# Art Institute of Chicago Scraper (`ninhothedev/art-institute-chicago-scraper`) Actor $0.5/1K 🔥 Fast Art Institute of Chicago scraper! Artworks — title, artist, date, medium & IIIF image. No key. JSON, CSV, Excel or API in seconds. Search or browse & pull thousands for art datasets & ML training ⚡ - **URL**: https://apify.com/ninhothedev/art-institute-chicago-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 ## Art Institute of Chicago Scraper Scrape artworks from the **Art Institute of Chicago** open collection — one of the largest, richest art datasets in the world — directly into clean, structured JSON. **No API key, no login, no setup.** Search by artist, title, style or keyword, or browse the entire public collection. Perfect for **art datasets, education, machine-learning / computer-vision training, and cultural-heritage research**. > Pricing: roughly **$0.5 per 1,000 artworks** — one of the cheapest art-data actors on the store. ### Features - 🎨 **Two modes** — `search` (run any list of queries) or `browse` (walk the full collection). - 🖼️ **IIIF image URLs** — ready-to-download high-resolution image links, built from the museum's IIIF server. - 🏛️ **Rich metadata** — artist, date, medium, dimensions, department, classification, gallery, credit line, subject terms, public-domain and on-view flags. - 🔑 **No API key** — uses the museum's public API. - ⚡ **Datacenter-friendly** — no residential proxies required, so it runs cheap. - 🧱 **Robust** — permissive schema, paginated collection, deduped results, graceful fallbacks. ### Input | Field | Type | Description | |-------|------|-------------| | `mode` | select | `search` (default) or `browse`. | | `queries` | array | Search terms (e.g. `["monet", "van gogh"]`). Used in `search` mode. | | `maxItems` | integer | Max artworks to collect (default 100, max 1000). | #### Example input ```json { "mode": "search", "queries": ["monet", "van gogh"], "maxItems": 100 } ```` ### Output Each dataset item is one artwork: ```json { "id": 16568, "title": "Water Lilies", "artist": "Claude Monet", "artist_display": "Claude Monet (French, 1840–1926)", "date": "1906", "medium": "Oil on canvas", "dimensions": "89.9 × 94.1 cm", "origin": "France", "department": "Painting and Sculpture of Europe", "classification": "painting", "gallery": "Gallery 240", "credit_line": "Mr. and Mrs. Martin A. Ryerson Collection", "is_public_domain": true, "is_on_view": false, "terms": ["oil on canvas", "Impressionism", "landscapes"], "image_url": "https://www.artic.edu/iiif/2/3c27b499-af56-f0d5-93b5-a7f2f1ad5813/full/843,/0/default.jpg", "url": "https://www.artic.edu/artworks/16568", "source": "artic", "scraped_at": "2026-07-20T00:00:00+00:00" } ``` `image_url` is `null` for artworks without a digitized image. ### Use cases - **Art datasets** — build labeled image/metadata datasets for analysis or apps. - **Education** — power classroom tools, quizzes, and study material. - **ML / computer-vision training** — public-domain imagery with clean labels. - **Cultural research** — study collections, movements, mediums and provenance at scale. ### Related actors - [Met Museum Scraper](https://apify.com/ninhothedev/met-museum-scraper) - [Cleveland Art Scraper](https://apify.com/ninhothedev/cleveland-art-scraper) - [Openverse Media Scraper](https://apify.com/ninhothedev/openverse-media-scraper) - [Wikimedia Commons Scraper](https://apify.com/ninhothedev/wikimedia-commons-scraper) ### Keywords art institute of chicago, artic scraper, art api, museum scraper, artwork dataset, public domain art, iiif images, art metadata, painting dataset, ml training data, cultural heritage, open access art, fine art scraper, image dataset ### Data & attribution Data comes from the Art Institute of Chicago's public API. Many works are public domain (`is_public_domain: true`); always check each work's rights before reuse and credit the museum accordingly. # Actor input Schema ## `mode` (type: `string`): How to collect artworks. 'search' runs each query against the collection; 'browse' walks the entire public collection page by page. ## `queries` (type: `array`): List of search terms (artist name, title, keyword, style). Used only in 'search' mode. Each query is paginated until maxItems is reached. ## `maxItems` (type: `integer`): Maximum number of artworks to scrape across all queries (or across the browse walk). Capped at 1000. ## Actor input object example ```json { "mode": "search", "queries": [ "monet", "van gogh" ], "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", "van gogh" ] }; // Run the Actor and wait for it to finish const run = await client.actor("ninhothedev/art-institute-chicago-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", "van gogh", ] } # Run the Actor and wait for it to finish run = client.actor("ninhothedev/art-institute-chicago-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", "van gogh" ] }' | apify call ninhothedev/art-institute-chicago-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=ninhothedev/art-institute-chicago-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Art Institute of Chicago Scraper", "description": "$0.5/1K 🔥 Fast Art Institute of Chicago scraper! Artworks — title, artist, date, medium & IIIF image. No key. JSON, CSV, Excel or API in seconds. Search or browse & pull thousands for art datasets & ML training ⚡", "version": "0.1", "x-build-id": "FHzoasq3bDivroqAW" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/ninhothedev~art-institute-chicago-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-ninhothedev-art-institute-chicago-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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~art-institute-chicago-scraper/runs": { "post": { "operationId": "runs-sync-ninhothedev-art-institute-chicago-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~art-institute-chicago-scraper/run-sync": { "post": { "operationId": "run-sync-ninhothedev-art-institute-chicago-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' runs each query against the collection; 'browse' walks the entire public collection page by page.", "default": "search" }, "queries": { "title": "Search queries", "type": "array", "description": "List of search terms (artist name, title, keyword, style). Used only in 'search' mode. Each query is paginated until maxItems is reached.", "default": [ "monet", "van gogh" ], "items": { "type": "string" } }, "maxItems": { "title": "Max items", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Maximum number of artworks to scrape across all queries (or across the browse walk). Capped at 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 } } } } } } } } } } ```