaguar, mercedes, land-rover, alfa-romeo. Leave empty for all makes.", "default": "" }, "category": { "title": "Category", "enum": [ "", "3", "9", "10", "11", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", "27" ], "type": "string", "description": "CarAndClassic category. If set, the Actor lists that category and filters make/year client-side. Leave empty to use the unified /search endpoint with server-side make/year filters (recommended for brand-based queries).", "default": "" }, "year_min": { "title": "Year from", "minimum": 1886, "maximum": 2030, "type": "integer", "description": "Earliest model year. Leave empty for no lower bound." }, "year_max": { "title": "Year to", "minimum": 1886, "maximum": 2030, "type": "integer", "description": "Latest model year. Leave empty for no upper bound." }, "max_items": { "title": "Max results", "minimum": 1, "maximum": 10000, "type": "integer", "description": "Hard cap on total records returned. ~40–50 listings per page.", "default": 50 }, "max_pages": { "title": "Max pages", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Optional page-count cap. Leave empty to paginate until max_items or end-of-results." } } }, "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 } } } } } } } } } } ```