# Automobile Catalog — Luxury Car Taxonomy Scraper (`scrappinglatam/automobile-catalog-taxonomy`) Actor Scrapes make/model/generation/variant/specs taxonomy from automobile-catalog.com for luxury and classic car brands. - **URL**: https://apify.com/scrappinglatam/automobile-catalog-taxonomy.md - **Developed by:** [ScrappingLatam](https://apify.com/scrappinglatam) (community) - **Categories:** E-commerce, Automation - **Stats:** 2 total users, 1 monthly users, 0.0% 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 ## Automobile Catalog — Luxury Car Taxonomy Scraper Scrapes the complete make/model/generation/variant/specs taxonomy from **automobile-catalog.com** for 65 luxury and classic car brands — Porsche, Ferrari, Mercedes-Benz, Lamborghini, Aston Martin, Bentley, Rolls-Royce, McLaren, Bugatti, and more. ### What does it do? Runs a 3-phase pipeline: 1. **Generations** — downloads all model generations per brand (e.g. Porsche 911 3rd gen, 1973–1989). ~1,500 rows. Fast (~5 min). 2. **Variants** — for each generation, downloads all individual variants (e.g. 911 Carrera RS 2.7 Coupe). ~8,000–12,000 rows. Medium (~3–4 h). 3. **Specs** — for each variant, downloads the full technical spec sheet (engine, power, torque, dimensions, weight, etc.). ~8,000–12,000 rows. Slow (~8–10 h). Run each phase separately using `resume_from_kv=true` to chain runs efficiently. ### Output fields | Field | Description | |---|---| | `_type` | `generation` / `variant` / `spec` | | `make_slug` | Brand slug (`porsche`, `mercedes-benz`) | | `model_slug` | Model/generation slug | | `family_slug` | Family slug (without generation suffix) | | `year_from` / `year_to` | Production years | | `car_id` | Stable numeric ID from automobile-catalog.com | | `variant_slug` | Full variant slug | | `spec_url` | Source URL | | `power_hp` / `power_kw` / `power_ps` | Engine power in 3 units | | `torque_nm` | Torque in Nm | | `displacement_cm3` | Engine displacement | | `transmission_type` | `manual` / `automatic` | | `body_style` | `coupe`, `convertible`, `sedan`, etc. | | `fuel_type` | `gasoline`, `diesel`, `electric`, `hybrid` | | `accel_0_100_kmh_s` | 0–100 km/h acceleration | | `top_speed_kmh` | Top speed | | `curb_weight_kg` | Curb weight in kg | ### Example input ```json { "phase": "generations", "makes": ["porsche", "ferrari", "lamborghini"], "max_items": 0, "resume_from_kv": false, "delay_min_s": 2, "delay_max_s": 4 } ```` ### Recommended run sequence ``` Run 1: phase=generations, resume_from_kv=false → ~5 min Run 2: phase=variants, resume_from_kv=true → ~3-4 h Run 3: phase=specs, resume_from_kv=true → ~8-10 h ``` Each run saves progress to the Key-Value store. If a run times out, re-run with `resume_from_kv=true` and it picks up exactly where it stopped. ### Notes - Uses Apify residential proxies to bypass Cloudflare Turnstile - Rate-limited to 2–4 s between requests (configurable) - Automatically retries on 429/503 with exponential backoff - All data is pushed incrementally — no data is lost on timeout # Actor input Schema ## `phase` (type: `string`): Which scraping phase to execute. Run in order: generations → variants → specs. Use 'all' to run everything in one go (slow). ## `makes` (type: `array`): List of make slugs (e.g. \['porsche', 'ferrari', 'mercedes-benz']). Leave empty to use the default luxury list (65 brands). ## `resume_from_kv` (type: `boolean`): If enabled, reads progress from the Key-Value store of the PREVIOUS run (same Actor) and skips already-scraped items. ## `max_items` (type: `integer`): Stop after processing this many items (generations / variants / specs). 0 = no limit. Useful for testing. ## `delay_min_s` (type: `number`): Minimum wait between HTTP requests. Increase if you see 429 errors. ## `delay_max_s` (type: `number`): Maximum wait between HTTP requests. ## Actor input object example ```json { "phase": "generations", "makes": [], "resume_from_kv": false, "max_items": 0, "delay_min_s": 2, "delay_max_s": 4 } ``` # Actor output Schema ## `results` (type: `string`): Dataset containing all scraped listings # 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/automobile-catalog-taxonomy").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/automobile-catalog-taxonomy").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/automobile-catalog-taxonomy --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scrappinglatam/automobile-catalog-taxonomy", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Automobile Catalog — Luxury Car Taxonomy Scraper", "description": "Scrapes make/model/generation/variant/specs taxonomy from automobile-catalog.com for luxury and classic car brands.", "version": "0.1", "x-build-id": "0PprTOotk01jY4gJ6" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scrappinglatam~automobile-catalog-taxonomy/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scrappinglatam-automobile-catalog-taxonomy", "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~automobile-catalog-taxonomy/runs": { "post": { "operationId": "runs-sync-scrappinglatam-automobile-catalog-taxonomy", "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~automobile-catalog-taxonomy/run-sync": { "post": { "operationId": "run-sync-scrappinglatam-automobile-catalog-taxonomy", "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": { "phase": { "title": "Phase to run", "enum": [ "generations", "variants", "specs", "all" ], "type": "string", "description": "Which scraping phase to execute. Run in order: generations → variants → specs. Use 'all' to run everything in one go (slow).", "default": "generations" }, "makes": { "title": "Brands to scrape", "type": "array", "description": "List of make slugs (e.g. ['porsche', 'ferrari', 'mercedes-benz']). Leave empty to use the default luxury list (65 brands).", "default": [], "items": { "type": "string" } }, "resume_from_kv": { "title": "Resume from checkpoint", "type": "boolean", "description": "If enabled, reads progress from the Key-Value store of the PREVIOUS run (same Actor) and skips already-scraped items.", "default": false }, "max_items": { "title": "Max items per phase", "minimum": 0, "type": "integer", "description": "Stop after processing this many items (generations / variants / specs). 0 = no limit. Useful for testing.", "default": 0 }, "delay_min_s": { "title": "Min delay between requests (seconds)", "minimum": 0.5, "type": "number", "description": "Minimum wait between HTTP requests. Increase if you see 429 errors.", "default": 2 }, "delay_max_s": { "title": "Max delay between requests (seconds)", "minimum": 1, "type": "number", "description": "Maximum wait between HTTP requests.", "default": 4 } } }, "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 } } } } } } } } } } ```