# Behance Images Scraper - Low-costπŸ’²πŸ”₯πŸ“ΈπŸŽ¨ (`delectable_incubator/behance-images-scraper---low-cost`) Actor Scrape Behance image data by keyword πŸ“ΈπŸŽ¨ with a powerful visual content scraper. Extract image sources, sizes, tags, colors, project details, and owner profiles. Ideal for creative trend analysis, portfolio insights, and structured design datasets for marketers and agencies πŸ“ŠπŸš€ - **URL**: https://apify.com/delectable\_incubator/behance-images-scraper---low-cost.md - **Developed by:** [Prime Scrape](https://apify.com/delectable_incubator) (community) - **Categories:** Videos, Social media, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $0.00005 / actor start This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. 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 Images Scraper - PrimeScrape

--- ### Behance Images Scraper πŸ“ΈπŸ”πŸŽ¨ The Behance Images Scraper is a fast and scalable Apify Actor designed to extract structured image data from Behance based on keyword searches. It helps you collect creative visuals, analyze design trends, and build datasets from Behance projects for research, inspiration, and automation workflows. --- ### 🎯 What This Scraper Does Simply provide bulk keywords and the scraper handles everything automatically. βœ… Supports multiple keywords in one run βœ… Extracts images + project metadata βœ… Handles pagination automatically βœ… Collects structured creative data βœ… Ideal for trend analysis & AI datasets βœ… Fully automation-ready --- ### πŸ“Š Data Extracted πŸ–ΌοΈ Image & Project Fields | Field | Description | | ----------------- | ------------------------ | | πŸ†” `id` | Image ID | | 🌐 `src` | Image source URL | | πŸ“ `width` | Image width | | πŸ“ `height` | Image height | | 🏷️ `tags` | Image tags | | 🎨 `colors` | Dominant RGB colors | | πŸ“‚ `project` | Project metadata | | 🌍 `project.url` | Project URL | | πŸ§‘β€πŸ’Ό `owners` | Project owner info | | πŸ“ `stats` | Views, likes, comments | | πŸ–ΌοΈ `image_sizes` | Available image variants | | βš–οΈ `license` | Usage rights information | --- ### πŸ›  How to Use 1️⃣ Configure Input Provide your search parameters: ```` { "keyword": \["design"], "maxitems": 100 } ``` 2️⃣ Run the Actor β€’ Perform keyword-based search on Behance β€’ Extract listings and details β€’ Structure and clean the dataset β€’ Stop when maxitems limit is reached 3️⃣ Export the Dataset Download your results in multiple formats: βœ… JSON βœ… CSV βœ… Excel βœ… XML βœ… HTML --- ### βš™οΈ Input Configuration #### πŸ“₯ Input Example ``` { "keywords": \["branding", "graphic design", "illustration"], "maxitems": 100 } ``` #### Input Fields | Field | Type | Description | | ---------- | ------- | ---------------------------------------- | | `keywords` | array | List of search keywords (bulk supported) | | `maxitems` | integer | Max results per keyword | --- ### πŸ“€ Output Example ``` \[ { "id": 779414805, "src": "https://mir-s3-cdn-cf.behance.net/project\_modules/source/example.png", "width": 600, "height": 1200, "tags": \["branding", "design", "marketing"], "colors": { "r": 10, "g": 11, "b": 13 }, "project": { "id": 137852973, "name": "Social Media Campaign", "url": "https://www.behance.net/gallery/example", "stats": { "appreciations": { "all": 344 }, "views": { "all": 8377 }, "comments": { "all": 15 } } } } ] ```` --- ### πŸ“Š Output explanation | Field | Description | | --------- | -------------------- | | `src` | Image URL | | `tags` | Image tags | | `colors` | RGB color data | | `project` | Related project info | | `owners` | Creator details | | `stats` | Engagement metrics | --- ### 🌍 Why Use This Scraper? 🎨 Creative Trend Analysis β€” track design styles πŸ“Š Market Research β€” analyze visual content trends 🧠 AI Dataset Building β€” structured image datasets πŸ’‘ Inspiration Mining β€” discover creative assets 🏒 Brand Monitoring β€” track visual identity trends πŸ”„ Automation Ready β€” scalable data collection --- ### ⚠️ Disclaimer This tool is an independent solution and is not affiliated with or endorsed by Behance or Adobe. --- ### πŸ’Έ Pricing This scraper runs on a **pay per events subscription model**. You only pay for **successful runs**. πŸ’³ **Price:** $9.99 / 1000 results --- ### Related Actors If you're interested in business scraping solutions, explore more tools: (Coming soon) --- ### πŸ“¬ Support ⭐⭐⭐⭐⭐ Leave a 5-star rating if you like this tool --- ### 🌍 PrimeScrape Built for scalable web data extraction & automation Contact for custom scraping solutions or enterprise requests via Apify or by email. # Actor input Schema ## `keywords` (type: `array`): List of keywords to search for on Behance Images. Each keyword is scraped independently. ## `maxitemsPerKeyword` (type: `integer`): Maximum number of images to scrape for each keyword. ## Actor input object example ```json { "keywords": [ "branding", "typography" ], "maxitemsPerKeyword": 60 } ```` # Actor output Schema ## `overview` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "keywords": [ "branding", "typography" ], "maxitemsPerKeyword": 60 }; // Run the Actor and wait for it to finish const run = await client.actor("delectable_incubator/behance-images-scraper---low-cost").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // πŸ“š Want to learn more πŸ“–? Go to β†’ 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 = { "keywords": [ "branding", "typography", ], "maxitemsPerKeyword": 60, } # Run the Actor and wait for it to finish run = client.actor("delectable_incubator/behance-images-scraper---low-cost").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # πŸ“š Want to learn more πŸ“–? Go to β†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "keywords": [ "branding", "typography" ], "maxitemsPerKeyword": 60 }' | apify call delectable_incubator/behance-images-scraper---low-cost --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=delectable_incubator/behance-images-scraper---low-cost", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Behance Images Scraper - Low-costπŸ’²πŸ”₯πŸ“ΈπŸŽ¨", "description": "Scrape Behance image data by keyword πŸ“ΈπŸŽ¨ with a powerful visual content scraper. \n\nExtract image sources, sizes, tags, colors, project details, and owner profiles. \n\nIdeal for creative trend analysis, portfolio insights, and structured design datasets for marketers and agencies πŸ“ŠπŸš€", "version": "0.0", "x-build-id": "I0qxroqT9iiEt1isc" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/delectable_incubator~behance-images-scraper---low-cost/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-delectable_incubator-behance-images-scraper---low-cost", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/delectable_incubator~behance-images-scraper---low-cost/runs": { "post": { "operationId": "runs-sync-delectable_incubator-behance-images-scraper---low-cost", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/delectable_incubator~behance-images-scraper---low-cost/run-sync": { "post": { "operationId": "run-sync-delectable_incubator-behance-images-scraper---low-cost", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "keywords" ], "properties": { "keywords": { "title": "Keywords πŸ”πŸ“Έ", "type": "array", "description": "List of keywords to search for on Behance Images. Each keyword is scraped independently.", "default": [ "branding", "typography" ], "items": { "type": "string" } }, "maxitemsPerKeyword": { "title": "Max results per keyword πŸ“Š", "type": "integer", "description": "Maximum number of images to scrape for each keyword.", "default": 60 } } }, "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 } } } } } } } } } } ```