Pinterest Scraper

Scrape Pinterest search results and collect pins with image URLs, descriptions, and direct links using infinite scroll. Use when you want to collect visual inspiration, trend data, or product imagery from Pinterest for research, datasets, or content planning.

Pinterest Search Scraper

Scrapes pins from Pinterest search results using Crawlee's PlaywrightCrawler with infinite scroll support. Collects pin URLs, image URLs, and descriptions without requiring login.

Requirements

pip install crawlee[playwright]
playwright install chromium

Usage

python pinterest_scraper.py "search query"
python pinterest_scraper.py "minimalist home decor" 30
python pinterest_scraper.py "brutalist architecture" 100

Arguments:

• query (required): Search term (use quotes for multi-word queries)
• max_pins (optional): Maximum pins to collect, default 30

What It Returns

Each pin object contains:

Field	Description
id	Pinterest pin ID
url	Full Pinterest pin URL (https://www.pinterest.com/pin/<id>/)
image_url	Highest-resolution image URL from srcset
description	Image alt text / pin description

Output

Saved to ./storage/pinterest/<safe_query>.json:

[
  {
    "id": "123456789",
    "url": "https://www.pinterest.com/pin/123456789/",
    "image_url": "https://i.pinimg.com/736x/ab/cd/ef/...",
    "description": "Minimalist living room with white walls"
  },
  ...
]

How It Works

The scraper uses Crawlee's PlaywrightCrawler to:

1. Navigate to the Pinterest search URL: https://www.pinterest.com/search/pins/?q=<encoded_query>
2. Wait for pin elements to appear ([data-test-id="pin"] or a[href*="/pin/"])
3. Iteratively scroll to the bottom to trigger infinite load
4. Extract pins after each scroll via page.evaluate() JavaScript injection
5. Deduplicate by pin ID and collect until max_pins is reached or scrolling stalls

The JavaScript extractor resolves srcset attributes to select the highest-resolution image available:

async def _extract_pins(self, page) -> None:
    """Extract pin data from the current page state."""
    # Runs JS in the browser context to walk the DOM and extract pin data
    # Handles multiple Pinterest DOM structures (data-test-id variants)
    # Resolves srcset to get highest resolution image
    ...

Configuration

Parameter	Default	Description
max_pins	30	Maximum pins to collect
headless	True	Run browser headlessly (set False for debugging)
max_scroll_attempts	10	Stop after N consecutive scrolls with no new pins
scroll_delay	1.5s	Wait between scrolls for content to load

To run in headed mode for debugging:

scraper = PinterestScraper(max_pins=10, headless=False)

Integrating Into a Pipeline

import asyncio
from pinterest_scraper import PinterestScraper

async def collect_inspiration(query: str, limit: int = 50) -> list[dict]:
    scraper = PinterestScraper(max_pins=limit, headless=True)
    pins = await scraper.scrape_search(query)
    return pins

pins = asyncio.run(collect_inspiration("editorial fashion photography", 50))

Troubleshooting

No pins found: Pinterest occasionally changes its DOM structure. Try setting headless=False to inspect visually. The scraper attempts two selector strategies ([data-test-id="pin"] and a[href*="/pin/"]).

Fewer pins than expected: Pinterest's infinite scroll depends on scroll velocity and network speed. Increase max_scroll_attempts in scrape_search() or add a longer asyncio.sleep() after each scroll.

Playwright install error: Run playwright install chromium to download the browser binary. If behind a corporate proxy, set PLAYWRIGHT_BROWSERS_PATH to a writable directory.

Rate limiting / CAPTCHA: Pinterest may show a CAPTCHA after many rapid requests. Add delays between scraper runs or rotate residential IPs.

Rate Limiting Guidelines

• Wait 5+ seconds between search queries when running multiple
• Avoid scraping more than 300-500 pins per hour from a single IP
• Pinterest does not require login for search, but aggressive scraping triggers bot detection

Use Cases

• Visual trend research: Collect images around a topic to identify visual patterns
• Dataset creation: Build image datasets for computer vision or aesthetic scoring models
• Content planning: Find top-performing visuals in a niche to guide creative direction
• Competitive research: Scrape brand-related queries to see what imagery dominates a category
• Mood board generation: Automate collection of reference images for design projects