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BSD-3-Clause 6 8 3,087
44.4 thousand (month) Sep 04 2013 1.6.0(2025-07-22 06:00:53 ago)
36,206 2 7 BSD-3-Clause
Aug 01 2024 397.4 thousand (month) 0.4.5(2026-04-07 04:22:27 ago)

Scrapyd is a service for running Scrapy spiders. It allows you to schedule spiders to run at regular intervals and also allows you to run spiders on remote machines. It is built in Python, and it is meant to be used in a server-client architecture, where the scrapyd server runs on a remote machine, and clients can schedule and control spider runs on the server using an HTTP API. With Scrapyd, you can schedule spider runs on a regular basis, schedule spider runs on demand, and view the status of running spiders.

You can also see the logs of completed spiders, and manage spider settings and configurations. Scrapyd also provides an API that allows you to schedule spider runs, cancel spider runs, and view the status of running spiders. You can install the package via pip by running pip install scrapyd and then you can run the package by running scrapyd command in your command prompt. By default, it will start a web server on port 6800, but you can specify a different port using the `--port`` option.

Scrapyd is a good solution if you need to run Scrapy spiders on a remote machine, or if you need to schedule spider runs on a regular basis. It's also useful if you have multiple spiders, and you need a way to manage and monitor them all in one place.

for more web interface see scrapydweb

Scrapling is an adaptive web scraping framework for Python that introduces "self-healing" selectors — selectors that can track and find elements even when the website's DOM structure changes. This solves one of the biggest maintenance headaches in web scraping: broken selectors after website updates.

Key features include:

  • Self-healing selectors Scrapling uses smart element matching that can identify target elements even after the page structure changes. It builds a fingerprint of the element based on multiple attributes (text, position, siblings, attributes) and uses fuzzy matching to relocate it.
  • Multiple parsing backends Supports different parsing engines including lxml (fast) and a custom engine, allowing you to choose the right balance of speed and features.
  • Scrapy-like Spider API Provides a familiar Spider class pattern for organizing crawling logic, similar to Scrapy but with the added benefit of adaptive selectors.
  • CSS and XPath selectors Full support for CSS selectors and XPath, plus the adaptive matching system on top.
  • Type hints and modern Python Built with full type annotations and 92% test coverage for reliability.
  • Async support Supports asynchronous crawling for efficient concurrent scraping.

Scrapling gained massive traction in 2025 as one of the most starred new Python scraping libraries. It is particularly useful for scraping targets that frequently update their HTML structure, where traditional selector-based scrapers would break.

Highlights


css-selectorsxpathfastpopular

Example Use


```shell $ scrapyd $ curl http://localhost:6800/schedule.json -d project=myproject -d spider=spider2 ```
```python from scrapling import Fetcher, StealthFetcher, PlayWrightFetcher # Simple fetching with adaptive parsing fetcher = Fetcher() page = fetcher.get("https://example.com/products") # CSS selectors work as expected products = page.css(".product-card") for product in products: name = product.css_first(".name").text() price = product.css_first(".price").text() print(f"{name}: {price}") # Adaptive selector - finds the element even if DOM changes # Uses element fingerprinting for resilient matching element = page.find("Product Title", auto_match=True) # Stealth fetching with anti-bot bypass stealth = StealthFetcher() page = stealth.get("https://protected-site.com") # Playwright-based fetching for JS-rendered pages pw = PlayWrightFetcher() page = pw.get("https://spa-example.com", headless=True) ```

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