Agents-as-a-Service Search Pipeline

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

API_KEY = "your_scavio_api_key"

# Simulated tenant credit ledger
CREDIT_LEDGER_PATH = Path("tenant_credits.json")

def load_ledger() -> dict:
    if CREDIT_LEDGER_PATH.exists():
        return json.loads(CREDIT_LEDGER_PATH.read_text())
    return {}

def save_ledger(ledger: dict):
    CREDIT_LEDGER_PATH.write_text(json.dumps(ledger, indent=2))

def execute_search(tenant_id: str, platform: str, query: str) -> dict:
    ledger = load_ledger()
    tenant = ledger.get(tenant_id, {"credits": 100, "used": 0})

    if tenant["credits"] - tenant["used"] <= 0:
        return {"error": "insufficient_credits", "tenant_id": tenant_id}

    res = requests.post(
        "https://api.scavio.dev/api/v1/search",
        headers={"x-api-key": API_KEY},
        json={"platform": platform, "query": query},
        timeout=15,
    )
    res.raise_for_status()
    data = res.json()

    # Debit credits
    tenant["used"] += 1
    tenant["last_query"] = datetime.utcnow().isoformat()
    ledger[tenant_id] = tenant
    save_ledger(ledger)

    return {
        "tenant_id": tenant_id,
        "credits_remaining": tenant["credits"] - tenant["used"],
        "results": data.get("organic", [])[:5],
        "query": query,
        "platform": platform,
    }

def run():
    # Simulate multi-tenant requests
    requests_batch = [
        {"tenant_id": "tenant_001", "platform": "google", "query": "best CRM software 2026"},
        {"tenant_id": "tenant_002", "platform": "reddit", "query": "search API recommendation"},
        {"tenant_id": "tenant_001", "platform": "google", "query": "AI agent frameworks comparison"},
    ]

    for req in requests_batch:
        result = execute_search(req["tenant_id"], req["platform"], req["query"])
        if "error" in result:
            print(f"  DENIED {req['tenant_id']}: {result['error']}")
        else:
            print(f"  OK {req['tenant_id']}: {len(result['results'])} results, {result['credits_remaining']} credits left")

if __name__ == "__main__":
    run()

const API_KEY = "your_scavio_api_key";

// In-memory tenant ledger (use a database in production)
const ledger = {
  tenant_001: { credits: 100, used: 0 },
  tenant_002: { credits: 50, used: 0 },
};

async function executeSearch(tenantId, platform, query) {
  const tenant = ledger[tenantId];
  if (!tenant || tenant.credits - tenant.used <= 0) return { error: "insufficient_credits" };

  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, query }),
  });
  if (!res.ok) throw new Error(`scavio ${res.status}`);
  tenant.used += 1;
  const data = await res.json();
  return { tenantId, remaining: tenant.credits - tenant.used, results: (data.organic ?? []).length };
}

const batch = [
  { tenant: "tenant_001", platform: "google", query: "best CRM 2026" },
  { tenant: "tenant_002", platform: "reddit", query: "search API recommendation" },
];
for (const req of batch) {
  const result = await executeSearch(req.tenant, req.platform, req.query);
  if (result.error) console.log(`DENIED ${req.tenant}: ${result.error}`);
  else console.log(`OK ${req.tenant}: ${result.results} results, ${result.remaining} credits left`);
}