Historical SERP Snapshot

import requests
import json
from pathlib import Path
from datetime import datetime

API_KEY = "your_scavio_api_key"

KEYWORDS = [
    "best SERP API 2026",
    "search API for AI agents",
    "web scraping API alternative",
    "MCP search server",
    "Google SERP data structured",
]

def snapshot_keyword(keyword: str) -> dict:
    res = requests.post(
        "https://api.scavio.dev/api/v1/search",
        headers={"x-api-key": API_KEY},
        json={"platform": "google", "query": keyword, "num": 20, "ai_overview": True},
        timeout=15,
    )
    res.raise_for_status()
    data = res.json()
    return {
        "keyword": keyword,
        "organic": [
            {
                "position": r.get("position"),
                "domain": r.get("link", "").split("/")[2] if "/" in r.get("link", "") else "",
                "title": r.get("title", ""),
            }
            for r in data.get("organic", [])[:20]
        ],
        "has_ai_overview": bool(data.get("ai_overview")),
        "ai_overview_preview": (data.get("ai_overview", {}) or {}).get("text", "")[:300],
        "has_featured_snippet": bool(data.get("featured_snippet")),
        "people_also_ask": [q.get("question", "") for q in data.get("people_also_ask", [])],
        "has_local_pack": bool(data.get("local_pack")),
    }

def run():
    date = datetime.utcnow().strftime("%Y-%m-%d")
    snapshots = [snapshot_keyword(kw) for kw in KEYWORDS]

    # Save weekly snapshot
    archive_dir = Path("serp_archive")
    archive_dir.mkdir(exist_ok=True)
    snapshot_path = archive_dir / f"snapshot_{date}.json"
    snapshot_path.write_text(json.dumps({"date": date, "keywords": len(KEYWORDS), "snapshots": snapshots}, indent=2))

    # Summary
    aio_count = sum(1 for s in snapshots if s["has_ai_overview"])
    snippet_count = sum(1 for s in snapshots if s["has_featured_snippet"])
    local_count = sum(1 for s in snapshots if s["has_local_pack"])

    print(f"Archived {len(KEYWORDS)} keywords for {date}")
    print(f"  AI Overviews: {aio_count}/{len(KEYWORDS)}")
    print(f"  Featured Snippets: {snippet_count}/{len(KEYWORDS)}")
    print(f"  Local Packs: {local_count}/{len(KEYWORDS)}")
    print(f"  Credits used: {len(KEYWORDS)}")

if __name__ == "__main__":
    run()

const API_KEY = "your_scavio_api_key";
const KEYWORDS = ["best SERP API 2026", "search API for AI agents", "web scraping API alternative"];

async function snapshotKeyword(keyword) {
  const res = await fetch("https://api.scavio.dev/api/v1/search", {
    method: "POST",
    headers: { "x-api-key": API_KEY, "content-type": "application/json" },
    body: JSON.stringify({ platform: "google", query: keyword, num: 20, ai_overview: true }),
  });
  if (!res.ok) throw new Error(`scavio ${res.status}`);
  const data = await res.json();
  return {
    keyword,
    organic: (data.organic ?? []).slice(0, 20).map((r) => ({ position: r.position, domain: r.link ? new URL(r.link).hostname : "", title: r.title ?? "" })),
    hasAiOverview: !!data.ai_overview,
    hasFeaturedSnippet: !!data.featured_snippet,
    paa: (data.people_also_ask ?? []).map((q) => q.question ?? ""),
  };
}

const snapshots = [];
for (const kw of KEYWORDS) snapshots.push(await snapshotKeyword(kw));
const aio = snapshots.filter((s) => s.hasAiOverview).length;
console.log(`Archived ${KEYWORDS.length} keywords: ${aio} with AI Overviews`);