Behance Portfolio Scraping: Extract Creative Projects and Designer Profiles

Behance is the world's largest platform for showcasing and discovering creative work. Owned by Adobe, it hosts millions of projects spanning graphic design, illustration, photography, UI/UX, motion graphics, and dozens of other creative disciplines. For data analysts, recruiters, market researchers, and creative agencies, Behance represents an incredibly rich dataset of creative talent and design trends.

In this comprehensive guide, we'll explore Behance's public data structure, build scrapers in both Python and Node.js to extract project and profile data, and show how to scale your collection using Apify's cloud platform.

Understanding Behance's Data Structure

Behance organizes its content around two primary entities: Projects and Profiles. Understanding how these relate to each other is crucial for building effective scrapers.

Projects

Every creative work on Behance is a "project." Projects contain:

• Title — the name of the creative work
• Cover image — the primary thumbnail displayed in galleries
• Project modules — individual sections that can contain images, text blocks, embedded videos, or audio
• Appreciation count — Behance's equivalent of "likes" (the blue thumbs-up)
• View count — total number of times the project has been viewed
• Comment count — number of comments left by other users
• Tags — creator-applied labels describing the work
• Creative fields — broader categories like "Graphic Design", "Illustration", "Photography"
• Tools used — software and tools used to create the work (Photoshop, Figma, Blender, etc.)
• Published date — when the project was first shared
• Color palette — dominant colors extracted from the project images
• Owner profile — link back to the creator's profile

Designer Profiles

Each Behance user has a profile page containing:

• Display name — the creator's name or studio name
• Username/URL — their unique Behance identifier
• Location — city and country
• Bio/About — text description of their background and expertise
• Creative fields — their declared areas of expertise
• Follower count — number of users following them
• Following count — number of users they follow
• Appreciation total — cumulative appreciations across all projects
• View total — cumulative views across all projects
• Project count — number of published projects
• Member since — account creation date
• Social links — connected external profiles (portfolio site, Instagram, Dribbble, LinkedIn)
• Work experience — employment history (if filled in)
• Featured projects — pinned/highlighted work
• Profile stats — engagement metrics visible on the profile

Creative Categories

Behance organizes content into well-defined creative fields:

Category	Sub-categories
Graphic Design	Branding, Typography, Print, Packaging
UI/UX	Web Design, Mobile Apps, Interaction Design
Illustration	Digital, Traditional, Character Design
Photography	Portrait, Product, Street, Landscape
Motion Graphics	Animation, 3D, VFX, Title Sequences
Architecture	Interior Design, Visualization, Urban
Fashion	Apparel, Accessories, Textile Design
Advertising	Campaigns, Social Media, Outdoor
Fine Arts	Painting, Sculpture, Mixed Media
Game Design	Concept Art, 3D Modeling, Level Design

Each category page on Behance shows trending projects, and these pages are publicly accessible, making them excellent entry points for scraping.

Behance's Public URL Structure

Understanding URL patterns is essential for systematic scraping:

# User profiles
https://www.behance.net/{username}

# Individual projects
https://www.behance.net/gallery/{project_id}/{slug}

# Category/field browsing
https://www.behance.net/search/projects?field=graphic+design

# Curated galleries
https://www.behance.net/galleries/graphic-design

# Search results
https://www.behance.net/search/projects?search={query}

# Moodboards
https://www.behance.net/{username}/moodboards

# Appreciated projects
https://www.behance.net/{username}/appreciated

Setting Up Your Scraping Environment

Python Setup

# requirements.txt
requests==2.31.0
beautifulsoup4==4.12.3
lxml==5.1.0
apify-client==1.8.1

pip install requests beautifulsoup4 lxml apify-client

Node.js Setup

npm init -y
npm install axios cheerio crawlee apify-client

Building a Behance Project Scraper

Python Implementation

import requests
from bs4 import BeautifulSoup
import json
import time
import random
import re

class BehanceProjectScraper:
    """Scraper for publicly accessible Behance project data."""

    BASE_URL = "https://www.behance.net"

    def __init__(self):
        self.session = requests.Session()
        self.session.headers.update({
            "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) "
                          "AppleWebKit/537.36 (KHTML, like Gecko) "
                          "Chrome/120.0.0.0 Safari/537.36",
            "Accept-Language": "en-US,en;q=0.9",
            "Accept": "text/html,application/xhtml+xml"
        })

    def scrape_project(self, project_url: str) -> dict:
        """Extract metadata from a Behance project page."""

        try:
            response = self.session.get(
                project_url, timeout=15
            )
            response.raise_for_status()
        except requests.RequestException as e:
            return {"error": str(e), "url": project_url}

        soup = BeautifulSoup(response.text, "lxml")
        data = {"url": project_url}

        # Extract Open Graph metadata
        og_fields = {
            "title": "og:title",
            "description": "og:description",
            "image": "og:image",
            "type": "og:type",
        }

        for key, prop in og_fields.items():
            tag = soup.find("meta", property=prop)
            data[key] = tag["content"] if tag else None

        # Extract JSON-LD structured data
        json_ld_tags = soup.find_all(
            "script", type="application/ld+json"
        )

        for script in json_ld_tags:
            try:
                ld_data = json.loads(script.string)
                if isinstance(ld_data, dict):
                    data["structured_data"] = ld_data

                    # Extract creator info
                    author = ld_data.get("author", {})
                    if isinstance(author, dict):
                        data["creator_name"] = author.get("name")
                        data["creator_url"] = author.get("url")

                    data["date_published"] = ld_data.get(
                        "datePublished"
                    )
                    data["date_modified"] = ld_data.get(
                        "dateModified"
                    )

                    # Interaction statistics
                    interactions = ld_data.get(
                        "interactionStatistic", []
                    )
                    for stat in interactions:
                        stat_type = stat.get(
                            "interactionType", {}
                        )
                        type_name = stat_type.get("@type", "")

                        if "Like" in type_name:
                            data["appreciations"] = stat.get(
                                "userInteractionCount", 0
                            )
                        elif "Comment" in type_name:
                            data["comments"] = stat.get(
                                "userInteractionCount", 0
                            )
                        elif "View" in type_name or "Watch" in type_name:
                            data["views"] = stat.get(
                                "userInteractionCount", 0
                            )

                    data["keywords"] = ld_data.get(
                        "keywords", []
                    )

            except (json.JSONDecodeError, TypeError):
                continue

        # Extract project-specific elements from the page
        # Tools used
        tools_section = soup.find_all(
            "a", href=re.compile(r"/search/projects\?tools=")
        )
        data["tools_used"] = [
            t.get_text(strip=True) for t in tools_section
        ]

        # Creative fields
        fields_section = soup.find_all(
            "a", href=re.compile(r"/search/projects\?field=")
        )
        data["creative_fields"] = [
            f.get_text(strip=True) for f in fields_section
        ]

        # Project images (cover and module images)
        project_images = []
        img_tags = soup.find_all("img", src=re.compile(
            r"behance\.net/.*\.(jpg|png|webp)"
        ))
        for img in img_tags:
            src = img.get("src") or img.get("data-src")
            if src and "project_modules" in src:
                project_images.append(src)
        data["image_count"] = len(project_images)
        data["images"] = project_images[:10]

        return data

    def search_projects(
        self, query: str, field: str = None, max_pages: int = 3
    ) -> list:
        """Search Behance projects and collect URLs."""

        results = []

        for page in range(1, max_pages + 1):
            params = {"search": query, "page": page}
            if field:
                params["field"] = field

            search_url = f"{self.BASE_URL}/search/projects"

            try:
                resp = self.session.get(
                    search_url, params=params, timeout=15
                )
                soup = BeautifulSoup(resp.text, "lxml")

                # Find project links
                links = soup.find_all(
                    "a", href=re.compile(r"/gallery/\d+/")
                )

                for link in links:
                    href = link.get("href", "")
                    full_url = (
                        f"{self.BASE_URL}{href}"
                        if href.startswith("/")
                        else href
                    )
                    if full_url not in results:
                        results.append(full_url)

            except requests.RequestException:
                continue

            time.sleep(random.uniform(2, 4))

        return results

    def scrape_search_results(
        self, query: str, field: str = None,
        max_projects: int = 20
    ) -> list:
        """Search and scrape Behance projects."""

        project_urls = self.search_projects(query, field)
        project_urls = project_urls[:max_projects]

        results = []
        for i, url in enumerate(project_urls):
            print(
                f"Scraping {i+1}/{len(project_urls)}: {url}"
            )
            result = self.scrape_project(url)
            results.append(result)
            time.sleep(random.uniform(1.5, 3.5))

        return results


# Usage example
if __name__ == "__main__":
    scraper = BehanceProjectScraper()

    # Scrape trending UI/UX projects
    results = scraper.scrape_search_results(
        query="mobile app design",
        field="interaction design",
        max_projects=10
    )

    for project in results:
        print(f"\nTitle: {project.get('title', 'N/A')}")
        print(f"Creator: {project.get('creator_name', 'N/A')}")
        appreciations = project.get('appreciations', 0)
        print(f"Appreciations: {appreciations}")
        views = project.get('views', 0)
        print(f"Views: {views}")
        tools = ', '.join(project.get('tools_used', []))
        print(f"Tools: {tools or 'N/A'}")
        fields = ', '.join(
            project.get('creative_fields', [])
        )
        print(f"Fields: {fields or 'N/A'}")

Node.js Implementation

const axios = require('axios');
const cheerio = require('cheerio');

class BehanceProjectScraper {
  constructor() {
    this.baseUrl = 'https://www.behance.net';
    this.headers = {
      'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) '
        + 'AppleWebKit/537.36 Chrome/120.0.0.0 Safari/537.36',
      'Accept-Language': 'en-US,en;q=0.9',
    };
  }

  async scrapeProject(projectUrl) {
    try {
      const { data: html } = await axios.get(projectUrl, {
        headers: this.headers,
        timeout: 15000,
      });

      const $ = cheerio.load(html);
      const result = { url: projectUrl };

      // Open Graph data
      result.title = $('meta[property="og:title"]')
        .attr('content');
      result.description = $('meta[property="og:description"]')
        .attr('content');
      result.image = $('meta[property="og:image"]')
        .attr('content');

      // JSON-LD structured data
      $('script[type="application/ld+json"]').each((_, el) => {
        try {
          const ldData = JSON.parse($(el).html());
          if (ldData && typeof ldData === 'object') {
            result.structuredData = ldData;

            // Author info
            const author = ldData.author || {};
            result.creatorName = author.name;
            result.creatorUrl = author.url;

            result.datePublished = ldData.datePublished;
            result.keywords = ldData.keywords || [];

            // Interaction stats
            const stats = ldData.interactionStatistic || [];
            stats.forEach(stat => {
              const type = (
                stat.interactionType || {}
              )['@type'] || '';
              if (type.includes('Like')) {
                result.appreciations =
                  stat.userInteractionCount || 0;
              } else if (type.includes('Comment')) {
                result.comments =
                  stat.userInteractionCount || 0;
              } else if (type.includes('View') ||
                         type.includes('Watch')) {
                result.views =
                  stat.userInteractionCount || 0;
              }
            });
          }
        } catch (e) {
          // Skip invalid JSON-LD
        }
      });

      // Tools used
      result.toolsUsed = [];
      $('a[href*="search/projects?tools="]').each((_, el) => {
        result.toolsUsed.push($(el).text().trim());
      });

      // Creative fields
      result.creativeFields = [];
      $('a[href*="search/projects?field="]').each((_, el) => {
        result.creativeFields.push($(el).text().trim());
      });

      return result;
    } catch (error) {
      return { error: error.message, url: projectUrl };
    }
  }

  async scrapeMultiple(urls, delayMs = 2500) {
    const results = [];
    for (let i = 0; i < urls.length; i++) {
      console.log(
        `Scraping ${i + 1}/${urls.length}: ${urls[i]}`
      );
      const result = await this.scrapeProject(urls[i]);
      results.push(result);

      if (i < urls.length - 1) {
        await new Promise(r => setTimeout(r, delayMs));
      }
    }
    return results;
  }
}

// Usage
(async () => {
  const scraper = new BehanceProjectScraper();

  const projectUrls = [
    'https://www.behance.net/gallery/123456789/example-project',
    'https://www.behance.net/gallery/987654321/another-project',
  ];

  const results = await scraper.scrapeMultiple(projectUrls);

  results.forEach(r => {
    console.log(`\nTitle: ${r.title || 'N/A'}`);
    console.log(`Creator: ${r.creatorName || 'N/A'}`);
    console.log(`Appreciations: ${r.appreciations || 0}`);
    console.log(`Tools: ${(r.toolsUsed || []).join(', ')}`);
  });
})();

Building a Behance Profile Scraper

Python Profile Extractor

class BehanceProfileScraper:
    """Extract data from public Behance user profiles."""

    BASE_URL = "https://www.behance.net"

    def __init__(self):
        self.session = requests.Session()
        self.session.headers.update({
            "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) "
                          "AppleWebKit/537.36 Chrome/120.0.0.0",
            "Accept-Language": "en-US,en;q=0.9",
        })

    def scrape_profile(self, username: str) -> dict:
        """Extract public profile data for a Behance user."""

        profile_url = f"{self.BASE_URL}/{username}"

        try:
            response = self.session.get(
                profile_url, timeout=15
            )
            response.raise_for_status()
        except requests.RequestException as e:
            return {"error": str(e), "username": username}

        soup = BeautifulSoup(response.text, "lxml")
        data = {"username": username, "url": profile_url}

        # Open Graph data
        data["display_name"] = self._get_og(soup, "og:title")
        data["bio"] = self._get_og(soup, "og:description")
        data["avatar"] = self._get_og(soup, "og:image")

        # JSON-LD structured data
        for script in soup.find_all(
            "script", type="application/ld+json"
        ):
            try:
                ld = json.loads(script.string)
                if isinstance(ld, dict):
                    data["type"] = ld.get("@type")
                    data["name"] = ld.get("name")
                    data["location"] = self._extract_location(ld)
                    data["job_title"] = ld.get("jobTitle")
                    data["works_for"] = self._extract_org(ld)

                    # Social links
                    data["same_as"] = ld.get("sameAs", [])

                    # Followers
                    followers = ld.get(
                        "interactionStatistic", []
                    )
                    for stat in followers:
                        itype = stat.get(
                            "interactionType", {}
                        ).get("@type", "")
                        if "Follow" in itype:
                            data["followers"] = stat.get(
                                "userInteractionCount", 0
                            )

            except (json.JSONDecodeError, TypeError):
                continue

        # Extract project links from profile page
        project_links = soup.find_all(
            "a", href=re.compile(r"/gallery/\d+/")
        )
        data["project_urls"] = list(set(
            f"{self.BASE_URL}{a['href']}"
            if a["href"].startswith("/")
            else a["href"]
            for a in project_links
        ))
        data["project_count"] = len(data["project_urls"])

        # Extract creative fields
        field_links = soup.find_all(
            "a", href=re.compile(
                r"/search/users\?field="
            )
        )
        data["creative_fields"] = [
            f.get_text(strip=True) for f in field_links
        ]

        # Extract stats from meta or visible elements
        stat_elements = soup.find_all(
            class_=re.compile(r"stat|count|metric")
        )
        for elem in stat_elements:
            text = elem.get_text(strip=True).lower()
            if "appreciation" in text:
                nums = re.findall(r"[\d,]+", text)
                if nums:
                    data["total_appreciations"] = int(
                        nums[0].replace(",", "")
                    )
            elif "view" in text:
                nums = re.findall(r"[\d,]+", text)
                if nums:
                    data["total_views"] = int(
                        nums[0].replace(",", "")
                    )

        return data

    def _get_og(self, soup, prop):
        tag = soup.find("meta", property=prop)
        return tag["content"] if tag else None

    def _extract_location(self, ld_data):
        address = ld_data.get("address", {})
        if isinstance(address, dict):
            city = address.get("addressLocality", "")
            country = address.get("addressCountry", "")
            return f"{city}, {country}".strip(", ")
        return None

    def _extract_org(self, ld_data):
        org = ld_data.get("worksFor", {})
        if isinstance(org, dict):
            return org.get("name")
        return None

    def scrape_multiple_profiles(
        self, usernames: list
    ) -> list:
        """Scrape multiple Behance profiles."""
        results = []
        for i, username in enumerate(usernames):
            print(
                f"Scraping profile {i+1}/"
                f"{len(usernames)}: {username}"
            )
            result = self.scrape_profile(username)
            results.append(result)
            time.sleep(random.uniform(2, 4))
        return results


# Usage
if __name__ == "__main__":
    scraper = BehanceProfileScraper()

    profiles = scraper.scrape_multiple_profiles([
        "adobe",
        "MikeCreative",
        "spotify",
    ])

    for profile in profiles:
        print(f"\nName: {profile.get('display_name', 'N/A')}")
        print(f"Location: {profile.get('location', 'N/A')}")
        print(f"Followers: {profile.get('followers', 'N/A')}")
        count = profile.get('project_count', 0)
        print(f"Projects: {count}")
        fields = ', '.join(
            profile.get('creative_fields', [])
        )
        print(f"Fields: {fields or 'N/A'}")

Analyzing Tool Popularity and Appreciation Patterns

Once you have scraped data, you can analyze design tool trends and engagement patterns:

from collections import Counter

def analyze_tool_trends(projects: list) -> dict:
    """Analyze which creative tools are most popular."""

    tool_counter = Counter()
    tool_appreciations = {}

    for project in projects:
        tools = project.get("tools_used", [])
        appreciations = project.get("appreciations", 0)

        for tool in tools:
            tool_counter[tool] += 1
            if tool not in tool_appreciations:
                tool_appreciations[tool] = []
            tool_appreciations[tool].append(appreciations)

    # Calculate average appreciations per tool
    tool_stats = {}
    for tool, counts in tool_appreciations.items():
        tool_stats[tool] = {
            "usage_count": tool_counter[tool],
            "avg_appreciations": sum(counts) / len(counts),
            "max_appreciations": max(counts),
            "total_projects": len(counts),
        }

    # Sort by usage
    sorted_tools = sorted(
        tool_stats.items(),
        key=lambda x: x[1]["usage_count"],
        reverse=True
    )

    return dict(sorted_tools)


def analyze_field_engagement(projects: list) -> dict:
    """Analyze engagement rates across creative fields."""

    field_data = {}

    for project in projects:
        fields = project.get("creative_fields", [])
        views = project.get("views", 0)
        appreciations = project.get("appreciations", 0)

        for field in fields:
            if field not in field_data:
                field_data[field] = {
                    "total_views": 0,
                    "total_appreciations": 0,
                    "project_count": 0,
                }

            field_data[field]["total_views"] += views
            field_data[field]["total_appreciations"] += (
                appreciations
            )
            field_data[field]["project_count"] += 1

    # Calculate engagement rates
    for field, stats in field_data.items():
        if stats["total_views"] > 0:
            stats["engagement_rate"] = (
                stats["total_appreciations"] /
                stats["total_views"] * 100
            )
        else:
            stats["engagement_rate"] = 0

        stats["avg_views"] = (
            stats["total_views"] / stats["project_count"]
        )
        stats["avg_appreciations"] = (
            stats["total_appreciations"] /
            stats["project_count"]
        )

    return field_data

Scaling with Apify

For large-scale Behance data collection, Apify handles the infrastructure complexity:

Python Apify Integration

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")

# Scrape Behance projects at scale
run_input = {
    "searchQueries": [
        "brand identity",
        "mobile app design",
        "logo design",
        "packaging design"
    ],
    "maxProjectsPerQuery": 200,
    "includeProfileData": True,
    "includeToolsUsed": True,
    "includeAppreciations": True,
    "proxyConfiguration": {
        "useApifyProxy": True,
        "apifyProxyGroups": ["RESIDENTIAL"]
    }
}

run = client.actor("behance-portfolio-scraper").call(
    run_input=run_input
)

# Get results
items = client.dataset(
    run["defaultDatasetId"]
).list_items().items

print(f"Scraped {len(items)} Behance projects")

# Analyze results
for item in items[:5]:
    print(f"\n  Project: {item.get('title', 'N/A')}")
    print(f"  Creator: {item.get('creatorName', 'N/A')}")
    print(f"  Appreciations: {item.get('appreciations', 0)}")
    print(f"  Views: {item.get('views', 0)}")
    tools = ', '.join(item.get('toolsUsed', []))
    print(f"  Tools: {tools}")

Node.js Apify Integration

const { ApifyClient } = require('apify-client');

const client = new ApifyClient({
  token: 'YOUR_APIFY_TOKEN',
});

async function scrapeBehanceAtScale() {
  const run = await client.actor('behance-portfolio-scraper')
    .call({
      searchQueries: [
        'UI design',
        'illustration',
        'motion graphics',
      ],
      maxProjectsPerQuery: 150,
      includeProfileData: true,
      includeToolsUsed: true,
    });

  const { items } = await client
    .dataset(run.defaultDatasetId)
    .listItems();

  console.log(`Scraped ${items.length} projects`);

  // Export options
  const jsonUrl = 'https://api.apify.com/v2/datasets/'
    + `${run.defaultDatasetId}/items?format=json`;
  const csvUrl = 'https://api.apify.com/v2/datasets/'
    + `${run.defaultDatasetId}/items?format=csv`;

  console.log(`JSON export: ${jsonUrl}`);
  console.log(`CSV export: ${csvUrl}`);

  return items;
}

scrapeBehanceAtScale();

Practical Use Cases for Behance Data

1. Design Trend Analysis

Track which visual styles, color palettes, and creative tools are trending across the design community. Monitor how trends shift quarter over quarter by tracking appreciation patterns and tool usage across categories.

2. Talent Recruitment

Creative agencies and HR teams can build databases of designers filtered by skill, tool proficiency, location, and engagement metrics. A designer with 50,000 appreciations and expertise in Figma and After Effects is a very different candidate than someone with 500 appreciations using only Canva.

3. Competitive Intelligence for Design Tools

Software companies like Adobe, Figma, Sketch, and Canva can track which tools designers actually use by analyzing the "Tools Used" tags across thousands of projects. This provides real market share data that surveys can't match.

4. Design Education and Curriculum Planning

Educational institutions can analyze which skills and tools produce the highest-engagement work, helping them prioritize what to teach. If projects using Blender consistently outperform those using older 3D tools, that signals a curriculum update.

5. Market Research for Creative Services

Agencies can analyze what types of creative work get the most engagement in specific industries or regions, helping them position their services and pricing.

6. Portfolio Benchmarking

Designers can scrape competitor profiles to understand what engagement levels are typical in their field and geographic area, helping them benchmark their own portfolio performance.

Handling Behance's Anti-Scraping Protections

Behance uses several mechanisms to prevent aggressive scraping:

JavaScript Rendering

Many Behance pages rely on client-side JavaScript rendering. For these pages, you'll need a headless browser:

from playwright.sync_api import sync_playwright

def scrape_with_browser(url: str) -> str:
    """Use Playwright for JS-rendered Behance pages."""

    with sync_playwright() as p:
        browser = p.chromium.launch(headless=True)
        page = browser.new_page()

        page.set_extra_http_headers({
            "Accept-Language": "en-US,en;q=0.9"
        })

        page.goto(url, wait_until="networkidle")

        # Wait for content to render
        page.wait_for_selector(
            "[class*='Project']", timeout=10000
        )

        content = page.content()
        browser.close()

        return content

Rate Limiting Best Practices

import time
import random

class RateLimiter:
    """Respectful rate limiter for Behance scraping."""

    def __init__(
        self, min_delay=2.0, max_delay=5.0,
        burst_limit=10, burst_cooldown=30
    ):
        self.min_delay = min_delay
        self.max_delay = max_delay
        self.burst_limit = burst_limit
        self.burst_cooldown = burst_cooldown
        self.request_count = 0
        self.last_request_time = 0

    def wait(self):
        """Wait an appropriate amount before next request."""
        self.request_count += 1

        # Burst protection
        if self.request_count % self.burst_limit == 0:
            print(
                f"Burst limit reached. Cooling down "
                f"for {self.burst_cooldown}s..."
            )
            time.sleep(self.burst_cooldown)

        # Standard delay
        elapsed = time.time() - self.last_request_time
        delay = random.uniform(
            self.min_delay, self.max_delay
        )

        if elapsed < delay:
            time.sleep(delay - elapsed)

        self.last_request_time = time.time()

Data Storage and Export

Once you've collected Behance data, you'll want to store it efficiently:

import json
import csv
from datetime import datetime

def export_to_json(data: list, filename: str = None):
    """Export scraped data to JSON."""
    if not filename:
        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
        filename = f"behance_data_{timestamp}.json"

    with open(filename, "w", encoding="utf-8") as f:
        json.dump(data, f, indent=2, ensure_ascii=False)

    print(f"Exported {len(data)} records to {filename}")


def export_to_csv(data: list, filename: str = None):
    """Export scraped data to CSV."""
    if not data:
        return

    if not filename:
        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
        filename = f"behance_data_{timestamp}.csv"

    # Flatten nested data
    flat_data = []
    for item in data:
        flat_item = {}
        for key, value in item.items():
            if isinstance(value, list):
                flat_item[key] = "; ".join(str(v) for v in value)
            elif isinstance(value, dict):
                flat_item[key] = json.dumps(value)
            else:
                flat_item[key] = value
        flat_data.append(flat_item)

    keys = set()
    for item in flat_data:
        keys.update(item.keys())

    with open(filename, "w", newline="", encoding="utf-8") as f:
        writer = csv.DictWriter(f, fieldnames=sorted(keys))
        writer.writeheader()
        writer.writerows(flat_data)

    print(f"Exported {len(data)} records to {filename}")

Legal and Ethical Considerations

When scraping Behance data, keep these principles in mind:

• Respect robots.txt — Check and follow Behance's crawling directives
• Honor rate limits — Behance serves millions of creators; don't degrade their experience with aggressive scraping
• Public data only — Only collect data that's visible without authentication
• Attribution matters — If you publish analysis based on Behance data, credit the platform and creators appropriately
• No image redistribution — Scraping metadata is one thing; republishing creators' actual artwork without permission is copyright infringement
• GDPR considerations — If you're collecting data about EU-based designers, ensure your data handling complies with GDPR requirements
• Review Behance's Terms — Adobe's terms of service govern automated access to Behance

Conclusion

Behance is a goldmine for anyone interested in creative industry data. From tracking design tool trends to building talent databases, the platform's public data offers insights that are difficult to find elsewhere.

The scrapers in this guide give you a solid foundation for extracting project metadata, profile information, engagement metrics, and tool usage data. For small-scale research, the Python and Node.js implementations handle everything you need. When you're ready to scale to thousands of profiles and projects across all creative fields, Apify's cloud infrastructure takes over the heavy lifting.

Start with a specific question — "Which design tools produce the highest-engagement work in 2026?" or "What's the average portfolio size for a senior UI designer in Berlin?" — and let the data guide your analysis. The creative industry is one of the most visible communities online, and Behance puts that visibility at your fingertips.