Scrapling
Extract structured website data with resilient selection patterns, adaptive relocation, and the right Scrapling fetcher mode for each target.
Workflow
- 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.
- 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.
- 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.
- 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.
- 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
- Install base package:
- pip install scrapling
- Install fetchers when browser-based fetching is needed:
- pip install "scrapling[fetchers]" - scrapling install - python3 -m playwright install (required for DynamicFetcher and StealthyFetcher)
- 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:
scrapling extract get "https://example.com" content.md --css-selector "main"Pattern: Python extraction script
Use the bundled helper:
# 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.
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.
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.