Is it legal to scrape Behance?

Behance's data is public, but their Terms of Use generally forbid automated extraction. Always check local regulations and scrape responsibly for non-commercial research or personal use while respecting copyright and intellectual property rights.

How do I avoid getting blocked by Cloudflare on Behance?

The most effective way is to use a browser-based scraper like Playwright or Puppeteer that handles JavaScript rendering. Additionally, incorporating residential proxies and rotating your user-agents will significantly reduce the chances of being flagged as a bot.

What format is the scraped data exported in?

Using professional automation tools, data can typically be exported as CSV, JSON, or sent directly to Google Sheets for analysis. If you are writing a custom script, you can save the data into a local database or a file format of your choice.

Does Behance have an official public API?

Currently, Behance does not offer a widely available public API for the general community as they did in the past. Access is now largely restricted, making web scraping the most viable method for large-scale data collection.

How often should I scrape Behance to keep data fresh?

For trend analysis, a weekly or monthly scrape is usually sufficient to capture significant changes. If you are monitoring specific designers or agencies, a daily scrape might be better to capture new project uploads immediately.

Can I scrape images directly from Behance?

Yes, you can extract the direct image URLs from the HTML source. However, be aware that Behance uses lazy-loading, so your scraper must trigger a scroll event or wait for the elements to load to capture the actual image source.

Do I need to be logged in to scrape Behance?

Most project and profile data is publicly accessible without an account. It is generally recommended to scrape without logging in to avoid risking account suspension and to simplify the scraping workflow.

How to Scrape Behance: A Step-by-Step Guide for Creative Data Extraction

Learn how to scrape Behance projects, creative portfolios, and talent data. This guide covers anti-bot bypass, JavaScript rendering, and automated extraction.

Start Scraping Free

behance.netHard

Coverage:GlobalNorth AmericaEuropeAsia

Available Data10 fields

TitlePriceLocationDescriptionImagesSeller InfoContact InfoPosting DateCategoriesAttributes

All Extractable Fields

Project TitleCreative Owner NameProfile URLProject DescriptionAppreciation CountView CountComment CountProject TagsCreative FieldsTools UsedImage Source URLsOwner LocationFollower CountPublished Date

Technical Requirements

JavaScript Required

No Login

Has Pagination

No Official API

Anti-Bot Protection Detected

CloudflareRate LimitingIP BlockingUser-Agent FilteringAI Bot Blocking

About Behance

Learn what Behance offers and what valuable data can be extracted from it.

Behance is the world's largest creative network, owned by Adobe, serving as a premier social media platform and portfolio hosting service for creators. It allows professionals across graphic design, photography, illustration, and UI/UX to showcase their work through project-based galleries. The platform is deeply integrated with the Adobe Creative Cloud ecosystem, making it the primary hub for creative talent globally.

The platform contains a massive repository of structured data, including project categories, specific tools used (like Photoshop or Figma), and detailed professional metadata. Each project listing usually includes high-resolution images, descriptions, view counts, appreciations, and direct links to the creator's profile. This makes it an essential resource for companies looking to understand visual trends or source high-end creative talent.

Scraping Behance is particularly valuable for competitive intelligence, trend forecasting in the design industry, and identifying top-tier talent for high-end creative roles. Because the data is rich with technical attributes, such as software used and project tags, it provides insights into how the creative industry is evolving and which tools are dominating the professional landscape.

Why Scrape Behance?

Discover the business value and use cases for extracting data from Behance.

Talent Acquisition and Recruitment

Market Research for Design Trends

Competitive Intelligence for Creative Agencies

Lead Generation for Software Companies

Data Aggregation for Portfolio Directories

Academic Research on Digital Arts

Scraping Challenges

Technical challenges you may encounter when scraping Behance.

Advanced Cloudflare Bot Protection

Heavy JavaScript rendering requirements

Dynamic infinite scroll pagination

Nested complex CSS selectors

Image lazy-loading and protection

Scrape Behance with AI

No coding required. Extract data in minutes with AI-powered automation.

How It Works

Describe What You Need

Tell the AI what data you want to extract from Behance. Just type it in plain language — no coding or selectors needed.

AI Extracts the Data

Our artificial intelligence navigates Behance, handles dynamic content, and extracts exactly what you asked for.

Get Your Data

Receive clean, structured data ready to export as CSV, JSON, or send directly to your apps and workflows.

Why Use AI for Scraping

Bypasses Cloudflare automatically

Requires zero coding skills

Handles infinite scroll seamlessly

Scheduled cloud execution

Start Scraping Free

No credit card requiredFree tier availableNo setup needed

No-Code Web Scrapers for Behance

Point-and-click alternatives to AI-powered scraping

Several no-code tools like Browse.ai, Octoparse, Axiom, and ParseHub can help you scrape Behance. These tools use visual interfaces to select elements, but they come with trade-offs compared to AI-powered solutions.

Typical Workflow with No-Code Tools

Install browser extension or sign up for the platform

Navigate to the target website and open the tool

Point-and-click to select data elements you want to extract

Configure CSS selectors for each data field

Set up pagination rules to scrape multiple pages

Handle CAPTCHAs (often requires manual solving)

Configure scheduling for automated runs

Export data to CSV, JSON, or connect via API

Common Challenges

Learning curve

Understanding selectors and extraction logic takes time

Selectors break

Website changes can break your entire workflow

Dynamic content issues

JavaScript-heavy sites often require complex workarounds

CAPTCHA limitations

Most tools require manual intervention for CAPTCHAs

IP blocking

Aggressive scraping can get your IP banned

Code Examples

import requests
from bs4 import BeautifulSoup

# Note: This will likely trigger Cloudflare if run from a data center IP
url = "https://www.behance.net/search/projects?field=graphic+design"
headers = {
    "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36"
}

try:
    response = requests.get(url, headers=headers, timeout=10)
    if response.status_code == 200:
        soup = BeautifulSoup(response.text, 'html.parser')
        # Behance renders content via JS; static scraping will find limited data
        projects = soup.find_all('div', class_='ProjectCover-root-167')
        for project in projects:
            title = project.find('a', class_='ProjectCover-title-3_1').text
            print(f"Found Project: {title}")
    else:
        print(f"Blocked or error: {response.status_code}")
except Exception as e:
    print(f"Request failed: {e}")

When to Use

Best for static HTML pages where content is loaded server-side. The fastest and simplest approach when JavaScript rendering isn't required.

Advantages

●Fastest execution (no browser overhead)
●Lowest resource consumption
●Easy to parallelize with asyncio
●Great for APIs and static pages

Limitations

●Cannot execute JavaScript
●Fails on SPAs and dynamic content
●May struggle with complex anti-bot systems

from playwright.sync_api import sync_playwright

def scrape_behance():
    with sync_playwright() as p:
        browser = p.chromium.launch(headless=True)
        page = browser.new_page()
        page.goto("https://www.behance.net/search/projects?field=architecture")
        # Wait for dynamic content to load
        page.wait_for_selector(".ProjectCover-root-167")
        # Scroll down to trigger lazy loading
        page.mouse.wheel(0, 5000)
        page.wait_for_timeout(2000)
        projects = page.query_selector_all(".ProjectCover-root-167")
        data = []
        for p_elem in projects:
            title = p_elem.query_selector(".ProjectCover-title-3_1").inner_text()
            owner = p_elem.query_selector(".ProjectCover-username-28M").inner_text()
            data.append({"title": title, "owner": owner})
        print(data)
        browser.close()

scrape_behance()

When to Use

Use when content loads dynamically via JavaScript, or when you need to interact with the page (clicks, scrolls, form fills). Handles modern anti-bot detection better.

Advantages

●Executes JavaScript like a real browser
●Handles SPAs and dynamic content
●Better anti-bot evasion with stealth plugins
●Can take screenshots and PDFs

Limitations

●Slower than HTTP requests
●Higher memory/CPU usage
●More complex to set up

import scrapy
from scrapy_playwright.page import PageMethod

class BehanceSpider(scrapy.Spider):
    name = "behance"
    start_urls = ["https://www.behance.net/search/projects?field=interaction"]

    def start_requests(self):
        for url in self.start_urls:
            yield scrapy.Request(
                url,
                meta={"playwright": True, "playwright_page_methods": [
                    PageMethod("wait_for_selector", ".ProjectCover-root-167"),
                ]},
            )

    def parse(self, response):
        for project in response.css(".ProjectCover-root-167"):
            yield {
                "title": project.css(".ProjectCover-title-3_1::text").get(),
                "url": project.css("a::attr(href)").get(),
            }

When to Use

Ideal for large-scale crawling projects that need to scrape thousands of pages. Built-in support for rate limiting, retries, and data pipelines.

Advantages

●Built for scale (millions of pages)
●Automatic request throttling
●Built-in data export pipelines
●Middleware system for proxies/headers

Limitations

●Steeper learning curve
●Overkill for small projects
●No native JavaScript rendering

const puppeteer = require('puppeteer');

(async () => {
  const browser = await puppeteer.launch();
  const page = await browser.newPage();
  await page.goto('https://www.behance.net/search/projects?field=branding');
  // Ensure content is loaded
  await page.waitForSelector('.ProjectCover-content-3Z_');
  const projects = await page.evaluate(() => {
    return Array.from(document.querySelectorAll('.ProjectCover-root-167')).map(el => ({
      title: el.querySelector('.ProjectCover-title-3_1')?.innerText,
      owner: el.querySelector('.ProjectCover-username-28M')?.innerText
    }));
  });
  console.log(projects);
  await browser.close();
})();

When to Use

Choose this if you're in a Node.js/JavaScript ecosystem or need tight integration with frontend tools. Similar capabilities to Playwright.

Advantages

●Native JavaScript/TypeScript support
●Chrome DevTools Protocol access
●Large ecosystem and community
●Good for JS-heavy projects

Limitations

●Chrome-only (vs Playwright's multi-browser)
●Similar overhead to Playwright
●Less mature stealth options

How to Scrape Behance with Code

Python + Requests

import requests
from bs4 import BeautifulSoup

# Note: This will likely trigger Cloudflare if run from a data center IP
url = "https://www.behance.net/search/projects?field=graphic+design"
headers = {
    "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36"
}

try:
    response = requests.get(url, headers=headers, timeout=10)
    if response.status_code == 200:
        soup = BeautifulSoup(response.text, 'html.parser')
        # Behance renders content via JS; static scraping will find limited data
        projects = soup.find_all('div', class_='ProjectCover-root-167')
        for project in projects:
            title = project.find('a', class_='ProjectCover-title-3_1').text
            print(f"Found Project: {title}")
    else:
        print(f"Blocked or error: {response.status_code}")
except Exception as e:
    print(f"Request failed: {e}")

Python + Playwright

from playwright.sync_api import sync_playwright

def scrape_behance():
    with sync_playwright() as p:
        browser = p.chromium.launch(headless=True)
        page = browser.new_page()
        page.goto("https://www.behance.net/search/projects?field=architecture")
        # Wait for dynamic content to load
        page.wait_for_selector(".ProjectCover-root-167")
        # Scroll down to trigger lazy loading
        page.mouse.wheel(0, 5000)
        page.wait_for_timeout(2000)
        projects = page.query_selector_all(".ProjectCover-root-167")
        data = []
        for p_elem in projects:
            title = p_elem.query_selector(".ProjectCover-title-3_1").inner_text()
            owner = p_elem.query_selector(".ProjectCover-username-28M").inner_text()
            data.append({"title": title, "owner": owner})
        print(data)
        browser.close()

scrape_behance()

Python + Scrapy

import scrapy
from scrapy_playwright.page import PageMethod

class BehanceSpider(scrapy.Spider):
    name = "behance"
    start_urls = ["https://www.behance.net/search/projects?field=interaction"]

    def start_requests(self):
        for url in self.start_urls:
            yield scrapy.Request(
                url,
                meta={"playwright": True, "playwright_page_methods": [
                    PageMethod("wait_for_selector", ".ProjectCover-root-167"),
                ]},
            )

    def parse(self, response):
        for project in response.css(".ProjectCover-root-167"):
            yield {
                "title": project.css(".ProjectCover-title-3_1::text").get(),
                "url": project.css("a::attr(href)").get(),
            }

Node.js + Puppeteer

const puppeteer = require('puppeteer');

(async () => {
  const browser = await puppeteer.launch();
  const page = await browser.newPage();
  await page.goto('https://www.behance.net/search/projects?field=branding');
  // Ensure content is loaded
  await page.waitForSelector('.ProjectCover-content-3Z_');
  const projects = await page.evaluate(() => {
    return Array.from(document.querySelectorAll('.ProjectCover-root-167')).map(el => ({
      title: el.querySelector('.ProjectCover-title-3_1')?.innerText,
      owner: el.querySelector('.ProjectCover-username-28M')?.innerText
    }));
  });
  console.log(projects);
  await browser.close();
})();

What You Can Do With Behance Data

Explore practical applications and insights from Behance data.

Creative Trend Analysis

Agencies can track which creative fields and design styles are gaining the most appreciations to forecast industry trends.

How to implement:

1Scrape 5,000 top projects monthly based on specific creative fields.
2Group data by creative category and appreciation-to-view ratio.
3Visualize growth and engagement over time to identify emerging aesthetics.

Use Automatio to extract data from Behance and build these applications without writing code.

More than just prompts

Supercharge your workflow with AI Automation

Automatio combines the power of AI agents, web automation, and smart integrations to help you accomplish more in less time.

AI Agents

Web Automation

Smart Workflows

Get Started Free

Pro Tips for Scraping Behance

Expert advice for successfully extracting data from Behance.

Monitor Internal XHR

Watch the Network tab for requests to internal endpoints which often return clean JSON data.

Use Residential Proxies

Residential IPs are necessary to avoid being flagged by Cloudflare's bot management.

Handle Image Selectors

Extract high-resolution URLs from the srcset attribute rather than the default src for better quality.

Throttle Your Requests

Limit scraping to 1-2 pages per minute to avoid rapid IP bans or CAPTCHA triggers.

Emulate Human Behavior

Rotate user-agents and implement random delays between page actions to appear more human.

Testimonials

What Our Users Say

Join thousands of satisfied users who have transformed their workflow

Jonathan Kogan

Co-Founder/CEO, rpatools.io

Automatio is one of the most used for RPA Tools both internally and externally. It saves us countless hours of work and we realized this could do the same for other startups and so we choose Automatio for most of our automation needs.

Mohammed Ibrahim

CEO, qannas.pro

I have used many tools over the past 5 years, Automatio is the Jack of All trades.. !! it could be your scraping bot in the morning and then it becomes your VA by the noon and in the evening it does your automations.. its amazing!

Ben Bressington

CTO, AiChatSolutions

Automatio is fantastic and simple to use to extract data from any website. This allowed me to replace a developer and do tasks myself as they only take a few minutes to setup and forget about it. Automatio is a game changer!

Sarah Chen

Head of Growth, ScaleUp Labs

We've tried dozens of automation tools, but Automatio stands out for its flexibility and ease of use. Our team productivity increased by 40% within the first month of adoption.

David Park

Founder, DataDriven.io

The AI-powered features in Automatio are incredible. It understands context and adapts to changes in websites automatically. No more broken scrapers!

Emily Rodriguez

Marketing Director, GrowthMetrics

Automatio transformed our lead generation process. What used to take our team days now happens automatically in minutes. The ROI is incredible.

Jonathan Kogan

Co-Founder/CEO, rpatools.io

Mohammed Ibrahim

CEO, qannas.pro

Ben Bressington

CTO, AiChatSolutions

Sarah Chen

Head of Growth, ScaleUp Labs

We've tried dozens of automation tools, but Automatio stands out for its flexibility and ease of use. Our team productivity increased by 40% within the first month of adoption.

David Park

Founder, DataDriven.io

The AI-powered features in Automatio are incredible. It understands context and adapts to changes in websites automatically. No more broken scrapers!

Emily Rodriguez

Marketing Director, GrowthMetrics

Automatio transformed our lead generation process. What used to take our team days now happens automatically in minutes. The ROI is incredible.

Related Web Scraping

Frequently Asked Questions About Behance

Find answers to common questions about Behance

How to Scrape Behance: A Step-by-Step Guide for Creative Data Extraction

About Behance

Why Scrape Behance?

Scraping Challenges

Scrape Behance with AI

How It Works

Why Use AI for Scraping

How to scrape with AI:

Why use AI for scraping:

No-Code Web Scrapers for Behance

Typical Workflow with No-Code Tools

Common Challenges

No-Code Web Scrapers for Behance

Typical Workflow with No-Code Tools

Common Challenges

Code Examples

How to Scrape Behance with Code

Python + Requests

Python + Playwright

Python + Scrapy

Node.js + Puppeteer

What You Can Do With Behance Data

Creative Trend Analysis

Lead Gen for Design Tools

Recruitment Sourcing at Scale

Visual Competitor Benchmarking

What You Can Do With Behance Data

Supercharge your workflow with AI Automation

Pro Tips for Scraping Behance

Monitor Internal XHR

Use Residential Proxies

Handle Image Selectors

Throttle Your Requests

Emulate Human Behavior

What Our Users Say

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Frequently Asked Questions About Behance

Is it legal to scrape Behance?

How do I avoid getting blocked by Cloudflare on Behance?

What format is the scraped data exported in?

Does Behance have an official public API?

How often should I scrape Behance to keep data fresh?

Can I scrape images directly from Behance?

Do I need to be logged in to scrape Behance?