scrapebadger 0.15.0

Official Python SDK for ScrapeBadger - Async web scraping APIs for Twitter and more

ScrapeBadger Python SDK

The official Python SDK for ScrapeBadger - async web scraping APIs for Twitter, Google, Vinted, Reddit, and more.

Features

• Async-first - Built with asyncio for high-performance concurrent scraping
• Type-safe - Full type hints and Pydantic models for all responses
• Automatic pagination - Iterator methods with smart rate limit handling
• Resilient retries - Exponential backoff on transient errors
• 37+ Twitter endpoints - Tweets, users, lists, communities, trends, geo, real-time streams
• 19 Google product APIs - Search (with optional deferred AI Overview follow-up), Maps, News, Hotels, Trends (incl. topic autocomplete), Jobs, Shopping (+ merchant URL enrichment), Patents, Scholar (search + profiles + author + author citation + cite formats), Images, Videos, Finance, AI Mode, Lens, Local Pack, Shorts, Flights, Products
• Vinted scraping - Search items, item details, user profiles, brands, colors, markets
• Reddit scraping - Search posts/subreddits/users/domains, subreddit posts, post comments, user profiles, trophies, wiki pages, moderators
• Web scraping - Anti-bot bypass, JS rendering, and AI data extraction

Installation

pip install scrapebadger

Or with uv:

uv add scrapebadger

Quick Start

import asyncio
from scrapebadger import ScrapeBadger

async def main():
    async with ScrapeBadger(api_key="your-api-key") as client:
        # Get a user profile
        user = await client.twitter.users.get_by_username("elonmusk")
        print(f"{user.name} has {user.followers_count:,} followers")

        # Scrape a website
        result = await client.web.scrape("https://scrapebadger.com", format="markdown")
        print(result.content)

        # Search tweets
        tweets = await client.twitter.tweets.search("python programming")
        for tweet in tweets.data:
            print(f"@{tweet.username}: {tweet.text[:100]}...")

asyncio.run(main())

Authentication

Get your API key from scrapebadger.com and pass it to the client:

from scrapebadger import ScrapeBadger

client = ScrapeBadger(api_key="sb_live_xxxxxxxxxxxxx")

You can also set the SCRAPEBADGER_API_KEY environment variable:

export SCRAPEBADGER_API_KEY="sb_live_xxxxxxxxxxxxx"

Available APIs

API	Description	Documentation
Web Scraping	Scrape any website with JS rendering, anti-bot bypass, and AI extraction	Web Scraping Guide
Twitter	37+ endpoints for tweets, users, lists, communities, trends, and real-time streams	Twitter Guide
Google	19 products — Search, Maps, News, Hotels, Trends, Jobs, Shopping, Patents, Scholar, Images, Videos, Finance, AI Mode, Lens, Autocomplete, Local, Shorts, Flights, Products	Google Guide
Vinted	Search items, item details, user profiles, brands, colors, statuses, and markets	Vinted Guide
Reddit	Search posts, subreddits, users, and domains; fetch post comments, subreddit rules, moderators, wiki pages, user trophies	Reddit Guide
Amazon	14 endpoints — search, autocomplete, product detail, offers, reviews, bestsellers, new releases, deals, category browse, seller profile/products/feedback, markets, categories	Amazon Guide
eBay	12 endpoints across 18 markets — search, completed/sold search, item detail, item reviews, seller profile/items/feedback, category browse, categories, autocomplete, markets	eBay Guide
TikTok	25 endpoints — user profile/videos/followers/following/liked/reposts, video detail/comments/replies/related/transcript/oEmbed, search (general/videos/hashtags/users), music detail/videos, hashtag detail/videos, trending videos/hashtags/songs, ad library, regions	TikTok Guide

Error Handling

from scrapebadger import (
    ScrapeBadger,
    ScrapeBadgerError,
    AuthenticationError,
    RateLimitError,
    InsufficientCreditsError,
    NotFoundError,
    ValidationError,
    ServerError,
)

async with ScrapeBadger(api_key="your-key") as client:
    try:
        user = await client.twitter.users.get_by_username("elonmusk")
    except AuthenticationError:
        print("Invalid API key")
    except RateLimitError as e:
        print(f"Rate limited. Retry after {e.retry_after} seconds")
        print(f"Limit: {e.limit}, Remaining: {e.remaining}")
    except InsufficientCreditsError:
        print("Out of credits! Purchase more at scrapebadger.com")
    except NotFoundError:
        print("User not found")
    except ValidationError as e:
        print(f"Invalid parameters: {e}")
    except ServerError:
        print("Server error, try again later")
    except ScrapeBadgerError as e:
        print(f"API error: {e}")

Configuration

Custom Timeout and Retries

from scrapebadger import ScrapeBadger

client = ScrapeBadger(
    api_key="your-key",
    timeout=120.0,      # Request timeout in seconds (default: 300)
    max_retries=5,      # Retry attempts (default: 10)
)

Advanced Configuration

from scrapebadger import ScrapeBadger
from scrapebadger._internal import ClientConfig

config = ClientConfig(
    api_key="your-key",
    base_url="https://scrapebadger.com",
    timeout=300.0,
    connect_timeout=10.0,
    max_retries=10,
    retry_on_status=(502, 503, 504),
    headers={"X-Custom-Header": "value"},
)

client = ScrapeBadger(config=config)

Retry Behavior

The SDK automatically retries requests that fail with 502, 503, or 504 status codes using exponential backoff (1s, 2s, 4s, 8s, ...). Each retry logs a warning:

⚠ 503 Service Unavailable — retrying in 4s (attempt 3/10)

To see these warnings, configure Python logging:

import logging
logging.basicConfig(level=logging.WARNING)

Rate Limit Aware Pagination

When using *_all pagination methods, the SDK reads X-RateLimit-Remaining and X-RateLimit-Reset headers from each response. When remaining requests drop below 20% of your tier's limit, pagination automatically slows down to spread requests across the remaining window — preventing 429 errors. A warning is logged when throttling activates:

⚠ Rate limit: 25/300 remaining (resets in 42s), throttling pagination to ~0.6 req/s

This works transparently with all tier levels (Free: 60/min, Basic: 300/min, Pro: 1000/min, Enterprise: 5000/min).

Development

Setup

# Clone the repository
git clone https://github.com/scrape-badger/scrapebadger-python.git
cd scrapebadger-python

# Install dependencies with uv
uv sync --dev

# Install pre-commit hooks
uv run pre-commit install

Running Tests

# Run all tests
uv run pytest

# Run with coverage
uv run pytest --cov=src/scrapebadger --cov-report=html

# Run specific tests
uv run pytest tests/test_client.py -v

Code Quality

# Lint
uv run ruff check src/ tests/

# Format
uv run ruff format src/ tests/

# Type check
uv run mypy src/

# All checks
uv run ruff check src/ tests/ && uv run ruff format --check src/ tests/ && uv run mypy src/

Contributing

Contributions are welcome! Please read our Contributing Guide for details.

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes
4. Run tests and linting (uv run pytest && uv run ruff check)
5. Commit your changes (git commit -m 'Add amazing feature')
6. Push to the branch (git push origin feature/amazing-feature)
7. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Support

• Documentation: docs.scrapebadger.com
• Issues: GitHub Issues
• Email: [e-mail]
• Discord: Join our community

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