# Cleveland Museum of Art Scraper (`ninhothedev/cleveland-art-scraper`) Actor $0.5/1K 🔥 Fast Cleveland Museum of Art scraper! Open-access artworks — title, artist, date, medium & image. No key. JSON, CSV, Excel or API in seconds. Pull thousands for art datasets, education & ML training ⚡ - **URL**: https://apify.com/ninhothedev/cleveland-art-scraper.md - **Developed by:** [ninhothedev](https://apify.com/ninhothedev) (community) - **Categories:** Developer tools, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $0.50 / 1,000 results 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 ## Cleveland Museum of Art Scraper Scrape open-access artwork data from the **Cleveland Museum of Art (CMA)** — one of the largest free, public-domain art collections in the world. Get structured records with **title, artist, date, medium, department, culture, description and high-resolution image URLs**. No API key, no login, no browser required. Built on the official CMA Open Access API, this Actor is **datacenter-proxy friendly** and cheap to run — ideal for building art datasets, education tools, machine-learning training sets and cultural research at scale. ### Features - 🎨 **Rich artwork metadata** — title, artist/creators, creation date, technique, department, culture, measurements, description and fun facts. - 🖼️ **High-resolution image URLs** — direct links to CMA's open-access image CDN. - 🔎 **Two modes** — `search` (scrape specific queries like an artist or keyword) or `browse` (paginate the entire collection). - 🔑 **No API key** — uses the public CMA Open Access API. - ⚡ **Fast & cheap** — clean JSON API, datacenter-proxy friendly. - 🧱 **Permissive schema** — every record is safe to ingest downstream; missing fields are `null`. ### Pricing This Actor is intentionally cheap: roughly **$0.5 per 1,000 artworks** depending on run size. You only pay for the Apify platform usage it consumes. ### Input | Field | Type | Description | |-------|------|-------------| | `mode` | select | `search` scrapes your queries; `browse` paginates the whole open-access collection. Default `search`. | | `queries` | array | Search terms (used in `search` mode). Prefilled with `["monet", "japanese"]`. | | `hasImage` | boolean | Only return artworks that have an image. Default `true`. | | `maxItems` | integer | Max artworks to scrape in total. Default `100`, hard cap `1000`. | #### Example input ```json { "mode": "search", "queries": ["monet", "japanese", "sculpture"], "hasImage": true, "maxItems": 200 } ```` ### Output Each dataset item is one artwork: ```json { "id": 135382, "accession_number": "1958.39", "title": "The Red Kerchief", "artist": "Claude Monet (French, 1840–1926)", "creators": ["Claude Monet (French, 1840–1926)"], "date": "c. 1868–73", "sortable_date": 1868, "type": "Painting", "technique": "oil on fabric", "department": "Modern European Painting and Sculpture", "culture": ["France, 19th century"], "measurements": "Framed: 128.3 x 105.7 x 14.6 cm", "description": "The Red Kerchief, c. 1868–73. Claude Monet (French, 1840–1926)...", "fun_fact": null, "image_url": "https://openaccess-cdn.clevelandart.org/1958.39/1958.39_web.jpg", "url": "https://clevelandart.org/art/1958.39", "source": "cleveland_art", "scraped_at": "2026-07-20T12:00:00+00:00" } ``` ### Use cases - **Art datasets** — assemble curated, license-clear collections for apps and galleries. - **Education** — power classroom tools, quizzes and interactive art-history lessons. - **ML training** — build image + caption datasets for computer-vision and multimodal models using public-domain works. - **Cultural research** — analyze collection trends across departments, cultures and eras. ### Related Actors - [Art Institute of Chicago Scraper](https://apify.com/ninhothedev/art-institute-chicago-scraper) - [Met Museum Scraper](https://apify.com/ninhothedev/met-museum-scraper) - [Openverse Media Scraper](https://apify.com/ninhothedev/openverse-media-scraper) - [Wikimedia Commons Scraper](https://apify.com/ninhothedev/wikimedia-commons-scraper) ### Keywords cleveland museum of art, CMA, open access art, public domain artwork, art API scraper, museum data, art dataset, artwork metadata, art images, art history, ML training data, cultural heritage, fine art scraper, painting scraper, open access museum. ### Notes Data is served by the Cleveland Museum of Art Open Access API. Many works are released under **CC0** (public domain); always confirm the individual license/rights before commercial reuse. # Actor input Schema ## `mode` (type: `string`): How to collect artworks. 'search' scrapes each of your queries; 'browse' paginates the entire open-access collection. ## `queries` (type: `array`): Search terms to look up (used only in 'search' mode). Each term is scraped separately, e.g. artist, culture, medium or keyword. ## `hasImage` (type: `boolean`): When enabled, only artworks that have a downloadable image are returned. ## `maxItems` (type: `integer`): Maximum number of artworks to scrape in total across all queries (hard cap 1000). ## Actor input object example ```json { "mode": "search", "queries": [ "monet", "japanese" ], "hasImage": true, "maxItems": 100 } ``` # 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 = { "queries": [ "monet", "japanese" ], "maxItems": 100 }; // Run the Actor and wait for it to finish const run = await client.actor("ninhothedev/cleveland-art-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 = { "queries": [ "monet", "japanese", ], "maxItems": 100, } # Run the Actor and wait for it to finish run = client.actor("ninhothedev/cleveland-art-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 '{ "queries": [ "monet", "japanese" ], "maxItems": 100 }' | apify call ninhothedev/cleveland-art-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=ninhothedev/cleveland-art-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Cleveland Museum of Art Scraper", "description": "$0.5/1K 🔥 Fast Cleveland Museum of Art scraper! Open-access artworks — title, artist, date, medium & image. No key. JSON, CSV, Excel or API in seconds. Pull thousands for art datasets, education & ML training ⚡", "version": "0.1", "x-build-id": "mcBc20OFu4XS9bobW" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/ninhothedev~cleveland-art-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-ninhothedev-cleveland-art-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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/ninhothedev~cleveland-art-scraper/runs": { "post": { "operationId": "runs-sync-ninhothedev-cleveland-art-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/ninhothedev~cleveland-art-scraper/run-sync": { "post": { "operationId": "run-sync-ninhothedev-cleveland-art-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": { "mode": { "title": "Mode", "enum": [ "search", "browse" ], "type": "string", "description": "How to collect artworks. 'search' scrapes each of your queries; 'browse' paginates the entire open-access collection.", "default": "search" }, "queries": { "title": "Search queries", "type": "array", "description": "Search terms to look up (used only in 'search' mode). Each term is scraped separately, e.g. artist, culture, medium or keyword.", "default": [ "monet", "japanese" ], "items": { "type": "string" } }, "hasImage": { "title": "Only artworks with an image", "type": "boolean", "description": "When enabled, only artworks that have a downloadable image are returned.", "default": true }, "maxItems": { "title": "Maximum items", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Maximum number of artworks to scrape in total across all queries (hard cap 1000).", "default": 100 } } }, "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 } } } } } } } } } } ```