edgeless-smart-scraper 0.1.0

Intelligent web scraping with automatic backend selection.

smart-scraper

Intelligent web scraping for Python with automatic backend selection.

Chooses the right scraping strategy for every URL — lightweight static fetch, cloud-based JavaScript rendering, or search-powered extraction — so you do not have to think about it.

Features

• Zero-config core — works out of the box with requests + beautifulsoup4
• Automatic backend selection — static HTML, JS-heavy SPAs, and paywalled content each get the best tool
• Optional cloud backends — Firecrawl and Tavily activate when their SDK and API key are present
• Graceful degradation — always returns a ScrapeResult, never raises on network errors
• Clean Markdown output — navigation, footers, scripts, and styles are stripped automatically
• Typed API — full type annotations, compatible with mypy strict mode

Install

# Core (no API keys required)
pip install smart-scraper

# With Firecrawl (JS-heavy sites)
pip install smart-scraper[firecrawl]

# With Tavily (search + paywalled content)
pip install smart-scraper[tavily]

# Everything
pip install smart-scraper[all]

Quick start

from smart_scraper import scrape_url

# Automatic backend selection — just pass a URL
result = scrape_url("https://docs.python.org/3/library/json.html")

if result.success:
    print(result.title)    # "json — JSON encoder and decoder"
    print(result.content)  # clean Markdown text
else:
    print(result.error)

Backend comparison

Backend	Best for	Dependencies	Free tier
basic	Static HTML, docs, blogs, GitHub raw files	requests, beautifulsoup4	Unlimited
firecrawl	SPAs, React/Next.js, social platforms, anti-bot sites	firecrawl-py + API key	500 scrapes
tavily	Research queries, paywalled/login-walled content	tavily-python + API key	1,000 credits/month

Automatic selection logic

scrape_url() runs the following decision tree before making any network call:

1. URL pattern match — raw file hosts (raw.githubusercontent.com, pastebin.com/raw/, arxiv.org/abs/) always use the basic backend regardless of what is installed.
2. Paywall domains — wsj.com, ft.com, nytimes.com, etc. prefer Tavily (its cached access often bypasses paywalls). Falls back to basic when Tavily is not configured.
3. JS-heavy domains — medium.com, substack.com, notion.so, twitter.com, etc. prefer Firecrawl. Falls back to basic when Firecrawl is not configured.
4. Unknown domains — prefer Firecrawl (highest quality), then Tavily, then basic.

Override automatic selection at any time:

# Force a specific backend
result = scrape_url("https://example.com", backend="firecrawl")
result = scrape_url("https://example.com", backend="tavily")
result = scrape_url("https://example.com", backend="basic")

# Shorthand flags
result = scrape_url("https://notion.so/page", force_firecrawl=True)
result = scrape_url("https://wsj.com/article", force_tavily=True)

API reference

scrape_url(url, *, backend=None, force_firecrawl=False, force_tavily=False, only_main_content=True, timeout=30, firecrawl_api_key=None, tavily_api_key=None)

Scrape a URL and return a ScrapeResult. Always returns — never raises.

Parameters

Parameter	Type	Default	Description
url	str	—	Fully-qualified URL
backend	str | Backend | None	None	Force a backend: "basic", "firecrawl", "tavily", "auto"
force_firecrawl	bool	False	Shorthand for backend="firecrawl"
force_tavily	bool	False	Shorthand for backend="tavily"
only_main_content	bool	True	Strip nav/footer/sidebar (Firecrawl only)
timeout	int	30	Request timeout in seconds
firecrawl_api_key	str | None	None	Per-call key override (reads env var otherwise)
tavily_api_key	str | None	None	Per-call key override (reads env var otherwise)

ScrapeResult

@dataclass
class ScrapeResult:
    url: str
    content: str           # Markdown text
    title: Optional[str]
    metadata: Optional[dict]
    source: str            # "basic" | "firecrawl" | "tavily"
    success: bool
    error: Optional[str]

bool(result) returns result.success for convenient conditional checks.

Using backends directly

Basic backend

from smart_scraper.backends.basic import BasicBackend

backend = BasicBackend(timeout=15)
result = backend.fetch("https://example.com")
print(result.content)

Firecrawl backend

from smart_scraper.backends.firecrawl import FirecrawlBackend

backend = FirecrawlBackend()  # reads FIRECRAWL_API_KEY

# Single page
result = backend.fetch("https://medium.com/@author/article", only_main_content=True)

# Crawl a site
pages = backend.crawl("https://docs.example.com", max_pages=20, max_depth=2)

# Structured extraction
schema = {
    "type": "object",
    "properties": {
        "title": {"type": "string"},
        "topics": {"type": "array", "items": {"type": "string"}},
    },
}
data = backend.extract_structured("https://example.com", schema=schema)

Tavily backend

from smart_scraper.backends.tavily import TavilyBackend

backend = TavilyBackend()  # reads TAVILY_API_KEY

# Extract a specific URL
result = backend.fetch("https://wsj.com/articles/some-story")

# Search the web
response = backend.search("Python web scraping 2024", max_results=5, include_answer=True)
for item in response.results:
    print(item.title, item.url)

# Get an AI-generated answer
answer = backend.qna_search("What is the capital of France?")

Inspecting backend selection

from smart_scraper.selector import BackendSelector, Backend

selector = BackendSelector(firecrawl_available=True, tavily_available=False)
chosen = selector.select("https://medium.com/@author/post")
print(chosen)  # Backend.FIRECRAWL

reason = selector.explain("https://medium.com/@author/post")
print(reason)  # "Backend 'firecrawl' selected: domain requires JavaScript..."

Environment variables

Copy .env.example to .env and fill in your keys:

FIRECRAWL_API_KEY=your_firecrawl_key_here
TAVILY_API_KEY=your_tavily_key_here

Keys are read at runtime via os.environ. Use python-dotenv or your preferred env-management tool to load them:

from dotenv import load_dotenv
load_dotenv()

from smart_scraper import scrape_url
result = scrape_url("https://notion.so/page")

Running tests

# Install dev dependencies
pip install smart-scraper[dev]

# Run offline tests (no network, no API keys)
pytest

# Include live network tests (requires internet)
pytest --live

# With coverage
pytest --cov=src --cov-report=term-missing

Contributing

1. Fork the repo and create a feature branch.
2. Install dev dependencies: pip install -e ".[dev]"
3. Run linting: ruff check . && mypy src/
4. Run tests: pytest
5. Open a pull request — all CI checks must pass.

License

MIT — see LICENSE.