# Ad Library Scraper - Meta, Google, TikTok, LinkedIn, Snapchat (`s-r/cross-platform-ad-library`) Actor

Scrape the Meta (Facebook) Ad Library, Google Ads Transparency, TikTok, LinkedIn and Snapchat ad libraries in one run. Every live ad an advertiser runs, aggregated by domain - competitor ad intelligence and ad spy across all platforms.

- **URL**: https://apify.com/s-r/cross-platform-ad-library.md
- **Developed by:** [SR](https://apify.com/s-r) (community)
- **Categories:** Marketing, Social media
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $8.00 / 1,000 ad returneds

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## Cross-Platform Ad Library

See every live ad one advertiser is running — across **Meta, Google, TikTok,
LinkedIn and Snapchat** — in a single run. Give it a brand and its domain; get
back one clean dataset of every creative, tagged with the platform it ran on
and the page it clicks through to.

### Why one run instead of five

Ad transparency libraries are fragmented. The same advertiser is a different
name and a different ID on every platform, so pulling a competitor's full ad
footprint normally means five separate tools and a manual join afterwards. This
actor does the join for you and returns one deduplicated, comparable dataset.

### The trick: it aggregates on the domain

An advertiser can use a slightly different name on each platform, but the
website they send clicks to is the same everywhere. So the **landing domain is
the join key**. Meta exposes each ad's landing URL and Google Ads Transparency
exposes the verified advertiser domain, so most ads are matched with high
confidence; a normalized advertiser-name match covers platforms that don't
publish a URL. Every returned ad carries `matched_by` (`domain` / `name`) and a
`confidence` score so you can trust or filter the join yourself.

### Input

| Field | What it does |
|---|---|
| `advertiser` | Brand / advertiser name (one per run). Required. |
| `domain` | The advertiser's website (e.g. `nike.com`). The primary aggregation key — strongly recommended. |
| `country` | Two-letter market for the libraries that filter by country. |
| `platforms` | Which of the five libraries to query. Defaults to all. |
| `max_ads_per_platform` | Cap per platform (1–500). |
| `strict_match` | Keep only ads confirmed as this advertiser (default on). |

### Output

The first row is an `advertiser_summary` (which platforms had ads, counts per
platform, the domains the ads pointed to). Every following row is one ad with a
common shape: `platform`, `advertiser_name`, `headline`, `body_text`, `cta`,
`format`, `landing_url`, `landing_domain`, `creative_url`, plus `impressions` /
`spend` / `first_shown` / `last_shown` wherever the source platform reports
them, and `matched_by` + `confidence`.

### Notes

- One advertiser per run keeps the result clean and the join unambiguous. Run it
  once per competitor.
- A platform that returns nothing or errors doesn't sink the run — it's listed
  under `errors` in the summary and the rest proceed.
- Some platforms report spend and impressions (TikTok, Snapchat, LinkedIn);
  others don't. Missing metric fields mean the platform doesn't publish them,
  not that the ad is missing.

### Common uses

- **Competitive ad intelligence** — a competitor's entire creative library in
  one place.
- **Creative research** — see which formats and hooks a brand runs per channel.
- **Brand monitoring** — track a brand's active ads over time by re-running.
- **Agency reporting** — one dataset per client competitor.

# Actor input Schema

## `advertiser` (type: `string`):

The brand / advertiser to look up on every platform (one per run).

## `domain` (type: `string`):

The advertiser's website domain (e.g. nike.com). This is the primary aggregation key: ads whose landing page points here are grouped as this advertiser, across platforms. Strongly recommended.

## `country` (type: `string`):

Two-letter country for the ad libraries that filter by market.

## `platforms` (type: `array`):

Which ad libraries to query. Defaults to all five.

## `max_ads_per_platform` (type: `integer`):

Cap on ads pulled from each platform (1-500).

## `strict_match` (type: `boolean`):

Keep only ads confirmed to be this advertiser (by domain or name). Turn off to also return unconfirmed ads from a platform's raw search.

## `concurrency` (type: `integer`):

How many sub-scrapers to run at once. Keep at or below your plan's concurrent-run limit (free plan = 5).

## Actor input object example

```json
{
  "advertiser": "Nike",
  "domain": "nike.com",
  "country": "US",
  "platforms": [
    "meta",
    "google",
    "tiktok",
    "linkedin",
    "snapchat"
  ],
  "max_ads_per_platform": 50,
  "strict_match": true,
  "concurrency": 4
}
```

# Actor output Schema

## `ads` (type: `string`):

One row per ad (plus the advertiser\_summary), tagged with platform, landing\_domain and matched\_by.

## `errors` (type: `string`):

Per-platform failures.

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "advertiser": "Nike",
    "domain": "nike.com",
    "country": "US",
    "platforms": [
        "meta",
        "google",
        "tiktok",
        "linkedin",
        "snapchat"
    ],
    "max_ads_per_platform": 50,
    "concurrency": 4
};

// Run the Actor and wait for it to finish
const run = await client.actor("s-r/cross-platform-ad-library").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "advertiser": "Nike",
    "domain": "nike.com",
    "country": "US",
    "platforms": [
        "meta",
        "google",
        "tiktok",
        "linkedin",
        "snapchat",
    ],
    "max_ads_per_platform": 50,
    "concurrency": 4,
}

# Run the Actor and wait for it to finish
run = client.actor("s-r/cross-platform-ad-library").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "advertiser": "Nike",
  "domain": "nike.com",
  "country": "US",
  "platforms": [
    "meta",
    "google",
    "tiktok",
    "linkedin",
    "snapchat"
  ],
  "max_ads_per_platform": 50,
  "concurrency": 4
}' |
apify call s-r/cross-platform-ad-library --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,s-r/cross-platform-ad-library"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/zeT3ZAhgCReiA0IP1/builds/Z9r7KCpGhBaBPJd6m/openapi.json