Scrapling

Web scraping and data extraction using the Python Scrapling library. Use to scrape static HTML pages, JavaScript-rendered pages (Playwright), and anti-bot or...

Extract structured website data with resilient selection patterns, adaptive relocation, and the right Scrapling fetcher mode for each target.

Workflow

1. Identify target type before writing code:
   • Use Fetcher for static pages and API-like HTML responses.
   • Use DynamicFetcher when JavaScript rendering is required.
   • Use StealthyFetcher when anti-bot protection or browser fingerprinting issues are likely.
2. Choose output contract first:
   • Return JSON for pipelines/automation.
   • Return Markdown/text for summarization or RAG ingestion.
   • Keep stable field names even if selector strategy changes.
3. Implement selectors in this order:
   • Start with CSS selectors and pseudo-elements (for example ::text, ::attr(href)).
   • Fall back to XPath for ambiguous DOM structure.
   • Enable adaptive relocation for brittle or changing pages.
4. Add safety controls:
   • Respect target site terms and legal boundaries.
   • Add timeouts, retries, and explicit error handling.
   • Log status code, URL, and selector misses for debugging.
5. Validate on at least 2 pages:
   • Test one happy path and one edge case page.
   • Confirm required fields are non-empty.
   • Keep extraction deterministic (no hidden random choices).

Quick Setup

1. Install base package:
   • pip install scrapling
2. Install fetchers when browser-based fetching is needed:
   • pip install "scrapling[fetchers]"
   • scrapling install
   • python3 -m playwright install (required for DynamicFetcher and StealthyFetcher)
3. Install optional extras as needed:
   • pip install "scrapling[shell]" for shell + extract commands
   • pip install "scrapling[ai]" for MCP capabilities

Execution Patterns

Pattern: One-off terminal extraction

Use Scrapling CLI for fastest no-code extraction:

bash

scrapling extract get "https://example.com" content.md --css-selector "main"

Pattern: Python extraction script

Use the bundled helper:

bash

# Static page (default)
python scripts/extract_with_scrapling.py --url "https://example.com" --css "h1::text"

# JavaScript-rendered page
python scripts/extract_with_scrapling.py --url "https://example.com" --fetcher dynamic --css "h1::text"

# Anti-bot protected page
python scripts/extract_with_scrapling.py --url "https://example.com" --fetcher stealthy --css "h1::text"

Pattern: Session-based scraping

Use session classes when cookies/state must persist across requests.

python

from scrapling.fetchers import FetcherSession

session = FetcherSession()
login_page = session.post("https://example.com/login", data={"user": "...", "pass": "..."})
protected_page = session.get("https://example.com/dashboard")
headline = protected_page.css_first("h1::text")

Use StealthySession or DynamicSession as drop-in replacements for anti-bot or JS-rendered targets.

Pattern: DOM change resilience

Use auto_save=True on initial capture and retry with adaptive selection on later runs when selectors break.

python

from scrapling.fetchers import Fetcher

# First run: saves DOM snapshot so adaptive relocation can work later
page = Fetcher.auto_match("https://example.com", auto_save=True, disable_adaptive=False)
price = page.css_first(".price::text")

# Later runs: automatically relocates the selector even if the DOM changed
page = Fetcher.auto_match("https://example.com", auto_save=False, disable_adaptive=False)
price = page.css_first(".price::text")

References

• Use scrapling-reference.md for fetcher/API examples and selector patterns.
• Use extract_with_scrapling.py for a reusable CLI script template.