LinkedIn Ad Discovery 📣

foXLabs LinkedIn series: Company profiles · Company 360 · Jobs · Hiring signals · Ad tracker

🎉 Turn the public LinkedIn Ad Library into clean, structured data — no login, no API key, one row per ad, with the advertiser, creative, format and run dates. Built for competitive intelligence, marketing and ad research.

🔍 What is the LinkedIn Ad Discovery — and when should you use it?

Give this actor advertiser (company) names and it returns matching ads from the public LinkedIn Ad Library — as clean, deduplicated rows you can filter, export or feed to an AI agent. Every run reads the source live.

Use it when you need: a LinkedIn ad company list for outreach; formation / status monitoring; or a canonical registry record for KYB and due diligence.

Use something else when: you need ad spend figures — the public library shows creatives and run dates, not spend.

🤖 Use with AI agents

Already on the Apify MCP server? Ask for this Actor by name: foxlabs/linkedin-ad-discovery.

Your agent can pay for its own runs. This Actor is pay-per-event with agentic payments, so an agent can discover it, run it and settle the bill over x402 (USDC on Base) or Skyfire — no Apify account or API token of its own. Billing is the same either way: per delivered record, never for errors.

Otherwise paste this into Claude, ChatGPT, Cursor or any MCP-enabled assistant:

I want to pull LinkedIn ad company records using the Apify Actor `foxlabs/linkedin-ad-discovery`.
Input: `keywords`, `output`, `maxAdsPerKeyword`, `maxResults` and more — see the Input table below. `maxResults` caps how many results are returned.
Start with: {"keywords":["CRM"],"output":"advertisers","maxResults":50}
Ask me what to look up, run the Actor, then summarise the rows as a table.

The machine-readable API, MCP config and OpenAPI definition live at apify.com/foxlabs/linkedin-ad-discovery.md.

📋 Overview

Everything you need to turn the public LinkedIn Ad Library into clean, structured data — in one actor, with no login, cookies or API key.

Why teams pick this actor:

• ✅ Whole source, one call — name or ID in, matching ads out.
• 🧹 No empty-promise columns — only fields this registry actually fills; degenerate columns are removed.
• 🔗 Stable identifiers — every row carries the source's own IDs, ready to join across runs and to other Fox Labs actors.
• 💰 Per-row pricing — a minimal price per delivered row, no subscription.
• 🤖 Agent-ready — MCP + x402 agentic payments.

✨ Features

• 🔍 Name or ID lookup — relevance-ranked name search or exact registry-ID lookup.
• 🏢 Full entity profile — status, legal form, formation date, address and the registry’s own contact fields.
• 🧹 Clean schema — deduplicated camelCase rows, ready for CSV/Excel/JSON.

🎬 Quick Start

curl -X POST "https://api.apify.com/v2/acts/foxlabs~linkedin-ad-discovery/runs?token=YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"keywords":["CRM"],"output":"advertisers","maxResults":50}'

🚀 Getting Started (3 steps)

1. Choose your targets — advertiser (company) names.
2. Set the cap — maxResults limits how many results are returned.
3. Run and export — get a clean dataset as JSON, CSV or Excel.

📥 Input

{"keywords":["CRM"],"output":"advertisers","maxResults":50}

Field	Type	Description
keywords	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	string	"Advertisers" rolls results up to one record per unique advertiser (recommended). "Ads" returns one record per individual ad.
maxAdsPerKeyword	integer	How many ads to scan per keyword before rolling up (1–1000). More ads = more advertisers discovered.
maxResults	integer	Maximum number of results (advertisers, or ads if Output shape = Ads) to push across all keywords combined — helps control cost. Set 0 for unlimited.
enrichAdvertiser	boolean	Attach each discovered advertiser's real firmographics (domain, industry, employee count, HQ, followers). Best-effort resolution from the advertiser name —…
includeOwnership	boolean	Resolve each advertiser to its ULTIMATE beneficial owner — a real person or a cross-border parent company — by walking UK holding-company ownership chains…
proxyConfiguration	object	Proxy configuration. Residential proxy (the default) is strongly recommended — LinkedIn rate-limits and blocks un-proxied and datacenter traffic.

📤 Output

One row per result, saved to the dataset. Every row carries scrapedAt. Lookups that cannot be completed are reported in the run log rather than silently dropped.

Field	Description
keyword	Keyword
advertiserName	Advertiser Name
adCount	Ad Count
advertiserCompany.industry	Advertiser Company.industry
advertiserCompany.employeeCount	Advertiser Company.employee Count
advertiserCompany.domain	Advertiser Company.domain
advertiserUrl	Advertiser Url

💼 Use cases

1. Competitor ad intel — see what ads a competitor runs. Input: advertiser names. Output: ads + creatives + dates. Use: a competitive teardown.

2. Messaging research — study messaging in a category. Input: advertiser names. Output: ad text + format. Use: a messaging swipe file.

3. Campaign monitoring — track a brand’s active ads. Input: advertiser names, scheduled. Output: active ads. Use: spot new campaigns.

🔗 Integration

JavaScript / Node.js

import { ApifyClient } from 'apify-client';
const client = new ApifyClient({ token: 'YOUR_TOKEN' });
const run = await client.actor('foxlabs/linkedin-ad-discovery').call({"keywords":["CRM"],"output":"advertisers","maxResults":50});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items[0]);

Python

from apify_client import ApifyClient
client = ApifyClient('YOUR_TOKEN')
run = client.actor('foxlabs/linkedin-ad-discovery').call(run_input={"keywords":["CRM"],"output":"advertisers","maxResults":50})
for item in client.dataset(run['defaultDatasetId']).iterate_items():
    print(item)

Automation (n8n / Zapier / Make): schedule or webhook → HTTP request to the actor API with your input → handle the JSON dataset → push to a sheet, CRM or dashboard.

📊 Pricing

Pay-per-event: per delivered record. Empty or failed lookups are never billed. View current pricing.

❓ FAQ

Do I need an account, login or API key? No. This reads the public LinkedIn Ad Library.

What do I search by? Advertiser (company) names.

How current is the data? Every run queries the source live, so results are as fresh as the registry.

What ad fields are returned? The advertiser, ad creative/text, format and run dates from the public LinkedIn Ad Library.

Can I export to CSV / Excel / JSON? Yes — directly from the Apify dataset.

🐛 Troubleshooting

• Fewer rows than expected — raise maxResults, or refine the input.
• A name returns an unexpected entity — it matched a similar registered name; search the exact registry ID.
• No rows for a name — try the entity’s exact legal name or its registry ID.

⚖️ Is it legal to scrape this data?

This actor reads the publicly available LinkedIn Ad Library. Results can still contain personal data (e.g. a person’s name); personal data is protected by the GDPR and similar laws, so only process it with a legitimate basis. See Apify’s blog post on the legality of web scraping.

🤝 Support & contact

• 🌐 Website: data.foxlabs.com.tr
• 📧 Email: info@foxlabs.com.tr
• 🐛 Issues: open a ticket in the Actor’s Issues tab
• 🧰 More clean B2B data actors: Fox Labs on Apify

Changelog

0.1.28 — 2026-09-20 — README examples corrected against the real input schema

• The README's code examples did not match this Actor. They used queries and maxResultsPerQuery — keys that do not exist in this Actor's input schema — with a placeholder value, and the input table listed those same phantom fields. Anyone who copied the AI-agent, cURL, JavaScript or Python example got a failing run. Every example now uses the real schema and matches the Console prefill: {"keywords":["CRM"],"output":"advertisers","maxResults":50}
• The input table is regenerated from input_schema.json, so it lists the fields the Actor actually accepts.
• Removed claims carried over from the same generator template where present: "formation / status monitoring", "a canonical registry record for KYB and due diligence", "every row carries query", and industry described as a NACE code.
• No code, output field or pricing change.

0.1 — 2026-09-07

• Enabled AI-agent payments (x402) + rebuilt the README to the full standard (What-is / when, AI-agents + x402 agentic payments + MCP, Overview, Features, Use cases, Integration, FAQ, Troubleshooting, Support & contact).

0.0

• Initial release: data from the public LinkedIn Ad Library by name or registry ID.