By use case · Python

Python web scraping API for scraping websites and extracting structured data

The short answer

A Python web scraping API lets you fetch and parse a page with a single HTTP request from your own code instead of maintaining requests, BeautifulSoup, Selenium and a proxy pool yourself. With ClawEngine you POST a URL and a schema and get back clean markdown or typed JSON, with JavaScript already rendered. The library-based stack is still the right answer for small, stable, static targets. The API earns its keep when pages render client-side, when layouts change under you, or when a crawl has to keep running unattended in production. Plans start at $39 a month.

Clean markdown & JSON · JavaScript rendered · robots.txt respected

Last updated July 2026

Live Extraction

Endpoint · POST /v1/extract

GET

try:

Hit Extract to turn this page into clean, LLM-ready data.

robots.txt respected · public data only

Markdown · JSON · structured fields, from one API call. Crawling, rendering and extracting ...

Almost every Python scraping project starts the same way and ends the same way. It starts with twelve lines of requests plus BeautifulSoup that work beautifully on the first target. It ends six months later as a repository full of per-site selector logic, a Selenium container nobody wants to upgrade, a retry decorator, a proxy rotation module and a Slack channel where someone reports that the numbers went blank again. The scraping was never the hard part. Keeping it working was.

ClawEngine collapses that stack into one call. From Python you POST a URL, optionally a schema describing the fields you want, and you get back either clean markdown suitable for a language model or typed JSON with your fields filled in. Rendering happens server side, so single page apps that ship an empty shell to requests come back complete. Crawling is the same call with a seed and scope rules instead of a single URL, so pulling an entire documentation set or product catalog does not mean writing a frontier queue, a dedupe set and a politeness limiter by hand.

Be honest about when you do not need this. If you are scraping one static page, on one site you control, once a week, requests and BeautifulSoup are free, fast and completely sufficient, and you should use them. Scrapy remains excellent when you want full control of the crawl loop and are happy owning the infrastructure it runs on. The API is the better trade when your Python code should be doing something with the data rather than fighting to get it: feeding a RAG pipeline, running a pricing model, enriching a CRM, or handing structured records to an agent.

CRAWL RENDER JS EXTRACT MARKDOWN JSON

Any URL in LLM-ready data out

robots.txt respected public data only

Why it works

What you get with Python

One POST, no scraping stack

The requests library plus an API key replaces BeautifulSoup selectors, a Selenium container, a proxy pool and a retry layer. Nothing to install, nothing to upgrade in CI.

JavaScript already rendered

Single page apps that return an empty shell to requests come back complete, because the page is rendered server side before extraction.

Typed fields, not soup

Pass a schema and get back a dict with your fields filled in. Layout changes on the source page stop being a code change on your side.

What it handles

Any URL in, clean structured data out

Point ClawEngine at a public page and it crawls, renders the JavaScript and extracts clean markdown or typed JSON in one call. Define a schema for structured fields, and respect robots.txt and Terms of Service by default.

Works with plain requests, no SDK required
Returns clean markdown or typed JSON
Renders JavaScript-heavy pages server side
Crawls whole sites from a single seed URL
Handles retries, politeness and crawl-delay
Feeds pandas, LangChain and vector stores directly

POST /v1/extract extraction result

200 · JSON

{
  "url": "https://example.com/products/atlas",
  "title": "Atlas Field Notebook",
  "markdown": "# Atlas Field Notebook\n\nDurable...",
  "data": {
    "name": "Atlas Field Notebook",
    "price": 24.00,
    "currency": "USD",
    "rating": 4.7
  },
  "links": [ "/products", "/cart" ],
  "metadata": { "rendered": true }
}

JS rendered · boilerplate stripped ✓ robots.txt respected

Why ClawEngine

One API that crawls, renders and extracts

Not a raw HTML dump, not a headless browser fleet to run, and not a brittle parser to maintain. One call crawls a public page, renders its JavaScript and returns clean markdown or typed JSON, built for RAG pipelines and AI agents.

LLM-ready output

Clean markdown or typed JSON with the boilerplate stripped, so the data drops straight into a vector store, a prompt or an agent without a cleanup step.

JavaScript rendered

Each page loads in a real browser environment before extraction, so single-page apps and client-rendered content come back complete, not as an empty shell.

Compliance-first

ClawEngine works on public, permitted data only. It respects robots.txt and site Terms of Service and honors crawl-delay, so responsible scraping is the default.

Code examples

Scrape a page from Python in one call

No SDK, no browser driver. Post a URL and the fields you want with the requests library, get a dict back. Crawl a whole site by swapping the endpoint for the crawl one.

python Extract typed fields with requests

import os, requests

BASE = "https://api.clawengine.ai/v1"
headers = {"Authorization": f"Bearer {os.environ['CLAWENGINE_API_KEY']}"}

res = requests.post(f"{BASE}/extract", headers=headers, json={
    "url": "https://example.com/products/widget-pro",
    "format": "json",
    "render": True,                 # the page builds client-side
    "schema": {
        "product_name": "string",
        "price": "number",
        "currency": "string",
        "in_stock": "boolean",
    },
})
res.raise_for_status()

data = res.json()["data"]
print(data["product_name"], data["price"], data["currency"])

python Crawl a site into a DataFrame

import os, requests, pandas as pd

BASE = "https://api.clawengine.ai/v1"
headers = {"Authorization": f"Bearer {os.environ['CLAWENGINE_API_KEY']}"}

job = requests.post(f"{BASE}/crawl", headers=headers, json={
    "url": "https://example.com/catalog",
    "include": ["/catalog/*"],      # stay inside the catalog
    "max_pages": 500,
    "format": "json",
    "schema": {"sku": "string", "title": "string", "price": "number"},
}).json()

# every record has the same keys, so it drops straight into pandas
df = pd.DataFrame([page["data"] for page in job["pages"]])
df = df.dropna(subset=["price"])
print(df.sort_values("price").head(20))

python Async fan-out over many URLs

import asyncio, os, httpx

BASE = "https://api.clawengine.ai/v1"
headers = {"Authorization": f"Bearer {os.environ['CLAWENGINE_API_KEY']}"}

async def fetch(client, url, sem):
    async with sem:                      # keep your own concurrency sane
        r = await client.post(f"{BASE}/extract", headers=headers, timeout=90,
                              json={"url": url, "format": "markdown", "render": True})
        r.raise_for_status()
        return url, r.json()["markdown"]

async def main(urls):
    sem = asyncio.Semaphore(8)
    async with httpx.AsyncClient() as client:
        return await asyncio.gather(*(fetch(client, u, sem) for u in urls))

pages = asyncio.run(main(["https://example.com/a", "https://example.com/b"]))
for url, md in pages:
    print(url, len(md), "chars of markdown")

Python web scraping API: the questions buyers ask

What is the best way to scrape a website with Python?

It depends on the target. For a handful of static pages, requests plus BeautifulSoup is the simplest thing that works. For a large crawl you control end to end, Scrapy gives you the most control. For pages that render client-side, or for a pipeline that must keep running without babysitting, a scraping API is usually cheaper once you count engineering time, because rendering, retries, proxies and parsing all move off your plate.

How do I scrape a website in Python using an API?

Send one HTTP POST from the requests library. The body carries the target URL, the output format you want, and optionally a schema listing the fields to extract. The response comes back as JSON containing clean markdown or your typed fields. There is nothing to install beyond an HTTP client, and no browser or driver to keep updated on your machine or in CI.

Can Python scrape JavaScript-rendered pages?

Yes, but not with requests alone, because requests only sees the HTML the server sent, and on a modern single page app that is an empty shell. Your options are running a headless browser locally with Playwright or Selenium, or calling an API that renders the page for you. The browser route gives you fine control and costs you a browser fleet to maintain. Server-side rendering keeps your Python process light.

Is BeautifulSoup or Scrapy better for web scraping?

They solve different problems. BeautifulSoup is a parser, so you pair it with requests and drive the fetching yourself, which suits small scripts. Scrapy is a full crawling framework with scheduling, concurrency, middleware and pipelines built in, which suits large multi-page crawls. Neither renders JavaScript on its own, so both need Playwright or a rendering service when the target builds its content in the browser.

How do I avoid getting blocked when scraping with Python?

Behave like a considerate client rather than trying to hide. Read robots.txt and respect it, set a real User-Agent that identifies you, limit concurrency, honor crawl-delay, back off on 429 and 5xx responses, and cache so you do not refetch pages that have not changed. If a site is actively blocking you after all that, take it as an answer and find a permitted source instead of escalating.

How much does a Python web scraping API cost?

Vendors price per request or per credit, and rendered requests usually cost more than plain fetches. ClawEngine starts at $39 a month for the Hobby plan, $99 for Startup and $399 for Scale, with no free tier. The comparison to make is not against zero, since libraries are free, but against the engineer-hours you currently spend fixing selectors and upgrading browser containers.

Good questions

Questions about Python

Plain requests works, and that is deliberate. The API is a normal JSON over HTTP endpoint, so httpx, aiohttp or anything else that speaks HTTP is equally fine. Async clients are worth using once you are extracting more than a few dozen URLs per run, since the calls are IO bound and parallelize cleanly.

For tabular work, request JSON with a schema and pass the list of records straight into pandas.DataFrame, since every record already has identical keys. For retrieval, request markdown, chunk it on heading boundaries, and embed it with the source URL and heading path attached as metadata. Both paths skip the cleanup step that normally eats the first hour of a scraping project.

Yes, and some teams do exactly that. Keep Scrapy for the crawl orchestration you already have, and call the extraction endpoint from inside a spider for the pages that need rendering or typed fields. That gets you rendering without adding a browser fleet to your Scrapy deployment, and it lets you migrate one troublesome source at a time instead of rewriting the project.

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Stop wrangling raw HTML. Get LLM-ready data.

Point ClawEngine at a public page and one call crawls, renders the JavaScript and extracts clean markdown or typed JSON, ready for your RAG pipeline or AI agent. Public, permitted data only.

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Crawl · render JS · extract markdown & JSON · robots.txt respected, public data only