The Screenshot & Scraping API
for AI Agents

Three APIs, Every Agent Use Case Covered

Your agents need to see the web, read the web, and extract structured data from the web. SnapAPI does all three — one API key, one pricing plan, one integration.

Vision Agent: See Any Website

Feed a screenshot directly to GPT-4o or Claude Sonnet. Your agent can analyze layouts, read UI text, detect changes, and reason about visual content.

import anthropic
import requests
import base64

def agent_see_website(url: str, question: str) -> str:
    # Step 1: Get screenshot from SnapAPI
    response = requests.get(
        "https://api.snapapi.pics/v1/screenshot",
        headers={"X-API-Key": "YOUR_SNAPAPI_KEY"},
        params={"url": url, "format": "png", "full_page": True, "width": 1280}
    )
    image_data = base64.standard_b64encode(response.content).decode("utf-8")

    # Step 2: Send to Claude vision
    client = anthropic.Anthropic(api_key="YOUR_ANTHROPIC_KEY")
    message = client.messages.create(
        model="claude-opus-4-6",
        max_tokens=1024,
        messages=[{
            "role": "user",
            "content": [
                {"type": "image", "source": {"type": "base64", "media_type": "image/png", "data": image_data}},
                {"type": "text", "text": question}
            ]
        }]
    )
    return message.content[0].text

# Usage
result = agent_see_website(
    "https://competitor.com/pricing",
    "What are their pricing tiers and monthly costs? List all plans."
)
print(result)

Web Research Agent: Read + Reason Loop

Build an autonomous research agent that browses the web, extracts content, and synthesizes answers — all with clean Python.

import anthropic
import requests

SNAPAPI_KEY = "YOUR_SNAPAPI_KEY"
ANTHROPIC_KEY = "YOUR_ANTHROPIC_KEY"

def extract_page(url: str) -> str:
    """Extract clean markdown text from any URL."""
    r = requests.get(
        "https://api.snapapi.pics/v1/extract",
        headers={"X-API-Key": SNAPAPI_KEY},
        params={"url": url, "format": "markdown"}
    )
    return r.text[:8000]  # Trim to fit context window

def research_agent(query: str, urls: list) -> str:
    """Agent that reads multiple pages and synthesizes an answer."""
    client = anthropic.Anthropic(api_key=ANTHROPIC_KEY)

    # Gather context from all URLs
    context_parts = []
    for url in urls:
        content = extract_page(url)
        context_parts.append(f"--- Source: {url} ---
{content}
")

    combined_context = "
".join(context_parts)

    response = client.messages.create(
        model="claude-sonnet-4-6",
        max_tokens=2048,
        messages=[{
            "role": "user",
            "content": f"Research question: {query}

Sources:
{combined_context}

Provide a detailed, accurate answer based only on the sources above."
        }]
    )
    return response.content[0].text

# Usage: competitive pricing research
answer = research_agent(
    "What are the pricing tiers and rate limits for screenshot APIs in 2026?",
    [
        "https://screenshotone.com/pricing",
        "https://urlbox.com/pricing",
        "https://apiflash.com/pricing"
    ]
)
print(answer)

OpenAI Tool Call Integration

Register SnapAPI as a tool in GPT-4o function calling. The model decides when to browse the web.

from openai import OpenAI
import requests, json

client = OpenAI(api_key="YOUR_OPENAI_KEY")
SNAPAPI_KEY = "YOUR_SNAPAPI_KEY"

tools = [{
    "type": "function",
    "function": {
        "name": "extract_webpage",
        "description": "Extract clean text content from a webpage URL for reading and analysis",
        "parameters": {
            "type": "object",
            "properties": {
                "url": {"type": "string", "description": "The URL to extract content from"}
            },
            "required": ["url"]
        }
    }
}]

def extract_webpage(url: str) -> str:
    r = requests.get("https://api.snapapi.pics/v1/extract",
        headers={"X-API-Key": SNAPAPI_KEY},
        params={"url": url, "format": "markdown"})
    return r.text[:6000]

messages = [{"role": "user", "content": "What does SnapAPI offer compared to ScreenshotOne? Check their websites."}]

# Agentic loop
while True:
    response = client.chat.completions.create(model="gpt-4o", tools=tools, messages=messages)
    msg = response.choices[0].message
    if msg.tool_calls:
        messages.append(msg)
        for tc in msg.tool_calls:
            result = extract_webpage(**json.loads(tc.function.arguments))
            messages.append({"role": "tool", "tool_call_id": tc.id, "content": result})
    else:
        print(msg.content)
        break

SnapAPI vs Other Agent Web Tools

Common Agent Patterns

FAQ for Agent Builders

Continuous Monitoring Agent Pattern

import requests, anthropic, hashlib, json
from datetime import datetime

SNAPAPI_KEY = "YOUR_SNAPAPI_KEY"
ANTHROPIC_KEY = "YOUR_ANTHROPIC_KEY"

def extract_page(url):
    r = requests.get("https://api.snapapi.pics/v1/extract",
        headers={"X-API-Key": SNAPAPI_KEY}, params={"url": url, "format": "markdown"})
    return r.text

def analyze_change(old, new, url):
    client = anthropic.Anthropic(api_key=ANTHROPIC_KEY)
    resp = client.messages.create(model="claude-sonnet-4-6", max_tokens=512, messages=[{
        "role": "user",
        "content": f"Summarize what changed at {url}:

OLD:
{old[:2000]}

NEW:
{new[:2000]}"
    }])
    return resp.content[0].text

# Run on a cron/schedule
WATCH_URLS = ["https://competitor.com/pricing", "https://competitor.com/features"]
state = {}
for url in WATCH_URLS:
    new_text = extract_page(url)
    new_hash = hashlib.sha256(new_text.encode()).hexdigest()
    if url in state and state[url]["hash"] != new_hash:
        print(f"CHANGE: {analyze_change(state[url]['text'], new_text, url)}")
    state[url] = {"hash": new_hash, "text": new_text, "at": datetime.now().isoformat()}

Agent Reliability Patterns

Common Agent Questions

Why SnapAPI is Purpose-Built for Agent Workloads

Most web scraping and screenshot APIs were built for human-driven workflows: a user clicks a button, a screenshot is taken. AI agent workflows are fundamentally different — they run at machine speed, in parallel, on arbitrary URLs, with no human in the loop.

No Rate Limits on Burst Traffic

When an agent processes a research task, it might make 20-50 API calls in a 30-second window. SnapAPI does not throttle at the per-minute level — only at the monthly quota level. This means agent batch operations run at full speed without hitting 429 errors every few seconds.

Markdown Output Optimized for LLM Token Budgets

The /v1/extract endpoint strips navigation, footers, ads, cookie notices, and boilerplate before returning text. For a typical news article or product page, this reduces the token count by 60-80% compared to passing raw HTML. At GPT-4o pricing of $10 per million input tokens, this token reduction directly translates to cost savings on every agent run.

Screenshot Format Optimized for Vision Models

Vision models like GPT-4o and Claude Sonnet work best with screenshots at 1280px width. SnapAPI defaults to this width and returns PNG by default — the format with the best quality-to-size ratio for vision model analysis. No resizing, no format conversion needed before sending to the LLM.

All Three Endpoints Under One Bill

Real agent workflows use all three modalities: screenshot a page for visual analysis, extract its text for context, scrape a specific data element for a structured record. Paying for three separate APIs (Firecrawl for text, ScrapingBee for scraping, a screenshot API for images) costs $250-400/mo at typical agent volumes. SnapAPI does all three for $79/mo at 50K calls.