# Behance Search Scraper (`headlessagent/behance-search-scraper`) Actor Scrape Behance search results by type — projects, users, assets, or images. Each type supports its own query list. Returns covers, owner info, stats, and image metadata - **URL**: https://apify.com/headlessagent/behance-search-scraper.md - **Developed by:** [Headless Agent](https://apify.com/headlessagent) (community) - **Categories:** Developer tools, Social media, Automation - **Stats:** 3 total users, 2 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $4.00 / 1,000 results This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 ## Behance Search Scraper Scrape Behance search results — no API key or login required. Search across **projects, users, assets, and images** simultaneously, each with their own query list. Get clean, structured JSON output. ### 🤔 What can Behance Search Scraper do? This Actor lets you **search Behance across multiple content types** in a single run. Define independent query lists per type and get structured results: 🔍 Search by **projects, users, assets, and images** — simultaneously, each with their own queries 🖼️ Extract project covers in **original resolution** and **808px** — both JPG and WEBP formats 👤 Get user profile info — username, display name, avatar, location, availability status 📦 Get asset projects — filtered to those that have downloadable source files 🎞️ Get image results — individual image modules with resolution, tags, colors, and EXIF data 📊 Collect views and appreciations (likes) for each project 🎨 Extract dominant colors from project covers 📅 Get published and last modified timestamps 🔁 Rotate proxies to avoid rate limiting (optional) 📦 Download results in **JSON, CSV, XML, or Excel** from the Apify Dataset 🦾 Integrate via **API, webhooks, SDKs** (Python & Node.js), or connect with Zapier, n8n, Make, and more ### ✅ What data can I scrape from Behance? #### Projects - Project ID, slug, URL, name, publish status, privacy level - Cover images — original (JPG & WEBP) and 808px (JPG & WEBP) - Dominant color (RGB) - Appreciations (likes) and total views - Published and last modified timestamps - Owner(s) — username, display name, avatar URL, location, country, city, CreatorPro status, full-time & freelance availability #### Users - User ID, username, display name, URL - Avatar URL (highest available resolution) - Location, country, city - CreatorPro status, full-time & freelance availability - Recent projects (up to 4) with covers - Social references (links) - Followers and following counts #### Assets - Same fields as projects, filtered to those with downloadable source files - Source file metadata when available: title, extension, MIME type, category, license type, tier, price #### Images - Image ID, source URL, dimensions (width × height) - Tags and dominant colors (RGB) - EXIF data - Best available JPG and WEBP URLs at highest resolution - Parent project info (ID, slug, stats, owners) ### 🔧 How to scrape data from Behance 1. [Create](https://console.apify.com/sign-up) a free Apify account. 2. Open Behance Search Scraper. 3. Define your **search tasks** — each task specifies a content type and its own list of queries. 4. Optionally set **max results per query** and **proxy URLs**. 5. Click **Save & Start** and wait for the results. 6. Download your data in JSON, CSV, XML, Excel, or access it via the Apify API. ### ⬇️ Input Each search type has its own query list. Leave a field empty (or omit it) to skip that type. | Field | Type | Description | |------------------|------------|-----------------------------------------------------------------------------------| | `projectQueries` | `string[]` | Search queries for Behance **projects**. | | `userQueries` | `string[]` | Search queries for Behance **users / designers**. | | `assetQueries` | `string[]` | Search queries for Behance **assets** (projects with downloadable source files). | | `imageQueries` | `string[]` | Search queries for individual Behance **images**. | | `maxResults` | `integer` | Max results per query per type (default: 20). | | `proxy` | `string[]` | Optional proxy URLs (`http://` or `socks5://`). Rotated round-robin per request. | At least one of the four query fields must be non-empty. #### Example input ```json { "projectQueries": ["figma ui design", "mobile app branding"], "userQueries": ["ui designer"], "assetQueries": ["ui kit mockup"], "imageQueries": ["sunset minimal"], "maxResults": 5, "proxy": ["http://user:pass@host:port"] } ```` ### ⬆️ Output Results are pushed to the Apify Dataset. Each item includes `search_type` and `search_query` fields so you know which task produced each result. #### Project result sample ```json { "search_type": "projects", "search_query": "figma ui design", "id": 246157347, "name": "Loftly — Furniture E-commerce Design", "slug": "Loftly-Furniture-E-commerce-Design", "url": "https://www.behance.net/gallery/246157347/Loftly-Furniture-E-commerce-Design", "publishedOn": 1774025002, "modifiedOn": 1777750073, "publishStatus": "PUBLISHED", "privacyLevel": "PUBLIC", "covers": { "original": [ { "url": "https://mir-s3-cdn-cf.behance.net/projects/original/7f1e24246157347.jpg", "width": null, "type": "JPG" }, { "url": "https://mir-s3-cdn-cf.behance.net/projects/original_webp/7f1e24246157347.jpg", "width": null, "type": "WEBP" } ], "size808": [ { "url": "https://mir-s3-cdn-cf.behance.net/projects/max_808/7f1e24246157347.jpg", "width": 808, "type": "JPG" }, { "url": "https://mir-s3-cdn-cf.behance.net/projects/max_808_webp/7f1e24246157347.jpg", "width": 808, "type": "WEBP" } ] }, "colors": { "r": 253, "g": 253, "b": 253 }, "appreciations": 180, "views": 3339, "isBoosted": false, "isPrivate": false, "owners": [ { "id": 120437107, "username": "h4zzzzzz", "displayName": "Volodymyr Harasevych", "firstName": "Volodymyr", "url": "https://www.behance.net/h4zzzzzz", "location": "Lviv, Ukraine", "country": "Ukraine", "city": "Lviv", "isAvailableFullTime": false, "isAvailableFreelance": true, "isCreatorPro": true, "avatarUrl": "https://pps.services.adobe.com/api/profile/.../276" } ] } ``` #### User result sample ```json { "search_type": "users", "search_query": "ui designer", "id": 12345678, "username": "janedoe", "displayName": "Jane Doe", "url": "https://www.behance.net/janedoe", "avatarUrl": "https://pps.services.adobe.com/api/profile/.../276", "location": "Berlin, Germany", "country": "Germany", "city": "Berlin", "isAvailableFullTime": true, "isAvailableFreelance": true, "isCreatorPro": false, "followers": 4200, "following": 310, "recentProjects": [ { "id": 111111111, "name": "Brand Identity", "url": "https://www.behance.net/gallery/111111111/Brand-Identity", "covers": { "original": [...], "size808": [...] } } ], "socialReferences": [ { "url": "https://dribbble.com/janedoe", "text": "Dribbble" } ] } ``` #### Image result sample ```json { "search_type": "images", "search_query": "sunset minimal", "id": 987654321, "src": "https://mir-s3-cdn-cf.behance.net/project_modules/disp/abc123.jpg", "width": 1400, "height": 900, "tags": ["sunset", "minimal", "nature"], "colors": [{ "r": 255, "g": 120, "b": 60 }], "bestJpg": { "url": "https://mir-s3-cdn-cf.behance.net/project_modules/fs/abc123.jpg", "width": 1400 }, "bestWebp": { "url": "https://mir-s3-cdn-cf.behance.net/project_modules/fs/abc123.webp", "width": 1400 }, "project": { "id": 200000001, "slug": "Sunset-Collection", "views": 8500, "appreciations": 620 } } ``` ### 🔗 Integrations Connect Behance Search Scraper with almost any cloud service or web app through [Apify integrations](https://apify.com/integrations): **Zapier, n8n, Make, Slack, Google Sheets, Google Drive, Airbyte, GitHub**, and many more. Use [webhooks](https://docs.apify.com/integrations/webhooks) to trigger actions whenever a run finishes — for example, sending a Slack notification or pushing data to a Google Sheet automatically. ### 🦾 Using the Apify API The Apify [API](https://docs.apify.com/api/v2) gives you programmatic access to this scraper. Start runs, fetch datasets, monitor execution, and integrate into your own workflows. - **Python**: [`apify-client`](https://pypi.org/project/apify-client/) on PyPI - **Node.js**: [`apify-client`](https://www.npmjs.com/package/apify-client) on NPM ### ❓ FAQ #### Can I search multiple types in one run? Yes. Fill in any combination of `projectQueries`, `userQueries`, `assetQueries`, and `imageQueries`. Each non-empty field runs as its own search task. Results are tagged with `search_type` and `search_query`. #### Can each type have different queries? Yes. Each field is independent — `projectQueries` can have different terms from `userQueries`, and so on. #### How many results can I get per query? Default is 20 per query per type. Increase this with `maxResults` (e.g. 50, 100, 500). Behance reports up to ~10,000 results per query. #### Can I use multiple proxies? Yes. The `proxy` field accepts an array of proxy URLs. They are rotated round-robin across requests. #### Does this require a Behance account or API key? No. The scraper uses Behance's public GraphQL API — no login or API key required. A unique session token (`bcp`) is auto-generated per run using UUID v4. #### Is it legal to scrape Behance? This scraper only extracts publicly available data — nothing behind authentication. However, scraped data may contain personal information protected by [GDPR](https://en.wikipedia.org/wiki/General_Data_Protection_Regulation) and other regulations. Ensure you have a legitimate reason for collection and consult legal counsel if unsure. See also Apify's blog on the [legality of web scraping](https://blog.apify.com/is-web-scraping-legal/). # Actor input Schema ## `projectQueries` (type: `array`): Search queries for Behance projects. Leave empty to skip. ## `userQueries` (type: `array`): Search queries for Behance users / designers. Leave empty to skip. ## `assetQueries` (type: `array`): Search queries for Behance assets (projects with downloadable source files). Leave empty to skip. ## `imageQueries` (type: `array`): Search queries for individual Behance images. Leave empty to skip. ## `maxResults` (type: `integer`): Maximum number of results to scrape per query per type (default: 20). ## `proxy` (type: `array`): Optional proxy URLs (e.g. http://user:pass@host:port). Rotated round-robin per request. ## Actor input object example ```json { "projectQueries": [ "figma ui design", "mobile app branding" ], "userQueries": [ "ui designer" ], "assetQueries": [ "ui kit mockup" ], "imageQueries": [ "sunset minimal" ], "maxResults": 5, "proxy": [] } ``` # 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 = { "projectQueries": [ "figma ui design", "mobile app branding" ], "userQueries": [ "ui designer" ], "assetQueries": [ "ui kit mockup" ], "imageQueries": [ "sunset minimal" ], "maxResults": 5, "proxy": [] }; // Run the Actor and wait for it to finish const run = await client.actor("headlessagent/behance-search-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 = { "projectQueries": [ "figma ui design", "mobile app branding", ], "userQueries": ["ui designer"], "assetQueries": ["ui kit mockup"], "imageQueries": ["sunset minimal"], "maxResults": 5, "proxy": [], } # Run the Actor and wait for it to finish run = client.actor("headlessagent/behance-search-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 '{ "projectQueries": [ "figma ui design", "mobile app branding" ], "userQueries": [ "ui designer" ], "assetQueries": [ "ui kit mockup" ], "imageQueries": [ "sunset minimal" ], "maxResults": 5, "proxy": [] }' | apify call headlessagent/behance-search-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=headlessagent/behance-search-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Behance Search Scraper", "description": "Scrape Behance search results by type — projects, users, assets, or images. Each type supports its own query list. Returns covers, owner info, stats, and image metadata", "version": "1.0", "x-build-id": "TvPOyoflDnx5QThMt" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/headlessagent~behance-search-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-headlessagent-behance-search-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/headlessagent~behance-search-scraper/runs": { "post": { "operationId": "runs-sync-headlessagent-behance-search-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/headlessagent~behance-search-scraper/run-sync": { "post": { "operationId": "run-sync-headlessagent-behance-search-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "projectQueries": { "title": "Project Queries", "type": "array", "description": "Search queries for Behance projects. Leave empty to skip.", "items": { "type": "string" } }, "userQueries": { "title": "User Queries", "type": "array", "description": "Search queries for Behance users / designers. Leave empty to skip.", "items": { "type": "string" } }, "assetQueries": { "title": "Asset Queries", "type": "array", "description": "Search queries for Behance assets (projects with downloadable source files). Leave empty to skip.", "items": { "type": "string" } }, "imageQueries": { "title": "Image Queries", "type": "array", "description": "Search queries for individual Behance images. Leave empty to skip.", "items": { "type": "string" } }, "maxResults": { "title": "Max Results per Query", "minimum": 1, "type": "integer", "description": "Maximum number of results to scrape per query per type (default: 20)." }, "proxy": { "title": "Proxy URLs", "type": "array", "description": "Optional proxy URLs (e.g. http://user:pass@host:port). Rotated round-robin per request.", "items": { "type": "string" } } } }, "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 } } } } } } } } } } ```