# Art Institute of Chicago Artworks Scraper (`jungle_synthesizer/art-institute-chicago-artworks-api-scraper`) Actor Scrapes the Art Institute of Chicago open API — 131k artworks with metadata, dominant colour analysis (HSL), style/subject taxonomy, and IIIF image URLs. Supports full-walk, keyword search, date-range, type/department filters, and by-ID lookups. No API key required. - **URL**: https://apify.com/jungle\_synthesizer/art-institute-chicago-artworks-api-scraper.md - **Developed by:** [BowTiedRaccoon](https://apify.com/jungle_synthesizer) (community) - **Categories:** AI, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per event 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.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 ## Art Institute of Chicago Artworks Scraper Scrape artwork records from the Art Institute of Chicago's open REST API. Returns full metadata for 131k+ artworks including title, artist, department, medium, dimensions, classification, style/subject taxonomy, dominant colour analysis (HSL), and IIIF image URLs. No API key required — the AIC API is fully public with CC0 metadata. ### What you get Each scraped record contains: | Field | Description | |-------|-------------| | `id` | Unique AIC artwork ID | | `title` | Artwork title | | `artist_display` | Full artist display string (name, nationality, dates) | | `artist_title` | Primary artist name (short form) | | `date_display` | Human-readable date string (e.g. "1906" or "c. 1890–1900") | | `date_start` | Earliest possible year for the artwork | | `date_end` | Latest possible year for the artwork | | `medium_display` | Materials and technique description | | `artwork_type_title` | Artwork type (Painting, Drawing, Print, Sculpture, etc.) | | `department_title` | Museum department holding the artwork | | `classification_titles` | Classification tags array (e.g. "oil on canvas", "european painting") | | `dimensions` | Physical dimensions string | | `place_of_origin` | Country or region where the artwork was created | | `is_public_domain` | True if the artwork image is in the public domain (CC0) | | `credit_line` | Donor/acquisition credit line | | `gallery_title` | Gallery currently displaying the artwork (null if in storage) | | `color_h` | Dominant colour hue (HSL, 0–360) | | `color_s` | Dominant colour saturation (HSL, 0–100) | | `color_l` | Dominant colour lightness (HSL, 0–100) | | `subject_titles` | Subject/theme tags array (e.g. "landscapes", "water lilies") | | `style_titles` | Art style tags array (e.g. "Impressionism", "20th Century") | | `image_id` | IIIF image identifier (UUID) | | `iiif_image_url` | Full IIIF image URL (843px wide JPEG) — null if no image | | `aic_artwork_url` | Canonical artwork page URL on artic.edu | ### Modes #### Walk mode (default) Paginate through the full collection of 131k+ artworks: ```json { "mode": "walk", "maxItems": 1000 } ```` Omit `maxItems` to walk the entire collection. Add `publicDomainOnly: true` to restrict results to CC0 artworks (image downloads safe): ```json { "mode": "walk", "publicDomainOnly": true, "maxItems": 500 } ``` #### Search mode Full-text keyword search across all artwork metadata: ```json { "mode": "search", "query": "impressionism", "maxItems": 100 } ``` #### Filter mode Filter by artwork type, department, or date range: ```json { "mode": "filter", "artworkTypeTitle": "Painting", "dateFrom": 1880, "dateTo": 1920, "maxItems": 200 } ``` Available filters: - `artworkTypeTitle` — artwork type string (e.g. "Painting", "Drawing", "Print", "Sculpture", "Photograph") - `departmentTitle` — department name string (e.g. "Photography and Media", "Painting and Sculpture of Europe") - `dateFrom` — filter artworks with `date_start` >= this year - `dateTo` — filter artworks with `date_start` <= this year Combine multiple filters in one run. #### By-ID mode Fetch specific artworks by their AIC numeric ID: ```json { "mode": "by_ids", "artworkIds": [16568, 27992, 28560], "maxItems": 10 } ``` IDs can be found in AIC URLs: `https://www.artic.edu/artworks/16568/water-lilies`. ### Use cases - **Art analytics datasets** — combine with the Met Museum actor for a comprehensive GLAM collection - **Colour research** — the `color_h/s/l` fields enable colour-palette analysis across 131k artworks - **Style/subject taxonomy** — `style_titles` and `subject_titles` provide ready-made ML training labels - **Public-domain image pipelines** — filter `is_public_domain: true` and use `iiif_image_url` for CC0 downloads - **Museum collection monitoring** — run incrementally to detect newly added records ### Rate limits The AIC API is fully open — no authentication required. The actor runs at ~60 requests/minute with a 1-second delay between pages, well within the API's polite-use advisory. No proxy is needed. # Actor input Schema ## `sp_intended_usage` (type: `string`): Please describe how you plan to use the data extracted by this crawler. ## `sp_improvement_suggestions` (type: `string`): Provide any feedback or suggestions for improvements. ## `sp_contact` (type: `string`): Provide your email address so we can get in touch with you. ## `mode` (type: `string`): Crawl mode: "walk" (full collection, paginated), "search" (keyword query), "filter" (artwork\_type / department / date-range), or "by\_ids" (fetch specific IDs). ## `query` (type: `string`): Keyword query string — used only in search mode. ## `artworkTypeTitle` (type: `string`): Filter by artwork type (e.g. Painting, Drawing, Print). Used in filter mode. ## `departmentTitle` (type: `string`): Filter by department name (e.g. Photography and Media). Used in filter mode. ## `dateFrom` (type: `integer`): Filter artworks with date\_start >= this year. Used in filter mode. ## `dateTo` (type: `integer`): Filter artworks with date\_end <= this year. Used in filter mode. ## `artworkIds` (type: `array`): List of numeric artwork IDs to fetch directly. Used only in by\_ids mode. ## `publicDomainOnly` (type: `boolean`): Return only artworks with is\_public\_domain = true (applies to all modes). ## `maxItems` (type: `integer`): Maximum number of artworks to return. Leave blank for unlimited (full walk). ## Actor input object example ```json { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "mode": "walk", "query": "impressionism", "artworkTypeTitle": "Painting", "artworkIds": [ 16568, 27992, 28560 ], "publicDomainOnly": false, "maxItems": 10 } ``` # Actor output Schema ## `results` (type: `string`): No description # 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 = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "mode": "walk", "query": "impressionism", "artworkTypeTitle": "Painting", "artworkIds": [ 16568, 27992, 28560 ], "maxItems": 10 }; // Run the Actor and wait for it to finish const run = await client.actor("jungle_synthesizer/art-institute-chicago-artworks-api-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 = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "mode": "walk", "query": "impressionism", "artworkTypeTitle": "Painting", "artworkIds": [ 16568, 27992, 28560, ], "maxItems": 10, } # Run the Actor and wait for it to finish run = client.actor("jungle_synthesizer/art-institute-chicago-artworks-api-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 '{ "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "mode": "walk", "query": "impressionism", "artworkTypeTitle": "Painting", "artworkIds": [ 16568, 27992, 28560 ], "maxItems": 10 }' | apify call jungle_synthesizer/art-institute-chicago-artworks-api-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jungle_synthesizer/art-institute-chicago-artworks-api-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Art Institute of Chicago Artworks Scraper", "description": "Scrapes the Art Institute of Chicago open API — 131k artworks with metadata, dominant colour analysis (HSL), style/subject taxonomy, and IIIF image URLs. Supports full-walk, keyword search, date-range, type/department filters, and by-ID lookups. No API key required.", "version": "0.1", "x-build-id": "dMw1L8jXmvG2uCLZT" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jungle_synthesizer~art-institute-chicago-artworks-api-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jungle_synthesizer-art-institute-chicago-artworks-api-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/jungle_synthesizer~art-institute-chicago-artworks-api-scraper/runs": { "post": { "operationId": "runs-sync-jungle_synthesizer-art-institute-chicago-artworks-api-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/jungle_synthesizer~art-institute-chicago-artworks-api-scraper/run-sync": { "post": { "operationId": "run-sync-jungle_synthesizer-art-institute-chicago-artworks-api-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", "required": [ "maxItems" ], "properties": { "sp_intended_usage": { "title": "What is the intended usage of this data?", "minLength": 1, "type": "string", "description": "Please describe how you plan to use the data extracted by this crawler." }, "sp_improvement_suggestions": { "title": "How can we improve this crawler for you?", "minLength": 1, "type": "string", "description": "Provide any feedback or suggestions for improvements." }, "sp_contact": { "title": "Contact Email", "minLength": 1, "type": "string", "description": "Provide your email address so we can get in touch with you." }, "mode": { "title": "Mode", "enum": [ "walk", "search", "filter", "by_ids" ], "type": "string", "description": "Crawl mode: \"walk\" (full collection, paginated), \"search\" (keyword query), \"filter\" (artwork_type / department / date-range), or \"by_ids\" (fetch specific IDs).", "default": "walk" }, "query": { "title": "Search Query", "type": "string", "description": "Keyword query string — used only in search mode." }, "artworkTypeTitle": { "title": "Artwork Type Filter", "type": "string", "description": "Filter by artwork type (e.g. Painting, Drawing, Print). Used in filter mode." }, "departmentTitle": { "title": "Department Filter", "type": "string", "description": "Filter by department name (e.g. Photography and Media). Used in filter mode." }, "dateFrom": { "title": "Date From (year)", "type": "integer", "description": "Filter artworks with date_start >= this year. Used in filter mode." }, "dateTo": { "title": "Date To (year)", "type": "integer", "description": "Filter artworks with date_end <= this year. Used in filter mode." }, "artworkIds": { "title": "Artwork IDs", "type": "array", "description": "List of numeric artwork IDs to fetch directly. Used only in by_ids mode.", "items": { "type": "string" } }, "publicDomainOnly": { "title": "Public Domain Only", "type": "boolean", "description": "Return only artworks with is_public_domain = true (applies to all modes).", "default": false }, "maxItems": { "title": "Max Items", "type": "integer", "description": "Maximum number of artworks to return. Leave blank for unlimited (full walk).", "default": 15 } } }, "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 } } } } } } } } } } ```