scrapling

Scrapling is a powerful Python web scraping library with a comprehensive CLI for extracting data from websites directly from the terminal without writing code. The primary use case is the extract command group for quick data extraction.

Installation

Install with the shell extras using uv:

uv tool install "scrapling[shell]"

Then install fetcher dependencies (browsers, system dependencies, fingerprint manipulation):

scrapling install

Update to the latest version:

uv tool update "scrapling[shell]"

Extract Commands (Primary Usage)

The scrapling extract command group allows you to download and extract content from websites without writing any code. Output format is determined by file extension:

Note: All examples use --ai-targeted by default. This flag extracts only main body content, strips noise tags (script, style, noscript, svg), removes hidden elements, strips zero-width unicode characters, and removes HTML comments - ideal when output is destined for an AI model.

• .md - Convert HTML to Markdown
• .html - Save raw HTML
• .txt - Extract clean text content

Quick Start

# Basic website download as text
scrapling extract get "https://example.com" page_content.txt --ai-targeted

# Download as markdown
scrapling extract get "https://blog.example.com" article.md --ai-targeted

# Save raw HTML
scrapling extract get "https://example.com" page.html --ai-targeted

Decision Guide: Which Command to Use?

Use Case	Command
Simple websites, blogs, news articles	get
Modern web apps, dynamic content (JavaScript)	fetch
Protected sites, Cloudflare, anti-bot	stealthy-fetch
Form submissions, APIs	post, put, delete

HTTP Request Commands

GET Request

Most common command for downloading website content:

# Basic download
scrapling extract get "https://news.site.com" news.md --ai-targeted

# Download with custom timeout
scrapling extract get "https://example.com" content.txt --timeout 60 --ai-targeted

# Extract specific content using CSS selectors
scrapling extract get "https://blog.example.com" articles.md --css-selector "article" --ai-targeted

# Send request with cookies
scrapling extract get "https://scrapling.requestcatcher.com" content.md \
    --cookies "session=abc123; user=john" --ai-targeted

# Add user agent
scrapling extract get "https://api.site.com" data.json \
    -H "User-Agent: MyBot 1.0" --ai-targeted

# Add multiple headers
scrapling extract get "https://site.com" page.html \
    -H "Accept: text/html" \
    -H "Accept-Language: en-US" --ai-targeted

# With query parameters
scrapling extract get "https://api.example.com" data.json \
    -p "page=1" -p "limit=10" --ai-targeted

GET options:

-H, --headers TEXT              HTTP headers "Key: Value" (multiple allowed)
--cookies TEXT                  Cookies "name1=value1;name2=value2"
--timeout INTEGER               Request timeout in seconds (default: 30)
--proxy TEXT                    Proxy URL from $PROXY_URL env variable
-s, --css-selector TEXT         Extract specific content with CSS selector
-p, --params TEXT               Query parameters "key=value" (multiple)
--follow-redirects / --no-follow-redirects  (default: True)
--verify / --no-verify          SSL verification (default: True)
--impersonate TEXT              Browser to impersonate (chrome, firefox)
--stealthy-headers / --no-stealthy-headers  (default: True)
--ai-targeted                   Extract main content and sanitize for AI

POST Request

# Submit form data
scrapling extract post "https://api.site.com/search" results.html \
    --data "query=python&type=tutorial" --ai-targeted

# Send JSON data
scrapling extract post "https://api.site.com" response.json \
    --json '{"username": "test", "action": "search"}' --ai-targeted

POST options: (same as GET plus)

-d, --data TEXT                 Form data "param1=value1&param2=value2"
-j, --json TEXT                 JSON data as string

PUT Request

# Send data
scrapling extract put "https://api.example.com" results.html \
    --data "update=info" \
    --impersonate "firefox" --ai-targeted

# Send JSON data
scrapling extract put "https://api.example.com" response.json \
    --json '{"username": "test", "action": "search"}' --ai-targeted

DELETE Request

scrapling extract delete "https://api.example.com/resource" response.txt --ai-targeted

# With impersonation
scrapling extract delete "https://api.example.com/" response.txt \
    --impersonate "chrome" --ai-targeted

Browser Fetching Commands

Use browser-based fetching for JavaScript-heavy sites or when HTTP requests fail.

fetch - Handle Dynamic Content

For websites that load content dynamically or have slight protection:

# Wait for JavaScript to load and network activity to finish
scrapling extract fetch "https://example.com" content.md --network-idle --ai-targeted

# Wait for specific element to appear
scrapling extract fetch "https://example.com" data.txt \
    --wait-selector ".content-loaded" --ai-targeted

# Visible browser mode for debugging
scrapling extract fetch "https://example.com" page.html \
    --no-headless --disable-resources --ai-targeted

# Use installed Chrome browser
scrapling extract fetch "https://example.com" content.md --real-chrome --ai-targeted

# With CSS selector extraction
scrapling extract fetch "https://example.com" articles.md \
    --css-selector "article" \
    --network-idle --ai-targeted

fetch options:

--headless / --no-headless      Run browser headless (default: True)
--disable-resources             Drop unnecessary resources for speed boost
--network-idle                  Wait for network idle
--timeout INTEGER               Timeout in milliseconds (default: 30000)
--wait INTEGER                  Additional wait time in ms (default: 0)
-s, --css-selector TEXT         Extract specific content
--wait-selector TEXT            Wait for selector before proceeding
--locale TEXT                   User locale (default: system)
--real-chrome                   Use installed Chrome browser
--proxy TEXT                    Proxy URL
-H, --extra-headers TEXT        Extra headers (multiple)
--ai-targeted                   Extract main content and sanitize for AI

stealthy-fetch - Bypass Protection

For websites with anti-bot protection or Cloudflare:

# Bypass basic protection
scrapling extract stealthy-fetch "https://example.com" content.md --ai-targeted

# Solve Cloudflare challenges
scrapling extract stealthy-fetch "https://nopecha.com/demo/cloudflare" data.txt \
    --solve-cloudflare \
    --css-selector "#padded_content a" --ai-targeted

# Use proxy for anonymity (set PROXY_URL environment variable)
scrapling extract stealthy-fetch "https://site.com" content.md \
    --proxy "$PROXY_URL" --ai-targeted

# Hide canvas fingerprint
scrapling extract stealthy-fetch "https://example.com" content.md \
    --hide-canvas \
    --block-webrtc --ai-targeted

stealthy-fetch options: (same as fetch plus)

--block-webrtc                  Block WebRTC entirely
--solve-cloudflare              Solve Cloudflare challenges
--allow-webgl / --block-webgl   Allow WebGL (default: True)
--hide-canvas                   Add noise to canvas operations
--ai-targeted                   Extract main content and sanitize for AI

CSS Selector Examples

Extract specific content with the -s or --css-selector flag:

# Extract all articles
scrapling extract get "https://blog.example.com" articles.md -s "article" --ai-targeted

# Extract specific class
scrapling extract get "https://example.com" titles.txt -s ".title" --ai-targeted

# Extract by ID
scrapling extract get "https://example.com" content.md -s "#main-content" --ai-targeted

# Extract links (href attributes)
scrapling extract get "https://example.com" links.txt -s "a::attr(href)" --ai-targeted

# Extract text only
scrapling extract get "https://example.com" titles.txt -s "h1::text" --ai-targeted

# Extract multiple elements with fetch
scrapling extract fetch "https://example.com" products.md \
    -s ".product-card" \
    --network-idle --ai-targeted

Help Commands

scrapling --help
scrapling extract --help
scrapling extract get --help
scrapling extract post --help
scrapling extract fetch --help
scrapling extract stealthy-fetch --help

Resources

• Documentation: https://scrapling.readthedocs.io/
• GitHub: https://github.com/D4Vinci/Scrapling