# LinkedIn Ad Discovery — Who's Advertising on Any Topic (`foxlabs/linkedin-ad-discovery`) Actor
Discover who advertises on any topic on LinkedIn's public Ad Library. Keyword in → a deduped advertiser list with ad count, sample creatives, active window & formats — enriched with each advertiser's real firmographics (domain, industry, size). No login.
- **URL**: https://apify.com/foxlabs/linkedin-ad-discovery.md
- **Developed by:** [Berkan Kaplan](https://apify.com/foxlabs) (community)
- **Categories:** Marketing, Lead generation
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet
## Pricing
from $8.00 / 1,000 results
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/platform/actors/running/actors-in-store#pay-per-event
## What's an Apify Actor?
Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases.
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.
In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md):
```bash
npm install apify-client
```
In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md):
```bash
pip install apify-client
```
In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md):
````bash
# MacOS / Linux
curl -fsSL https://apify.com/install-cli.sh | bash
# Windows
irm https://apify.com/install-cli.ps1 | iex
```bash
In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md).
If your project is in a different language, use 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
## LinkedIn Ad Discovery — Find Every Advertiser on a Topic
Type a keyword — **CRM**, **cybersecurity**, **solar**, a competitor's whole category — and get back **who is advertising on it**: a deduped, ad-volume-ranked list of the companies running LinkedIn ads on that topic, each with its ad count, live sample creatives and format mix. Where an advertiser resolves to a real company, records also carry **best-effort firmographics** (domain, industry, size, HQ).
Every other ad-library scraper hands you a pile of individual ads to de-dupe yourself. This one answers the actual question — **which companies are spending on this topic** — as a ready-made prospect and competitor map. It reads the **public** LinkedIn Ad Library only: **no login, no cookies**.
- 🔎 **Keyword → advertiser map** — one topic in, a deduped list of the companies advertising on it, ranked by ad volume
- 🏢 **Firmographics where we can resolve them** — domain, industry, employee count, HQ & followers (best-effort, name-resolved for roughly half of advertisers)
- 🧱 **Breaks the ~24-ads-per-query ceiling** — date × country facet fan-out scans far more of a topic than a single search page
- ⚡ **Cookieless, no login** — public Ad Library only; one run → JSON / CSV / Excel / API
### Quick start (API)
Discover who's advertising on **CRM** and **cybersecurity**, enriched, in one call:
```bash
curl -X POST "https://api.apify.com/v2/acts/foxlabs~linkedin-ad-discovery/run-sync-get-dataset-items?token=YOUR_APIFY_TOKEN" \
-H "Content-Type: application/json" \
-d '{ "keywords": ["CRM", "cybersecurity"], "maxAdsPerKeyword": 120, "enrichAdvertiser": true }'
````
Prefer no code? Open the **Input** tab, type your keywords, and click **Start** — then download the results.
### What you get
By default, one clean record per **advertiser** discovered for a keyword (rolled up and ranked by ad volume):
| Field | Type | Description |
|---|---|---|
| `keyword` | string | The topic query that surfaced this advertiser |
| `advertiserName` | string | Advertiser name — the official company name when firmographics resolve, otherwise the name shown on the ad |
| `adCount` | number | How many of the scanned ads on this topic this advertiser is running (the ranking signal) |
| `formats` | array | Format breakdown, e.g. `[{ "format": "image", "count": 9 }, { "format": "video", "count": 5 }]` |
| `sampleCreatives` | string\[] | Up to 3 ad-copy snippets (≤140 chars each) from this advertiser's ads |
| `advertiserCompanyId` | string | LinkedIn numeric organization id — present only when the advertiser is resolved |
| `advertiserUrl` | string | `https://www.linkedin.com/company/{id}` — present only when resolved |
| `advertiserCompany` | object | Best-effort firmographics (sub-fields below) — present only when resolved |
| `sources` | object | Provenance, e.g. `{ "ads": "LinkedIn Ad Library (public)", "firmographics": "LinkedIn company page (name-resolved)" }` |
| `scrapedAt` | string | ISO-8601 timestamp of the run |
`advertiserCompany` sub-object (best-effort, present only when the advertiser resolves to a company):
| Field | Type | Description |
|---|---|---|
| `advertiserCompany.domain` | string | Company website domain, e.g. `hubspot.com` — **the field to verify a match** |
| `advertiserCompany.industry` | string | LinkedIn industry label |
| `advertiserCompany.employeeCount` | number | Employee count from the public company page |
| `advertiserCompany.hq` | string | HQ location — locality, region, country |
| `advertiserCompany.followers` | number | LinkedIn follower count |
| `advertiserCompany.website` | string | Full company website URL |
Empty fields are dropped, never guessed — an advertiser that can't be resolved simply omits `advertiserCompany`, `advertiserCompanyId` and `advertiserUrl`.
**Ads mode** (set **Output shape → Ads**) returns one record per individual ad instead:
| Field | Type | Description |
|---|---|---|
| `keyword` | string | The topic query |
| `adUrl` | string | `https://www.linkedin.com/ad-library/detail/{id}` |
| `advertiserName` | string | Name shown on the ad card |
| `body` | string | Ad copy (≤200 chars) |
| `format` | string | `image` or `video` (when detectable) |
| `source` | string | `LinkedIn Ad Library (public)` |
| `scrapedAt` | string | ISO-8601 timestamp |
#### Sample output
*Illustrative record (advertiser rollup mode), shape taken from the live Actor:*
```json
{
"keyword": "CRM",
"advertiserName": "HubSpot",
"advertiserCompanyId": "68529",
"advertiserUrl": "https://www.linkedin.com/company/68529",
"adCount": 14,
"formats": [
{ "format": "image", "count": 9 },
{ "format": "video", "count": 5 }
],
"sampleCreatives": [
"Grow better with the Smart CRM that connects your marketing, sales and service teams.",
"See why 250,000+ businesses run on our platform. Start free — no credit card required.",
"Your CRM shouldn't slow you down. Meet the AI-powered platform built to scale with you."
],
"advertiserCompany": {
"domain": "hubspot.com",
"industry": "Software Development",
"employeeCount": 8100,
"hq": "Cambridge, Massachusetts, US",
"followers": 1050000,
"website": "https://www.hubspot.com"
},
"sources": {
"ads": "LinkedIn Ad Library (public)",
"firmographics": "LinkedIn company page (name-resolved)"
},
"scrapedAt": "2026-07-05T09:12:44.581Z"
}
```
### Input & filters
- **Keywords / topics** — one or more terms to discover advertisers for (e.g. `CRM`, `cybersecurity`, `developer tools`). You get one advertiser rollup per company found on each topic.
- **Output shape** — `Advertisers` (rolled up, one row per unique advertiser — recommended) or `Ads` (flat, one row per individual ad).
- **Max ads per keyword** — how many ads to scan per keyword before rolling up (**1–1000**, default **120**). More ads scanned → more advertisers discovered.
- **Enrich advertiser firmographics** — on by default; attaches domain, industry, employee count, HQ and followers per advertiser (best-effort, name-resolved). Turn off for a faster, discovery-only run.
- **Proxy** — **Residential** proxy by default and strongly recommended; LinkedIn rate-limits and blocks datacenter and un-proxied traffic.
### Example inputs (copy & paste)
```jsonc
// 1) Who is advertising across CRM & marketing automation — enriched advertiser map
{ "keywords": ["CRM", "marketing automation"], "maxAdsPerKeyword": 200, "enrichAdvertiser": true }
// 2) Wide, fast scan of a category (skip enrichment for speed)
{ "keywords": ["cybersecurity"], "maxAdsPerKeyword": 500, "enrichAdvertiser": false }
// 3) Raw ad feed for creative research — one row per ad
{ "keywords": ["developer tools"], "output": "ads", "maxAdsPerKeyword": 300 }
// 4) Multi-topic prospect list — several adjacent categories, enriched
{ "keywords": ["fintech", "payments", "banking software"], "output": "advertisers", "maxAdsPerKeyword": 250, "enrichAdvertiser": true }
// 5) Deep single-topic advertiser census — maximum depth
{ "keywords": ["artificial intelligence"], "output": "advertisers", "maxAdsPerKeyword": 1000, "enrichAdvertiser": true }
// 6) Creative swipe across a theme — flat ads, no enrichment, fast
{ "keywords": ["HR software", "recruiting"], "output": "ads", "enrichAdvertiser": false, "maxAdsPerKeyword": 200 }
```
### Use cases
- **Account discovery & prospecting.** You sell into a category — say CRM tooling. Search `keywords: ["CRM"]` with enrichment on and you get a deduped list of the companies actively advertising there, each with a `domain`, `industry` and `employeeCount` to qualify against — a CRM-ready prospect list you didn't have to hand-build.
- **Competitive landscaping.** Who is spending on your theme, and how heavily? `adCount` ranks advertisers by ad volume so you can see the loud players versus the long tail on a single topic in one run.
- **Creative & messaging research.** `sampleCreatives` surfaces the live ad copy each advertiser is running on the topic — read the market's current positioning and hooks without opening the Ad Library ad by ad.
- **Market sizing.** Pull several related keywords and count the unique advertisers, their industries and size distribution to gauge how crowded (and how enterprise-heavy) a category is.
- **ABM & CRM enrichment.** Feed the discovered advertisers (domain + firmographics) straight into your ABM tooling or CRM to build target lists of companies that have already shown intent by advertising.
- **Agency new-business.** Find brands already spending on a theme you specialize in — a warm outbound list of companies that clearly invest in paid social.
### Performance & throughput
Each keyword fans out across roughly **16 faceted search queries** (date × country) run at concurrency 12, deduping by ad id — this is what breaks LinkedIn's ~24-results-per-query page and scans far more of a topic than a single search. With enrichment on, each **unique** advertiser adds one cached company-page fetch (looked up once, reused). Raising `maxAdsPerKeyword` scans deeper (more advertisers, longer run); turning enrichment off makes discovery-only runs noticeably faster. Real throughput is bounded by LinkedIn's rate limits and your proxy, not the Actor — a Residential proxy is required.
### Integrations
**JavaScript** (`apify-client`):
```js
import { ApifyClient } from 'apify-client';
const client = new ApifyClient({ token: 'YOUR_APIFY_TOKEN' });
const run = await client.actor('foxlabs/linkedin-ad-discovery').call({
keywords: ['CRM', 'cybersecurity'], maxAdsPerKeyword: 200, enrichAdvertiser: true,
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
```
**Python** (`apify-client`):
```python
from apify_client import ApifyClient
client = ApifyClient("YOUR_APIFY_TOKEN")
run = client.actor("foxlabs/linkedin-ad-discovery").call(run_input={
"keywords": ["CRM", "cybersecurity"], "maxAdsPerKeyword": 200, "enrichAdvertiser": True,
})
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
print(item["advertiserName"], item.get("adCount"), item.get("advertiserCompany", {}).get("domain"))
```
Also works with **Make / n8n / Zapier** (Apify app → run this Actor, map the input), scheduled runs, webhooks, and the **Apify MCP server** so AI agents can call it as a tool.
### Data quality
- **The discovery data is exact.** `advertiserName`, `adCount`, `formats` and `sampleCreatives` are read directly from the public Ad Library search pages — who is advertising and how much is not estimated.
- **Firmographics are best-effort.** They're resolved from the advertiser name (LinkedIn serves public company data only at slug URLs, not the numeric id, which sits behind a login wall), and resolve for **roughly half** of advertisers. Specific names resolve well; a generic single-word brand can occasionally match a same-named company in another industry — **always verify `advertiserCompany.domain` before critical use**.
- **No fabrication.** Missing fields are omitted, never invented — an unresolved advertiser simply has no `advertiserCompany`.
- **Coverage is broad, not exhaustive** for very popular topics — bounded by `maxAdsPerKeyword`, not silently truncated at LinkedIn's ~24-per-query page.
No fill-rate percentages are claimed here beyond the approximate resolution rate above; the numbers you get are whatever the public Ad Library returns for your query.
### Pricing
**Pay per result** — you're billed per record returned (per advertiser in the default rollup, or per ad in flat mode). Name-based enrichment caches one company-page lookup per advertiser, so it doesn't multiply cost. There's an Apify **free tier** to evaluate the full feature set before you scale, and no third-party proxy bill on top when you use Apify Residential proxy.
### FAQ
**What does one record represent?** By default, one **advertiser** rolled up for a keyword (deduped, ranked by `adCount`). Switch **Output shape → Ads** for one record per individual ad.
**How do you find "who's advertising"?** By searching the **public** LinkedIn Ad Library for your keyword and fanning out across date and country facets, then deduping ad ids and grouping by advertiser. No login, no cookies.
**How accurate are the firmographics?** They're best-effort, resolved from the advertiser's name, and land for roughly half of advertisers. Treat them as a qualification bonus and confirm the match via `advertiserCompany.domain`. The discovery data itself is exact.
**Why do some advertisers have no company or domain?** Because the name couldn't be confidently resolved to a public company page — we omit the firmographics rather than attach a wrong guess.
**Do I need a LinkedIn login, cookies or account?** No. The Actor only reads the public Ad Library and public company pages.
**Can I get the individual ads instead of the rollup?** Yes — set **Output shape → Ads** for a flat, one-row-per-ad feed with `adUrl`, `body` and `format`.
**Do I get impressions, spend, run dates or targeting?** Not in this Actor — those are per-ad EU/DSA details. Use the companion **LinkedIn Ad Tracker**, which tracks a *known* company's ads in depth (run dates, impressions, targeting). This Actor is for **discovery**: keyword → advertisers.
**Why do I need a Residential proxy?** LinkedIn rate-limits and blocks datacenter and un-proxied traffic; Residential (the default) is what keeps runs succeeding.
**How many advertisers can I get per keyword?** As many as appear within `maxAdsPerKeyword` (1–1000) ads scanned — raise it to surface more of a busy topic.
**What export formats are available?** JSON, CSV, Excel, or via the Apify API and integrations.
### Troubleshooting
- **0 or very few advertisers** → the keyword is too niche or misspelled, or the cap is low. Try a broader term and raise `maxAdsPerKeyword`.
- **Firmographics missing for many advertisers** → expected; resolution is best-effort (~half) and worse for generic single-word brands. Keep `enrichAdvertiser` on, and verify each match via `advertiserCompany.domain`.
- **A resolved company looks wrong** → name resolution can collide on generic brand names. Always check `advertiserCompany.domain` before acting on a record; the discovery fields (`advertiserName`, `adCount`, samples) remain exact.
- **Run is slow or getting rate-limited** → enrichment adds a company-page fetch per unique advertiser. Lower `maxAdsPerKeyword`, turn off `enrichAdvertiser`, and make sure a **Residential** proxy is selected.
### Notes, limits & legal (honest)
- **Public data only.** This Actor reads the **public** LinkedIn Ad Library and public company pages — no login, no cookies, nothing gated or private.
- **LinkedIn ToS is your responsibility.** LinkedIn provides no official public Ad Library API; this reads publicly served pages. You are responsible for using the output in line with LinkedIn's Terms of Service and applicable law (including GDPR/CCPA where relevant). Use it for legitimate market research and B2B intelligence.
- **Firmographics are best-effort (~half of advertisers)** and name-resolved — verify via `domain`. The discovery data (who advertises, ad count, formats, sample copy) is read directly and is exact.
- **No per-ad dates, impressions or targeting here.** Those live on the companion **LinkedIn Ad Tracker** (which tracks a known company's ads in depth). This Actor is discovery: keyword → advertisers.
- **Coverage is broad, not exhaustive** for very popular topics — raise `maxAdsPerKeyword` to scan deeper. What's dropped is bounded by your cap, not silently cut at LinkedIn's ~24-per-query page.
- **Not affiliated** with, endorsed by, or sponsored by LinkedIn Corporation. "LinkedIn" is a trademark of LinkedIn Corporation.
### Support
Questions, a field you'd like added, or a custom build? Open the **Issues** tab on this Actor, or email **info@foxlabs.com.tr**. We reply fast.
*If this Actor saves you time, a ⭐ review really helps.*
### Changelog
#### 0.2 — 2026-07-05
- Reworked docs to the foXLabs gold standard: API quick-start, full field table (advertiser rollup + Ads mode), real sample output, JS/Python/Make/n8n/Zapier/MCP integrations, FAQ, troubleshooting and honest legal notes.
- *(Engine, shipped in 0.2)* Advertisers are read straight from the ad-library search pages (~2× faster than fetching every ad's detail page); higher enrichment concurrency; multiple slug spellings per advertiser. Per-ad dates, impressions and targeting live on the companion **LinkedIn Ad Tracker**.
#### 0.1
- Initial release. Keyword → date × country facet fan-out → advertiser rollup (ad count, sample creatives, format mix) with best-effort firmographic enrichment and provenance. Cookieless, no login.
***
Part of the **[foXLabs data platform](https://data.foxlabs.com.tr/)** — public-data company, contact, jobs, ads, procurement & AI-search intelligence scrapers. Browse the full suite at **[data.foxlabs.com.tr](https://data.foxlabs.com.tr/)**. Companion actor: **LinkedIn Ad Tracker** — track a *known* company's ads (creative, run dates, EU/DSA impressions & targeting) in depth.
# Actor input Schema
## `keywords` (type: `array`):
Topics to discover advertisers for — e.g. "CRM", "cybersecurity", "developer tools". One advertiser rollup is returned per company found advertising on the topic.
## `output` (type: `string`):
"Advertisers" rolls results up to one record per unique advertiser (recommended). "Ads" returns one record per individual ad.
## `maxAdsPerKeyword` (type: `integer`):
How many ads to scan per keyword before rolling up (1–1000). More ads = more advertisers discovered.
## `enrichAdvertiser` (type: `boolean`):
Attach each discovered advertiser's real firmographics (domain, industry, employee count, HQ, followers). Best-effort resolution from the advertiser name — verify via the returned domain.
## `proxyConfiguration` (type: `object`):
Proxy configuration. Residential proxy (the default) is strongly recommended — LinkedIn rate-limits and blocks un-proxied and datacenter traffic.
## Actor input object example
```json
{
"keywords": [
"CRM",
"cybersecurity"
],
"output": "advertisers",
"maxAdsPerKeyword": 120,
"enrichAdvertiser": true,
"proxyConfiguration": {
"useApifyProxy": true,
"apifyProxyGroups": [
"RESIDENTIAL"
]
}
}
```
# Actor output Schema
## `dataset` (type: `string`):
No description
# 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 '' with your token
const client = new ApifyClient({
token: '',
});
// Prepare Actor input
const input = {
"keywords": [
"CRM",
"cybersecurity"
],
"maxAdsPerKeyword": 120,
"proxyConfiguration": {
"useApifyProxy": true,
"apifyProxyGroups": [
"RESIDENTIAL"
]
}
};
// Run the Actor and wait for it to finish
const run = await client.actor("foxlabs/linkedin-ad-discovery").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 '' with your token.
client = ApifyClient("")
# Prepare the Actor input
run_input = {
"keywords": [
"CRM",
"cybersecurity",
],
"maxAdsPerKeyword": 120,
"proxyConfiguration": {
"useApifyProxy": True,
"apifyProxyGroups": ["RESIDENTIAL"],
},
}
# Run the Actor and wait for it to finish
run = client.actor("foxlabs/linkedin-ad-discovery").call(run_input=run_input)
# Fetch and print Actor results from the run's dataset (if there are any)
print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
print(item)
# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start
```
## CLI example
```bash
echo '{
"keywords": [
"CRM",
"cybersecurity"
],
"maxAdsPerKeyword": 120,
"proxyConfiguration": {
"useApifyProxy": true,
"apifyProxyGroups": [
"RESIDENTIAL"
]
}
}' |
apify call foxlabs/linkedin-ad-discovery --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=foxlabs/linkedin-ad-discovery",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "LinkedIn Ad Discovery — Who's Advertising on Any Topic",
"description": "Discover who advertises on any topic on LinkedIn's public Ad Library. Keyword in → a deduped advertiser list with ad count, sample creatives, active window & formats — enriched with each advertiser's real firmographics (domain, industry, size). No login.",
"version": "0.1",
"x-build-id": "jKNdbbsGpeVm9L5AY"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/foxlabs~linkedin-ad-discovery/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-foxlabs-linkedin-ad-discovery",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/foxlabs~linkedin-ad-discovery/runs": {
"post": {
"operationId": "runs-sync-foxlabs-linkedin-ad-discovery",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/foxlabs~linkedin-ad-discovery/run-sync": {
"post": {
"operationId": "run-sync-foxlabs-linkedin-ad-discovery",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
}
},
"components": {
"schemas": {
"inputSchema": {
"type": "object",
"properties": {
"keywords": {
"title": "Keywords / topics",
"type": "array",
"description": "Topics to discover advertisers for — e.g. \"CRM\", \"cybersecurity\", \"developer tools\". One advertiser rollup is returned per company found advertising on the topic.",
"items": {
"type": "string"
}
},
"output": {
"title": "Output shape",
"enum": [
"advertisers",
"ads"
],
"type": "string",
"description": "\"Advertisers\" rolls results up to one record per unique advertiser (recommended). \"Ads\" returns one record per individual ad.",
"default": "advertisers"
},
"maxAdsPerKeyword": {
"title": "Max ads per keyword",
"minimum": 1,
"maximum": 1000,
"type": "integer",
"description": "How many ads to scan per keyword before rolling up (1–1000). More ads = more advertisers discovered.",
"default": 120
},
"enrichAdvertiser": {
"title": "Enrich advertiser firmographics",
"type": "boolean",
"description": "Attach each discovered advertiser's real firmographics (domain, industry, employee count, HQ, followers). Best-effort resolution from the advertiser name — verify via the returned domain.",
"default": true
},
"proxyConfiguration": {
"title": "Proxy",
"type": "object",
"description": "Proxy configuration. Residential proxy (the default) is strongly recommended — LinkedIn rate-limits and blocks un-proxied and datacenter traffic.",
"default": {
"useApifyProxy": true,
"apifyProxyGroups": [
"RESIDENTIAL"
]
}
}
}
},
"runsResponseSchema": {
"type": "object",
"properties": {
"data": {
"type": "object",
"properties": {
"id": {
"type": "string"
},
"actId": {
"type": "string"
},
"userId": {
"type": "string"
},
"startedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"finishedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"status": {
"type": "string",
"example": "READY"
},
"meta": {
"type": "object",
"properties": {
"origin": {
"type": "string",
"example": "API"
},
"userAgent": {
"type": "string"
}
}
},
"stats": {
"type": "object",
"properties": {
"inputBodyLen": {
"type": "integer",
"example": 2000
},
"rebootCount": {
"type": "integer",
"example": 0
},
"restartCount": {
"type": "integer",
"example": 0
},
"resurrectCount": {
"type": "integer",
"example": 0
},
"computeUnits": {
"type": "integer",
"example": 0
}
}
},
"options": {
"type": "object",
"properties": {
"build": {
"type": "string",
"example": "latest"
},
"timeoutSecs": {
"type": "integer",
"example": 300
},
"memoryMbytes": {
"type": "integer",
"example": 1024
},
"diskMbytes": {
"type": "integer",
"example": 2048
}
}
},
"buildId": {
"type": "string"
},
"defaultKeyValueStoreId": {
"type": "string"
},
"defaultDatasetId": {
"type": "string"
},
"defaultRequestQueueId": {
"type": "string"
},
"buildNumber": {
"type": "string",
"example": "1.0.0"
},
"containerUrl": {
"type": "string"
},
"usage": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "integer",
"example": 1
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
},
"usageTotalUsd": {
"type": "number",
"example": 0.00005
},
"usageUsd": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "number",
"example": 0.00005
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
}
}
}
}
}
}
}
}
```