Ad library aggregation
Pull every ad a brand is running across Meta, Google, LinkedIn, and Reddit in one parallel fan-out — 20 credits per brand audit.
Ad library aggregation
Build a competitive-intelligence dashboard that shows every ad a target company is running across Meta, Google, LinkedIn, and Reddit — for sales-call prep, market-research reports, or buying-team intake.
How do you find all the ads a company is running?
Query the four public ad libraries — Meta, Google Ad Transparency, LinkedIn, and Reddit — in parallel through one API key. Each network has a search endpoint that takes the brand name (or domain, for Google), and the four 5-credit calls complete in one Promise.all, returning every active and recent ad for 20 credits total.
The problem
Every major ad network publishes a transparency library, but each lives behind a different UI, a different query model, and a different response shape. Manually checking four libraries per competitor doesn't scale past your first sales call, and none of the official UIs export data.
The solution
Four ad-library endpoints, all advanced tier (5 credits each):
GET /v1/facebook/adlibrary/search/ads— Meta Ad Library keyword search (param:query)GET /v1/google/company/ads— Google Ad Transparency Library by advertiser (param:domainoradvertiser_id)GET /v1/linkedin/ads/search— LinkedIn Ad Library keyword search (param:query)GET /v1/reddit/ads/search— Reddit Ad Library keyword search (param:query)
The four ad-library responses have different upstream shapes; this recipe flattens them into a unified { network, ad } row so you can render them in one table.
// recipe-ad-audit.ts
// Pulls every ad a brand is running across 4 networks in parallel.
// Run with: SOCIALCRAWL_KEY=sc_... npx tsx recipe-ad-audit.ts
const KEY = process.env.SOCIALCRAWL_KEY;
if (!KEY) throw new Error("Set SOCIALCRAWL_KEY");
const BASE = "https://www.socialcrawl.dev/v1";
const brand = "stripe";
async function get(path: string, params: Record<string, string>) {
const url = new URL(`${BASE}/${path}`);
for (const [k, v] of Object.entries(params)) url.searchParams.set(k, v);
const res = await fetch(url, { headers: { "x-api-key": KEY! } });
if (!res.ok) return { success: false, error: await res.text() };
return (await res.json()) as {
success: boolean;
data?: { items?: unknown[] };
credits_remaining: number;
};
}
type AdRow = { network: string; ad: unknown };
// Google ad-library searches by domain or advertiser_id — not free-text.
// Brand → domain is the most common shape; map your input accordingly.
const brandDomain = "stripe.com";
const [meta, google, linkedin, reddit] = await Promise.all([
get("facebook/adlibrary/search/ads", { query: brand }),
get("google/company/ads", { domain: brandDomain }),
get("linkedin/ads/search", { query: brand }),
get("reddit/ads/search", { query: brand }),
]);
const ads: AdRow[] = [
...(meta.data?.items ?? []).map((ad) => ({ network: "meta", ad })),
...(google.data?.items ?? []).map((ad) => ({ network: "google", ad })),
...(linkedin.data?.items ?? []).map((ad) => ({ network: "linkedin", ad })),
...(reddit.data?.items ?? []).map((ad) => ({ network: "reddit", ad })),
];
console.log(`${ads.length} ads found for "${brand}"`);
const counts = ads.reduce<Record<string, number>>(
(acc, row) => ({ ...acc, [row.network]: (acc[row.network] ?? 0) + 1 }),
{},
);
console.table(counts);
console.log(`credits left: ${reddit.credits_remaining}`);What you get back
// Final aggregated shape after the flattening loop:
[
{
"network": "meta",
"ad": {
"id": "ad_1234567890",
"page_name": "Stripe",
"ad_creative_body": "Accept payments online in minutes...",
"first_active": "2026-05-01", // <-- normalised from raw upstream
"active": true,
},
},
{
"network": "linkedin",
"ad": {
"id": "ln_987654321",
"advertiser_name": "Stripe",
"headline": "Built for fast-growing teams",
"creative_url": "https://www.linkedin.com/ads/...",
},
},
// ... google + reddit rows mixed in
]Credits cost
Cost per run: 20 credits per brand audit (4 networks × 5 credits). Auditing a 25-company competitive set costs 500 credits.
Take it further
- See Endpoint pricing for why ad-library endpoints are advanced tier (they hit slower upstream APIs that return larger payloads).
- Swap
brand = "stripe"for any company name — the Meta, LinkedIn, and Reddit endpoints all accept free-text queries. - Next: Creator engagement scoring shows the same parallel-fan-out pattern applied to profile data.
- Tracking the competitor's organic side too? See Competitor tracking. Platform references: Facebook API, LinkedIn API.
Video transcription
One function that returns the spoken content of any social video — TikTok, Instagram, YouTube, Facebook, X, Reddit, or Rumble — for 10 credits per video.
Creator engagement scoring
Compare a creator's engagement rate across TikTok, Instagram, and YouTube on one ranked table — same formula, same clamp, 3 credits per creator.
