Ad library aggregation

You will build a competitive-intelligence view of every ad a company is running on Meta (Facebook and Instagram), LinkedIn and TikTok, flattened into one table. Useful for sales-call prep, market research and creative benchmarking.

How do you list every ad one advertiser has live?

Query each network's public ad library in parallel with the brand name. All three have a search endpoint that takes a name, and the three calls finish together.

Every major ad network publishes a transparency library, but each has its own interface, its own query model and its own response shape, and none of the official interfaces export data.

What you need

The code

The three responses have different shapes, so the script flattens them into {network, ad} rows.

# ad_audit.py
# Pulls the ads a brand is running on three networks in parallel.
# Run with: INSIGHTSOCIAL_API_KEY=isk_live_... python ad_audit.py
import os
from collections import Counter
from concurrent.futures import ThreadPoolExecutor

import requests

KEY = os.environ["INSIGHTSOCIAL_API_KEY"]
BASE = "https://api.insightsocial.app/v1"
BRAND = "Duolingo"

def get(path, **params):
    res = requests.get(f"{BASE}/{path}", params=params,
                       headers={"x-api-key": KEY}, timeout=120)
    return res.json()

LEGS = {
    "meta":     ("facebook/adlibrary/search/ads", {"query": BRAND}),
    "linkedin": ("linkedin/ads/search",           {"company": BRAND}),
    "tiktok":   ("tiktok/adlibrary/search",       {"advertiser_name": BRAND}),
}

with ThreadPoolExecutor(max_workers=3) as pool:
    futures = {net: pool.submit(get, path, **params) for net, (path, params) in LEGS.items()}
    results = {net: f.result() for net, f in futures.items()}

ads, spent = [], 0
for network, body in results.items():
    spent += body.get("credits_used", 0)
    if not body["success"]:
        print(f"{network}: {body['error']['type']} (not charged)")
        continue
    for ad in body["data"].get("items", []):
        ads.append({"network": network, "ad": ad})

print(f'{len(ads)} ads found for "{BRAND}"')
print(dict(Counter(row["network"] for row in ads)))
print(f"credits used: {spent}, remaining: {results['meta'].get('credits_remaining')}")

What you get back

57 ads found for "Duolingo"
{'meta': 30, 'linkedin': 15, 'tiktok': 12}
credits used: 300, remaining: 9700

The figures are illustrative. Each ad keeps its own network's fields: Meta ads carry the creative text, images, sponsor and running status; LinkedIn ads carry the copy and the advertiser; TikTok ads carry the creative, title, advertiser, impressions and date.

What to change

• Widen the Meta leg. GET /v1/facebook/adlibrary/search/companies (100) resolves a brand name to its advertiser pages and their pageId. GET /v1/facebook/adlibrary/company/ads (100) then returns one page's full run, which is more precise than a keyword search. GET /v1/facebook/adlibrary/ad/transcript (200) returns what a video ad says out loud.
• Read one ad in full. GET /v1/facebook/adlibrary/ad, GET /v1/linkedin/ad and GET /v1/tiktok/adlibrary/ad are 100 credits each.
• Richer TikTok rows. include=ad on the TikTok search adds the brand name, landing page and advertiser profile link to every row.
• What actually performs. GET /v1/tiktok/ads/top (200–2000) returns TikTok's leaderboard of top ads for a market and time window, with click-through rate and rank. Filter it with keyword to see a category's best creative.
• Scope by market and date. Add country and start_date / end_date on Meta, and countries and startDate / endDate on LinkedIn.
• Page deeper. Meta and TikTok page with cursor, LinkedIn with paginationToken. See Pagination.

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