# Wright20 Design Auction Results Scraper (`jungle_synthesizer/wright20-design-auction-results-scraper`) Actor Scrape Wright auction results — lot data, hammer prices, provenance, literature, and condition reports for 20th-century design objects across 25+ years of Wright sales. - **URL**: https://apify.com/jungle\_synthesizer/wright20-design-auction-results-scraper.md - **Developed by:** [BowTiedRaccoon](https://apify.com/jungle_synthesizer) (community) - **Categories:** E-commerce - **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 ## Wright20 Design Auction Results Scraper Scrapes Wright auction lot data — hammer prices, designer attribution, provenance, literature citations, condition reports, and catalog images — across all past and upcoming Wright sales. Covers 20th-century design, mid-century modern, Scandinavian design, Italian design, studio furniture, and related categories. ### What it does Wright is the dominant US auction house for 20th-century design. This actor: 1. Walks the Wright sitemap index (600+ per-auction sitemaps) to discover all lot URLs 2. Fetches each lot page and extracts the embedded Inertia.js JSON (no browser rendering needed) 3. Returns a clean, flat record per lot with full provenance, pricing, and image data ### Use cases - **Appraisal research**: Cross-reference hammer prices, provenance chains, and exhibition history for attribution and insurance valuation - **Dealer intelligence**: Track price history for specific designers (Nelson, Eames, Bertoia, Saarinen, Knoll, Herman Miller) across years - **Academic cataloging**: Harvest literature citations and exhibition records for design history research - **Market analytics**: Build price indices for MCM design categories, estimate/result ratios, buy-in patterns ### Input | Field | Type | Default | Description | |-------|------|---------|-------------| | `maxItems` | integer | 10 | Maximum number of lot records to return. Set to 0 for no limit (full archive). | ### Output Each record in the dataset represents one auction lot: | Field | Type | Description | |-------|------|-------------| | `lot_id` | string | Wright's internal lot identifier | | `auction_id` | string | Parent auction identifier | | `auction_title` | string | Full auction title (e.g. "Design and Important Italian Glass") | | `auction_date` | string | ISO date of the auction session (YYYY-MM-DD) | | `lot_number` | string | Catalog lot number | | `title` | string | Object title and description | | `designer` | string | Designer or maker (primary attribution) | | `manufacturer` | string | Manufacturer (e.g. Herman Miller, Knoll, Cassina) | | `circa` | string | Date or period (e.g. "1956", "circa 1952") | | `dimensions` | string | Object dimensions | | `materials` | string | Materials / techniques | | `provenance` | string | Provenance history | | `literature` | string | Literature citations | | `exhibited` | string | Exhibition history | | `condition` | string | Condition report | | `estimate_low` | number | Low pre-sale estimate (USD) | | `estimate_high` | number | High pre-sale estimate (USD) | | `currency` | string | Always "USD" | | `hammer_price` | number | Hammer price excluding buyer's premium (null if unsold) | | `price_realized` | number | Total price including buyer's premium (null if unsold) | | `is_sold` | boolean | Whether the lot sold | | `image_urls` | array | Full-resolution (2000px) catalog image URLs | | `lot_url` | string | Direct URL to the lot page on wright20.com | ### Example output ```json { "lot_id": "192808", "auction_id": "1326", "auction_title": "Design and Important Italian Glass", "auction_date": "2025-12-09", "lot_number": "389", "title": "Thin Edge cabinet, model 5723", "designer": "George Nelson & Associates", "manufacturer": "Herman Miller", "circa": "1952", "dimensions": "33⅜ h × 67¼ w × 19⅜ d in", "materials": "teak, aluminum", "provenance": "", "literature": "George Nelson: Architect, Writer, Designer, Teacher, von Vegesack and Eisenbrand, pg. 244 discusses series", "exhibited": "", "condition": "Very good condition. Cabinet has been expertly restored.", "estimate_low": 4000, "estimate_high": 6000, "currency": "USD", "hammer_price": 3000, "price_realized": 3810, "is_sold": true, "image_urls": [ "https://www.wright20.com/items/index/2000/389_1_design_december_2025_..." ], "lot_url": "https://www.wright20.com/auctions/2025/12/design/389" } ```` ### Coverage The actor works across all Wright auction categories visible in the sitemap, including: - Design (quarterly flagship sales) - Important Design - Scandinavian Design - Machine Age Design 1925-1950 - Essential Design - American Design - Italian Design - Studio Furniture - Ceramics, glass, art, and specialty sales Wright's archive spans 25+ years and 50,000+ lots. Set `maxItems: 0` to collect the full archive (a complete run takes several hours at the default concurrency). ### Technical notes - No proxy required — lot pages return 200 with a Chrome User-Agent from Apify datacenter IPs - Data is embedded as structured JSON in each page (Inertia.js `data-page` attribute), so extraction is deterministic and fast - The sitemap index lists upcoming and past auctions — the actor collects both - `hammer_price` (sans buyer's premium) equals `price_realized` divided by approximately 1.27 for standard sales; both fields are provided for flexibility # 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. ## `maxItems` (type: `integer`): Maximum number of records to scrape ## 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...", "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...", "maxItems": 10 }; // Run the Actor and wait for it to finish const run = await client.actor("jungle_synthesizer/wright20-design-auction-results-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...", "maxItems": 10, } # Run the Actor and wait for it to finish run = client.actor("jungle_synthesizer/wright20-design-auction-results-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...", "maxItems": 10 }' | apify call jungle_synthesizer/wright20-design-auction-results-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jungle_synthesizer/wright20-design-auction-results-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Wright20 Design Auction Results Scraper", "description": "Scrape Wright auction results — lot data, hammer prices, provenance, literature, and condition reports for 20th-century design objects across 25+ years of Wright sales.", "version": "0.1", "x-build-id": "sa3OpDXOwWrrEvOUz" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jungle_synthesizer~wright20-design-auction-results-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jungle_synthesizer-wright20-design-auction-results-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~wright20-design-auction-results-scraper/runs": { "post": { "operationId": "runs-sync-jungle_synthesizer-wright20-design-auction-results-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~wright20-design-auction-results-scraper/run-sync": { "post": { "operationId": "run-sync-jungle_synthesizer-wright20-design-auction-results-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." }, "maxItems": { "title": "Max Items", "type": "integer", "description": "Maximum number of records to scrape", "default": 10 } } }, "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 } } } } } } } } } } ```