AI Web Scraping API: Analyze & Extract with LLMs

Combine full-browser rendering with large language model analysis. Pass any URL to SnapAPI's analyze endpoint and receive structured summaries, extracted data, classifications, and insights — all in one API call.

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AI Page Analysis in One API Call

The analyze endpoint renders any URL in a full browser, extracts the page content, and passes it to an LLM with your prompt. Ask any question about the page and receive a structured answer:

curl -X POST "https://api.snapapi.pics/v1/analyze" \
  -H "X-Api-Key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "url": "https://techcrunch.com/article/openai-product-launch",
    "prompt": "Summarize this article in 3 bullet points and identify the main companies mentioned.",
    "format": "json"
  }'

The response includes the LLM's answer along with the page title and URL. Pass "format": "json" to receive structured JSON output when your prompt asks for extractable data like lists, prices, or key-value pairs.

Python Integration

import requests, os

def analyze_page(url: str, prompt: str) -> dict:
    response = requests.post(
        "https://api.snapapi.pics/v1/analyze",
        json={"url": url, "prompt": prompt, "format": "json"},
        headers={"X-Api-Key": os.environ["SNAPAPI_KEY"]},
        timeout=60
    )
    response.raise_for_status()
    return response.json()

# Extract company info from a business directory listing
result = analyze_page(
    url="https://www.ycombinator.com/companies/stripe",
    prompt="Extract: company name, founding year, description, and funding stage as JSON."
)
print(result["answer"])
# {"company": "Stripe", "founded": 2010, "description": "...", "stage": "Public"}

The analyze endpoint uses a 60-second timeout by default to allow time for both browser rendering and LLM inference. For complex prompts on long pages, increase the timeout in your client.

AI Web Scraping Use Cases

Combining browser rendering with LLM analysis unlocks extraction tasks that CSS selectors cannot reliably handle — understanding context, following instructions in natural language, and adapting to page layout changes automatically.

Competitor Intelligence at Scale

Monitor competitor product pages, pricing pages, and blog posts without writing or maintaining CSS selectors. Ask the LLM to extract specific fields — pricing tiers, feature lists, customer testimonials — and the model adapts when the page layout changes, unlike CSS selectors that break silently.

competitors = [
    "https://screenshotone.com/#pricing",
    "https://urlbox.com/pricing",
    "https://apiflash.com/#pricing"
]

for url in competitors:
    result = analyze_page(url,
        "Extract all pricing tiers as JSON: [{name, price_monthly, captures_per_month}]"
    )
    print(url, result["answer"])

Content Classification and Moderation

For platforms that accept user-submitted URLs, automatically classify page content before displaying it. Ask the LLM to categorize the page type, detect inappropriate content, extract the primary language, or assess the credibility of claims — all without storing the page content on your servers.

Lead Enrichment from Company Websites

Given a list of company domain names from a CRM or lead list, analyze each company's website to extract team size indicators, technology signals (job postings mentioning specific tools), product category, and target market. This enrichment data improves lead scoring without requiring a dedicated data enrichment vendor subscription.

Article Summarization Pipelines

News aggregators, research tools, and content curation platforms use the analyze endpoint to generate summaries, extract key entities, and classify article topics at ingestion time. One API call per URL replaces a pipeline of separate tools: scraper, HTML cleaner, NLP model, and entity extractor.

BYOK: Bring Your Own LLM Key

SnapAPI's analyze endpoint supports BYOK — bring your own API key for OpenAI, Anthropic, or other supported LLM providers. Pass your key in the request body and SnapAPI routes the rendered page content to your preferred model, billing the LLM call to your own account:

response = requests.post(
    "https://api.snapapi.pics/v1/analyze",
    json={
        "url": "https://example.com",
        "prompt": "What is the main call to action on this page?",
        "llm_key": os.environ["OPENAI_API_KEY"],
        "model": "gpt-4o"
    },
    headers={"X-Api-Key": os.environ["SNAPAPI_KEY"]},
    timeout=60
)

BYOK gives you full control over model selection, cost management, and data residency. Your LLM key is used only for the duration of the API call and is never stored. SnapAPI handles only the browser rendering and content extraction; all LLM inference happens via your own account.

Get started at snapapi.pics — 200 free captures per month, no credit card required. The analyze endpoint is available on all plans. Combine it with screenshot, PDF, and extract endpoints under a single API key for a complete web capture and analysis pipeline.

Batch AI Analysis with asyncio in Python

For pipelines that analyze dozens of URLs, run analysis concurrently using asyncio and httpx. AI analysis calls are slower than screenshot captures — allow 10 to 30 seconds per URL — so concurrency control matters:

import asyncio, httpx, os

async def analyze_async(client: httpx.AsyncClient, url: str, prompt: str) -> dict:
    response = await client.post(
        "https://api.snapapi.pics/v1/analyze",
        json={"url": url, "prompt": prompt, "format": "json"},
        headers={"X-Api-Key": os.environ["SNAPAPI_KEY"]},
        timeout=60
    )
    return {"url": url, "result": response.json().get("answer", "")}

async def analyze_batch(urls: list[str], prompt: str, concurrency: int = 3):
    sem = asyncio.Semaphore(concurrency)
    async with httpx.AsyncClient() as client:
        async def bounded(url):
            async with sem:
                return await analyze_async(client, url, prompt)
        return await asyncio.gather(*[bounded(u) for u in urls])

results = asyncio.run(analyze_batch(
    urls=company_urls,
    prompt="Extract company name, description, and primary product category as JSON.",
    concurrency=3
))

Use a concurrency limit of 3 for AI analysis calls — higher concurrency rarely improves throughput because the bottleneck is LLM inference time, not network latency. Each result includes the structured JSON answer from the LLM alongside the source URL for easy storage in a database or export to CSV.

Combining Screenshot and AI Analysis

Many workflows benefit from both a visual capture and an AI-extracted summary from the same URL. Fire both requests in parallel to minimize wall-clock time:

import asyncio, httpx

async def capture_and_analyze(url: str, api_key: str):
    headers = {"X-Api-Key": api_key}
    async with httpx.AsyncClient() as client:
        screenshot_task = client.get(
            "https://api.snapapi.pics/v1/screenshot",
            params={"url": url, "format": "jpeg", "full_page": "true"},
            headers=headers, timeout=30
        )
        analyze_task = client.post(
            "https://api.snapapi.pics/v1/analyze",
            json={"url": url, "prompt": "Summarize this page in one sentence."},
            headers=headers, timeout=60
        )
        screenshot_resp, analyze_resp = await asyncio.gather(screenshot_task, analyze_task)
        return {
            "image": screenshot_resp.content,
            "summary": analyze_resp.json()["answer"]
        }

Running both requests concurrently with asyncio.gather completes the combined operation roughly as fast as the slower of the two individual requests. Both use the same SnapAPI account and count against your monthly quota.

AI Scraping vs Traditional CSS Selectors

CSS selector-based scraping is fast and cheap but brittle — a site redesign breaks your selectors silently, often without error, returning empty strings or wrong data. AI-based analysis is slower and costs more per request but adapts to layout changes automatically because it understands page content semantically rather than matching DOM structure.

The right approach depends on your use case. For high-volume, homogeneous scraping of a small number of well-understood sites — like daily price monitoring of 10 competitor pages — CSS selectors with a monitoring alert for selector failures is more cost-effective. For long-tail scraping across many diverse sites, or for extraction tasks that require understanding context rather than matching a specific HTML element, AI analysis eliminates the ongoing selector maintenance burden.

SnapAPI supports both approaches under the same API key. Start with the extract endpoint for CSS selector extraction, and switch to the analyze endpoint when you encounter pages where selectors are unreliable or maintenance becomes expensive.

Register at snapapi.pics for 200 free captures per month — no credit card required. Both the extract and analyze endpoints are available on the free tier for evaluation. Full documentation with prompt engineering tips, response formats, and language-specific code examples is at snapapi.pics/docs.html.

Enterprise AI Scraping: Compliance and Data Governance

Regulated industries extracting web data at scale face additional requirements beyond technical performance. SnapAPI processes all browser rendering in isolated, ephemeral Chromium instances — no session state persists between requests, and no scraped content is stored on SnapAPI infrastructure after the response is delivered. This makes it straightforward to meet data minimization requirements under GDPR and CCPA: your application receives the extracted data and decides what to persist, not the API layer.

For teams that need to keep LLM calls entirely within their own cloud boundary, the BYOK (Bring Your Own Key) mode sends the page content to your OpenAI or Anthropic key directly rather than routing through shared inference. Combined with custom proxy routing for geographic compliance, enterprises can build AI scraping pipelines that satisfy both legal counsel and the security team without sacrificing the speed advantages of a managed API.

Start with the free tier at snapapi.pics — 200 captures per month, no card required — and scale to the Business plan when your pipeline matures.

AI web scraping with SnapAPI works across JavaScript rendered single page applications built with React Angular Vue and Next.js frameworks where traditional HTTP scrapers see only empty HTML shells without executing JavaScript code bundles.