# ArchDaily Architecture Projects Scraper (`jungle_synthesizer/archdaily-architecture-projects-scraper`) Actor Scrape architectural project data from ArchDaily — the world's most visited architecture website. Extracts project metadata, location, team credits, images, drawings, and publication details for thousands of global projects across all building types. - **URL**: https://apify.com/jungle\_synthesizer/archdaily-architecture-projects-scraper.md - **Developed by:** [BowTiedRaccoon](https://apify.com/jungle_synthesizer) (community) - **Categories:** E-commerce, Business - **Stats:** 2 total users, 1 monthly users, 33.3% 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 ## ArchDaily Architecture Projects Scraper Scrape architectural project data from [ArchDaily](https://www.archdaily.com) — the world's most visited architecture website. Extracts comprehensive project metadata including location, team credits, images, architectural drawings, and publication details. ### What You Get Each result is a structured record for one architectural project: | Field | Description | |-------|-------------| | `project_id` | Numeric ArchDaily project ID | | `project_url` | Canonical ArchDaily project URL | | `project_title` | Full project name | | `architect_firm` | Primary architecture firm | | `architect_firm_url` | ArchDaily profile URL for the firm | | `architect_country` | Country of the architecture firm | | `project_year` | Year of completion | | `project_location_country` | Country where the project is located | | `project_location_city` | City of the project | | `project_location_address` | Street address | | `latitude` | GPS latitude | | `longitude` | GPS longitude | | `building_type` | Primary category (e.g. Cultural, Residential, Educational) | | `typology_tags` | Comma-separated typology tags | | `area_sqm` | Floor area in square metres | | `area_sqft` | Floor area in square feet | | `photographer_name` | Primary photo credit | | `photographer_url` | ArchDaily photographer profile URL | | `lead_architects` | Named lead architects | | `design_team` | Full design team credits | | `clients` | Client name(s) | | `structural_engineers` | Structural engineering firm(s) | | `mechanical_engineers` | Mechanical / services engineering | | `landscape_architects` | Landscape architecture firm(s) | | `manufacturers` | Product and manufacturer credits | | `awards` | Awards listed for the project | | `publication_date` | ISO date published on ArchDaily | | `text_summary` | First editorial paragraph | | `image_urls` | JSON array of photo image URLs | | `drawing_urls` | JSON array of drawing URLs (plans, sections, elevations) | | `num_images` | Total number of photos | | `num_drawings` | Total number of architectural drawings | | `num_comments` | Reader comment count | | `num_saves` | Times the project was saved | | `scrapedAt` | ISO-8601 scrape timestamp | ### Input Parameters | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | `maxItems` | integer | — | Maximum number of projects to collect (required) | | `startPage` | integer | 1 | Listing page to start from | | `endPage` | integer | — | Last listing page to scrape (optional) | ### Example Output ```json { "project_id": 612345, "project_url": "https://www.archdaily.com/612345/some-building-firm", "project_title": "Community Library / Studio Example", "architect_firm": "Studio Example Architecture", "architect_country": "Japan", "project_year": 2022, "project_location_country": "Japan", "project_location_city": "Tokyo", "building_type": "Cultural Architecture", "area_sqm": 1840, "area_sqft": 19805, "image_urls": "[\"https://images.adsttc.com/...\"]", "drawing_urls": "[\"https://images.adsttc.com/...-plan.jpg\"]", "num_images": 18, "num_drawings": 4, "publication_date": "2023-03-15", "scrapedAt": "2025-01-01T00:00:00.000Z" } ```` ### Use Cases - **Architecture research** — analyse global trends in building typology, materials, and sustainable design across thousands of projects - **Real estate intelligence** — benchmark residential and commercial projects by size, location, and year - **Academic datasets** — build training datasets for architectural AI models linking images to project metadata - **Firm profiling** — map a firm's portfolio, geographic reach, and project history - **Market analysis** — track which building types and geographies receive the most coverage in international architecture media ### Notes - Images and drawings are classified from filename keywords (supports English, Spanish, Polish, and German drawing names) - Area is normalised to both sq m and sq ft — if the source uses imperial units, the actor converts automatically - The scraper paginates through ArchDaily's project listing pages sequentially; use `startPage` + `endPage` to target a specific range - Concurrency is kept low (1 concurrent request, ~3 s between requests) to be a respectful crawling client # 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 projects to scrape. ## `startPage` (type: `integer`): Page to start from (1-indexed). ## `endPage` (type: `integer`): Last page to scrape. Leave blank for unlimited. ## 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, "startPage": 1 } ``` # 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, "startPage": 1 }; // Run the Actor and wait for it to finish const run = await client.actor("jungle_synthesizer/archdaily-architecture-projects-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, "startPage": 1, } # Run the Actor and wait for it to finish run = client.actor("jungle_synthesizer/archdaily-architecture-projects-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, "startPage": 1 }' | apify call jungle_synthesizer/archdaily-architecture-projects-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jungle_synthesizer/archdaily-architecture-projects-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "ArchDaily Architecture Projects Scraper", "description": "Scrape architectural project data from ArchDaily — the world's most visited architecture website. Extracts project metadata, location, team credits, images, drawings, and publication details for thousands of global projects across all building types.", "version": "0.1", "x-build-id": "o56uuEso6k4ZJ8WFG" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jungle_synthesizer~archdaily-architecture-projects-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jungle_synthesizer-archdaily-architecture-projects-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~archdaily-architecture-projects-scraper/runs": { "post": { "operationId": "runs-sync-jungle_synthesizer-archdaily-architecture-projects-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~archdaily-architecture-projects-scraper/run-sync": { "post": { "operationId": "run-sync-jungle_synthesizer-archdaily-architecture-projects-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 projects to scrape." }, "startPage": { "title": "Start Page", "type": "integer", "description": "Page to start from (1-indexed).", "default": 1 }, "endPage": { "title": "End Page", "type": "integer", "description": "Last page to scrape. Leave blank for unlimited." } } }, "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 } } } } } } } } } } ```