quanticdata — Python SDK for the QuanticData API

Scrape any page to clean Markdown, run structured Google/Bing/DuckDuckGo searches, crawl and map whole sites, run 74 ready-made Collectors (Amazon, Google Maps, LinkedIn jobs, app stores…), build datasets from a plain-language prompt — everything through QuanticData' residential proxy network with real-browser TLS fingerprints. Pay per successful call; blocked pages cost nothing.

pip install quanticdata

Quickstart

from quanticdata import QuanticData

client = QuanticData()  # reads QUANTICDATA_API_KEY from the environment

page = client.scrape("https://example.com")
print(page["title"], page["engine"])
print(page["content"])          # the page as clean Markdown

Get a free API key at quanticdata.io — every account includes free monthly usage, no card required. Set it once:

export QUANTICDATA_API_KEY=qd_live_your_key_here

What's in the box

Every REST endpoint, one method each — responses come back with the API envelope already unwrapped:

# Structured search — 3 engines, 17 verticals, SerpApi-compatible JSON
serp = client.search("best espresso machine", country="us", num=20)
for r in serp["organic"]:
    print(r["rank"], r["title"], r["link"])

# SERP → citation-ready Markdown context for an AI prompt
ctx = client.search_and_read("latest EU AI act status", top_n=3)

# Map a site's URLs in seconds (sitemaps + homepage links)
urls = client.map("https://stripe.com", search="/blog")

# Async crawl — wait=True polls until it settles and returns the pages
job = client.crawl("https://docs.python.org", limit=30, depth=2, wait=True)

# Batch-scrape known URLs
job = client.batch(["https://a.example", "https://b.example"], wait=True)

# CSS/AI extraction on one page
data = client.scrape(
    "https://books.toscrape.com",
    extract={"titles": {"selector": "h3 a", "attr": "title", "all": True}},
)

# Learn selectors once with an LLM, then scrape the same layout for free
parser = client.generate_parser(
    "https://news.ycombinator.com",
    fields={"titles": "every story title, as a list"},
)

# 74 ready-made Collectors — semantic input instead of URLs
places = client.run_collector(
    "google_maps_places", keyword="dentist", location="Austin, TX", max_results=20
)

# Dataset from a prompt (validated rows, budget-capped)
ds = client.create_dataset(
    "coffee roasters in Portland with email and phone",
    limits={"max_rows": 50, "max_cost_usd": 2},
    wait=True,
)

# Proxy endpoints of every type — residential, mobile, datacenter, ISP, IPv6
plans = client.list_proxies(active=True)
proxies = client.generate_proxies(plans["proxies"][0]["orderId"], country="us", quantity=5)

Errors and retries

Failures raise QuanticDataError with .status, .message and .payload. Connection errors and HTTP 429 are retried with backoff; billable calls are never re-sent after a response was received, so nothing gets double-billed behind your back.

from quanticdata import QuanticData, QuanticDataError

try:
    QuanticData(api_key="qd_live_wrong").scrape("https://example.com")
except QuanticDataError as err:
    print(err.status, err.message)

Also available

• MCP server for Claude, Cursor and any MCP client: npx -y quanticdata-mcp exposes the same 25 tools to AI agents.
• REST reference: quanticdata.io/docs

MIT licensed.