DEV Community: Agnes Maina

Facebook Ad Library API alternative: no token, no ID check, and days running on every ad

Agnes Maina — Wed, 07 Oct 2026 14:04:42 +0000

I wanted a weekly list of competitor ads for a few ecommerce brands, sorted by how long each ad has been running. An ad that a brand keeps paying for after 60 days is usually an ad that makes money. That's the whole idea behind ad spy tools.

The Problem

The official route is Meta's Ad Library API. Here's what it asks for:

Confirm your identity with a government ID on your Facebook account. Takes one to three business days.
Create a Meta developer app.
Generate an access token in the Graph API Explorer, and keep it fresh.
Pass ad_reached_countries on every request.

Then the catch. For normal commercial ads, the API only covers ads delivered in the EU or the UK. A US brand's US only campaign isn't in it. Spend and impressions show up only for political and issue ads.

The Ad Library website is better. It shows every active ad a page runs, in any country. But clicking through a brand's 200 ads by hand, opening "See ad details" one by one for the EU numbers, and copying start dates into a sheet is not something I'm doing every Monday. And a plain HTTP request to the library page got a 403 and a JavaScript challenge in my test.

The Solution

So I built Meta Ad Library Scraper on Apify. It reads the same public library Meta also calls the Facebook Ads Library, so Instagram and Messenger ads come back next to the Facebook ones. Search by keyword or by advertiser page. No login, no token, no ID check.

Each row is one ad:

the advertiser page, the ad's Library ID and its link
start date, end date, whether it's still active, and daysRunning, computed for every ad
platforms (Facebook, Instagram, Messenger, Audience Network)
headline, body text, call to action, landing URL and landing domain
image and video URLs
for ads shown in the EU: reach by country, reach by age and gender, published targeting, and who paid for the ad

That last block sits behind a second click on the website. The actor opens it for you.

There's also a monitor mode. Turn on onlyNewSinceLastRun, schedule it weekly, and every row tells you whether the ad is new since your last run, whether it just crossed 30, 60 or 90 days running, or whether it stopped.

What I measured in October 2026:

20 test sessions, 0 real blocks
every ad in my test runs had a start date, so daysRunning was never empty
a run for Nike plus "protein powder" returned 85 ads
the same search as a monitor run 20 minutes later skipped all 85 and returned only the 50 it hadn't sent before

One honest warning about keyword search: Meta matches keywords loosely, so a keyword run picks up ads that only mention the word somewhere. I wrote about that in an earlier post. If you know the brand, search by advertiser instead.

Quick Start (3 Minutes)

Open Meta Ad Library Scraper on Apify and click Try for free.
Paste this into the JSON input:

{
  "advertisers": ["Nike"],
  "country": "US",
  "activeStatus": "active",
  "maxAdsPerSearch": 50
}

Click Start, open the results table, and sort by daysRunning.

Code Example

Python, with the official client (pip install apify-client). This one keeps only ads running for 30 days or more and prints the ten oldest:

import os
from apify_client import ApifyClient

client = ApifyClient(os.environ["APIFY_TOKEN"])

run = client.actor("agnes.developer.queen/meta-ad-library-scraper").call(run_input={
    "advertisers": ["Nike"],
    "searchTerms": ["protein powder"],
    "country": "US",
    "activeStatus": "active",
    "minDaysRunning": 30,
    "maxAdsPerSearch": 50,
})

ads = client.dataset(run["defaultDatasetId"]).list_items().items
ads.sort(key=lambda ad: ad.get("daysRunning") or 0, reverse=True)
for ad in ads[:10]:
    text = (ad.get("headline") or ad.get("body") or "")[:60]
    print(ad["daysRunning"], "days |", ad["advertiserName"], "|", text, "|", ad["libraryUrl"])

The same run from n8n, Make or Zapier works through the Apify integration: run the actor, then pass the dataset items to your sheet or Slack.

Use Cases

Ad research for ecommerce brands. Sort a competitor's ads by days running and study the ones they keep paying for.
Agency reports. One scheduled monitor per client: new ads this week, ads that crossed 30, 60 or 90 days, and ads that stopped.
Creative swipe files. Pull image and video URLs for the long runners.
EU audience research. Reach by country, age and gender for ads shown in the EU.

Pricing

$0.005 per ad, so $5 per 1,000 ads. A run that returns 200 ads costs $1. Ads with no start date or no creative are delivered free, with a reason field that says why. Apify gives every account $5 of free platform credit each month, which covers about 1,000 ads.

FAQ

Do I need a Facebook login or an access token?
No. It reads the public Ad Library pages.

Does it show ad spend?
No. Meta publishes spend only for political and issue ads, and this actor doesn't target those.

Why do some ads have no reach numbers?
Meta publishes reach, targeting and payer only for ads shown in the EU. The other rows still have the ad itself and its dates.

Is this allowed?
It reads what Meta's public transparency library shows anyone. Check Meta's terms for your use case, and talk to a lawyer if you're building a commercial product on it.

Get Started

Run Meta Ad Library Scraper on Apify. Try one advertiser first, sort by daysRunning, and see which ads a brand has kept paying for. Questions or bugs go in the actor's Issues tab.

Indeed API alternative in 2026: 100 Chicago nurse jobs, 100 full descriptions, no login

Agnes Maina — Wed, 07 Oct 2026 14:04:38 +0000

I needed Indeed jobs in a spreadsheet every morning. Title, company, salary, the full description. Should have been a ten minute job. It wasn't.

The Problem

First stop was the official API. There isn't one anymore, at least not for reading jobs. Indeed's own developer docs mark the old job search endpoints as deprecated and closed to new integrations. If a tutorial calls api.indeed.com/ads/apisearch with a publisher= key, it's dead. What Indeed offers today goes the other way: APIs for employers and ATS vendors to push jobs and applications into Indeed. None of them gives you search results.

So I tried scraping it myself. Three walls, in this order.

Cloudflare. A plain Node HTTP client got the Cloudflare challenge on 50 out of 50 searches in my test.
The login wall on page 2. Logged out, page 1 of a search loads. Page 2 redirects to Indeed's sign in page. I measured this on October 7, 2026, even in a session that had just loaded page 1 fine. So the classic &start=10 loop is gone.
The job page wants you signed in too. Opening a job URL while logged out gets you a sign in prompt instead of the description.

The data is right there on the search page, and you still can't get it out cleanly.

Who runs into this: recruiting agencies building daily feeds, people running niche job boards, salary research, and sales teams who want to know which companies are hiring for roles their product serves.

The Solution

I ended up building it as an Apify actor: Indeed Jobs Scraper. You give it keywords and a location, or paste Indeed search URLs, and it gives back clean rows. No Indeed account, no cookies.

Each row has:

title, company, company rating and review count
location split into city, state and country
salary text plus parsed salaryMin, salaryMax, currency and period (yearly or hourly), only when Indeed publishes them. Nothing is estimated.
job types and benefits
posted date as a timestamp, plus the "3 days ago" text Indeed shows
the full description as plain text and HTML, on by default

How does it get past page 1 without logging in? It doesn't fight the wall. It runs narrower versions of your search instead: newest first, shorter posting windows, smaller radius, job type filters. Each version has its own page 1, and the results get deduplicated. In one test, a Chicago registered nurse search gave 47 jobs on its first page and 225 unique jobs across nine of these versions. That test used a wider radius than the actor uses, so your numbers will differ.

What I measured on October 7, 2026, on the Apify platform:

a Chicago nurse search with maxJobs: 100 returned 100 jobs, all 100 with full descriptions
a London run returned 15 of 15 descriptions
in five runs, the charged events matched the delivered rows exactly
a repeat run with onlyNew skipped the 28 jobs it had already sent and delivered only new ones

That last one is the monitor mode. Set onlyNew: true, schedule it, and each run only returns jobs you haven't received in the last 90 days.

Quick Start (3 Minutes)

Open Indeed Jobs Scraper on Apify and click Try for free.
Paste this into the JSON input:

{
  "keywords": "registered nurse",
  "location": "Chicago, IL",
  "country": "us",
  "maxJobs": 20
}

Click Start. When it finishes, export the dataset as CSV, JSON or Excel.

It covers the US, UK, Germany, Australia and Canada.

Code Example

Python, with the official client (pip install apify-client):

import os
from apify_client import ApifyClient

client = ApifyClient(os.environ["APIFY_TOKEN"])

run = client.actor("agnes.developer.queen/indeed-jobs-scraper").call(run_input={
    "keywords": "data engineer",
    "location": "Remote",
    "country": "us",
    "postedWithinDays": 7,
    "titleExcludes": ["intern"],
    "maxJobs": 100,
})

jobs = client.dataset(run["defaultDatasetId"]).list_items().items
for job in jobs:
    if job["salaryMin"]:
        pay = f'{job["salaryMin"]} to {job["salaryMax"]} {job["salaryCurrency"]} {job["salaryPeriod"]}'
    else:
        pay = "no salary listed"
    print(job["title"], "|", job["companyName"], "|", pay)

If you prefer a single HTTP call, the sync endpoint runs the actor and returns the items in one response:

curl -X POST "https://api.apify.com/v2/acts/agnes.developer.queen~indeed-jobs-scraper/run-sync-get-dataset-items" \
  -H "Authorization: Bearer $APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"keywords":"accountant","location":"New York, NY","maxJobs":20}'

In n8n the Apify node does the same thing: Run an Actor, wait for it to finish, then send the items to Google Sheets. I'll write that one up separately.

Use Cases

Recruiting feeds. Save a task per client search with onlyNew: true and schedule it every morning. Only new jobs land in the sheet.
Job boards. Backfill a niche board with titles, companies, salaries and full descriptions.
Salary research. Compare published ranges by city. Missing salaries stay empty, they're never guessed.
Hiring signals for sales. Watch which companies post roles your product serves, for example "data engineer" if you sell data tooling.

Pricing

You pay per delivered job. $0.002 per job with a full description, $0.001 per job without one. So 100 jobs with descriptions cost $0.20, and 1,000 cost $2. Apify adds a tiny start fee per run.

Duplicates, jobs your title filters remove, and jobs skipped by onlyNew are never charged. If a description can't be fetched, that row drops to the cheaper price.

FAQ

Does Indeed still have a public jobs API?
Not for search. The old job search endpoints are marked deprecated in Indeed's developer docs. The current APIs are for employers posting jobs and handling applications.

Do I need an Indeed account or cookies?
No. It only reads public pages, logged out.

Why did my run stop before maxJobs?
Without a login only page 1 of each search version is reachable. When new versions stop adding jobs, the run stops. The run's RUN_SUMMARY record tells you why.

Is scraping Indeed legal?
It reads publicly visible job postings, but Indeed's terms restrict automated access, so check your use case and talk to a lawyer if you're building a commercial product on top of it. It doesn't return personal data about job seekers, and you shouldn't use it to collect any.

Get Started

Run Indeed Jobs Scraper on Apify. Start with maxJobs: 20 and see what your search actually returns. If something breaks, open an issue on the actor page with your run ID and I'll look at it.

LinkedIn Ad Library: 40 HubSpot ads, and 23 came back with no impressions or targeting

Agnes Maina — Mon, 05 Oct 2026 02:13:55 +0000

I pulled HubSpot's ads from the LinkedIn Ad Library. 40 ads, and 23 of them came back with no impressions, no targeting and no run dates.

Then I ran the same search with the country set to Germany. 40 of 40 had run dates, impressions and targeting.

Nothing broke. LinkedIn only publishes those fields for ads shown in the European Union, and this is the bit people keep filing bugs about.

The problem

The LinkedIn Ad Library at linkedin.com/ad-library shows the ads a company runs, with no login. For each ad you get the copy, the image or video and who paid. The good stuff, impressions, the country split, the targeting groups and the run dates, only exists for ads shown in the EU.

So an advertiser running mostly outside Europe looks half empty, and it's easy to assume your scraper is broken. It also changes week to week. When I first tested this search in September, 28 of 40 came back with the EU fields. Today it was 17.

Limits first. No spend. Impressions are ranges like "5k-10k", never exact numbers. Targeting tells you which groups were used (job, company, audience and so on), not the actual job titles or companies picked.

Two searches, same company

Both runs: company HubSpot, max 40 ads, details on, the default date window, the last 30 days. One with no country, one with countries: ["DE"]. Both started 2026-10-03 around 16:07 UTC and took about a minute each.

	No country filter	Germany
Ads	40	40
With run dates	17	40
With an impressions range	17	40
With targeting	17	40
With the impressions split by country	10	33
Video ads	14	14

Only 5 ads were in both runs.

Here's the part I didn't expect. Searching "HubSpot" doesn't only return HubSpot. It also returns partner agencies with HubSpot in their page name, things like "HubSpot Platinum Partner" and "MAN Digital: HubSpot & RevOps Agency". In the unfiltered run, 26 ads were paid for by HubSpot, Inc. and 14 by partners or individuals.

Unfiltered run	Ads	With EU fields
Paid by HubSpot, Inc.	26	3
Paid by partners or individuals	14	14

So all 23 empty rows were HubSpot's own ads, with headlines like "CRM slowing you down?", "A CRM Built To Scale." and "Finish every quarter on a high note.". None of them has run dates at all, and none showed up in the Germany run. LinkedIn has no EU data for them, and the usual reason is that the ad never ran in the EU.

The partner ads all had EU data: a Finnish partner targeting Finland, a Polish agency targeting the US and EMEA, and the rest targeting Germany and the Netherlands.

What the Germany run gives you instead:

Impressions ranges from "1k-5k" up to "100k-150k". Three of HubSpot's DE ads were in the top band.
Country splits that go past Germany. Ads shown in Germany also listed Switzerland and Austria (11 ads each), and some listed the US, the UK, Canada and Ireland.
Targeting like this, from one of HubSpot's own DACH ads (trimmed to three of its six groups):

{
  "language": { "includes": ["English"], "excludes": [] },
  "location": { "includes": ["DACH"], "excludes": [] },
  "parameters": [
    { "parameter": "Company", "targeted": true, "excluded": true },
    { "parameter": "Job", "targeted": true, "excluded": true },
    { "parameter": "Audience", "targeted": false, "excluded": false }
  ]
}

That ad's impressions split was Germany 68%, Switzerland 20%, Austria 12%. Seven ads in the Germany run had a total impressions range but no country split, so don't count on that one being there.

The solution

I use my own LinkedIn Ad Library Scraper on Apify. Give it company names, keywords, payers or a pasted Ad Library URL. Each ad comes back as one row with the copy, image and video links, who paid, the format, and the EU fields wherever LinkedIn publishes them: run dates, impressions range, impressions by country and targeting.

Two input notes from this test. Countries take ISO codes like DE or FR, not "EU". And if you only want the company's own ads, filter on payer afterwards, because the search matches page names.

Quick start (3 minutes)

Open the LinkedIn Ad Library Scraper and click Try for free.
Type a company in Companies, for example HubSpot. Add an EU country your competitor sells in, like DE. Set max ads to 40.
Click Start, then open the Ads table or download CSV or JSON.

Code example

Runs both searches and compares how many rows came back with impressions:

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")
actor = client.actor("agnes.developer.queen/linkedin-ad-library-scraper")

for countries in ([], ["DE"]):
    run = actor.call(run_input={"companies": ["HubSpot"], "countries": countries,
                                "maxAdsPerSearch": 40, "withDetails": True})
    ads = client.dataset(run["defaultDatasetId"]).list_items().items
    own = [a for a in ads if a["payer"] == "HubSpot, Inc."]
    with_eu = sum(1 for a in ads if a["totalImpressions"])
    print(countries or "no filter", len(ads), "ads,", with_eu, "with impressions,",
          len(own), "paid by HubSpot")

Use cases

See what a competitor runs in Europe, with impressions ranges and the country split.
Check how they target. Job, company, audience, interests, or a mix.
Find partners and resellers advertising on a brand name. The payer field tells you who paid.
Watch them weekly with onlyNewSinceLastRun, so later runs only return new ads.

Pricing

$0.003 per ad, plus $0.00005 per run. 100 ads cost $0.30. My two runs, 80 ads, cost $0.24.

Rows without usable content are free. Apify's $5 of free monthly credit covers about 1,600 ads.

FAQ

Why do some ads have no impressions or targeting?

LinkedIn only publishes them for ads shown in the EU. Filter to an EU country to get ads that have them.

Can I get exact impression numbers?

No. LinkedIn publishes ranges like "5k-10k".

Does it show which job titles or companies were targeted?

No. LinkedIn shows which groups were used and whether anything was excluded, not the values.

Do I need a LinkedIn account?

No. The Ad Library is public and nothing logs in.

Get started

Open the LinkedIn Ad Library Scraper, click Try for free and run a competitor with and without an EU country.

For B2B research, what do you look at first: the copy or the targeting?

Google Ads Transparency Center scraper: 60 Nike ads, and 17 of the text ads had no text

Agnes Maina — Mon, 05 Oct 2026 02:11:42 +0000

I exported 60 Nike ads from the Google Ads Transparency Center to see what's actually in there.

17 of the text ads came back with no text at all. Google publishes them as pictures only. None of the 60 were video. And the oldest one has been showing up since October 2021.

The problem

The Transparency Center at adstransparency.google.com lists the ads an advertiser runs on Google. Search, shopping, display, YouTube. It's free and public. It also has no export, so you click through one card at a time.

Scrapers fill that gap. What they can't fix is that the columns you'd expect, headline and description, are often empty, because Google publishes a lot of text ads as rendered images and never publishes the words.

Limits first. No spend, no budgets, no keywords, no bids, no clicks. Google publishes none of that, so nothing can scrape it.

What 60 Nike ads actually contained

Input: domain nike.com, region anywhere, format all, cap 60. Run on 2026-10-03 at 16:06 UTC, 59 seconds, run ID BLC69zWNQOcBuulfD. The run read 80 creatives over two result pages and delivered 60, my cap.

First surprise, nike.com isn't one advertiser. It maps to four verified accounts: Nike Retail BV (37 ads), Nike, Inc. (20), Nike Global Trading B.V. Singapore Branch (2) and a media agency account, WPP Media Management (1).

By ad type:

Ad type	Ads	Headline as text	Picture of the ad	Notes
Search	41	41	33	description and display URL on all 41
Text, picture only	17	0	17	Google publishes no words for these
Shopping	2	2	2 (product photo)	product title, merchant "Nike Officiel"
Video	0			none in this sample

The 17 picture only ads still come back with the rendered image of every variation, so you can read them, just not search them as text. 50 of the 60 ads had at least one variation picture. 25 had one, 25 had two to four.

The search ads have real copy. One Polish ad reads "Oficjalna strona Nike" with the description "Odkryj {Keyword} online na Nike.com". That {Keyword} is a dynamic placeholder, published as is, so you can see they swap the search term into the copy. Google counts it as shown on 345 days, in 29 countries.

Dates. daysShown is Google's own count of days the ad was shown:

	Ads
Last shown in the past week	60 of 60
Shown on 90 days or more	58
Shown on 365 days or more	27
Most days shown	1,792

The 1,792 is a shopping ad for a kids' Kobe V basketball shoe, in French, size 36.5. First shown 2021-10-25, last shown 2026-10-03, so Google counts 1,792 days shown in a 1,804 day window.

Countries: 33 of the 60 ads ran in one country, 18 of those in the US. The widest ran in 50. Across all 60, France and Italy show up most (24 ads each), then Spain and Germany (22), then the US (21).

The solution

I use my own Google Ads Transparency Center Scraper on Apify for this. You give it a domain, an advertiser name or an advertiser ID starting with AR. It returns one row per ad: the copy as text where Google publishes it, the rendered picture of every variation, the product title, photo and merchant for shopping ads, the YouTube link for video ads, and first and last shown dates overall and per country.

Rows with no usable content at all are delivered free with the reason. Every ad in this run had content, so all 60 were charged.

Quick start (3 minutes)

Open the Google Ads Transparency Center Scraper and click Try for free.
Type a domain in Website domains, for example nike.com. Leave region on anywhere and format on all, and set max ads to 60.
Click Start, then open the Ads table or download CSV or JSON.

Code example

Node, using apify-client. Pulls the ads, counts picture only rows and prints the longest running ones:

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'YOUR_APIFY_TOKEN' });
const run = await client.actor('agnes.developer.queen/google-ads-transparency-scraper').call({
    domains: ['nike.com'],
    region: 'anywhere',
    format: 'all',
    maxAdsPerAdvertiser: 60,
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();

const pictureOnly = items.filter((ad) => !ad.headline && ad.variations?.length);
console.log(`${pictureOnly.length} of ${items.length} ads have pictures but no text`);

const longest = [...items].sort((a, b) => b.daysShown - a.daysShown).slice(0, 5);
for (const ad of longest) {
    console.log(ad.daysShown, ad.adType, ad.headline ?? '(picture only)', ad.transparencyUrl);
}

countries is a list with first and last shown dates per country. CSV spreads it over a column per country and field, so keep the JSON export if you need it.

Use cases

Competitor copy research. Every search headline and description a rival runs, and in which countries.
Swipe files. The rendered picture of every variation, without screenshotting anything.
Market checks. Set region to one country and see which of a competitor's ads run there.
Weekly monitoring. Turn on onlyNewSinceLastRun and later runs only return ads that are new since the last one.

Pricing

$0.0025 per ad, plus $0.00005 per run. 100 ads cost $0.25. My 60 Nike ads cost $0.15.

Rows without usable content are free. Apify gives every account $5 of free credit a month, which covers about 2,000 ads.

FAQ

Can I see how much Nike spends on Google Ads?

No. Google doesn't publish spend in the Transparency Center, so no scraper can return it.

Why do some text ads have no headline?

Google publishes those only as rendered pictures. You get the picture of every variation instead.

Does it get video ads?

Yes, when the advertiser runs them, with the YouTube link in videoUrl. This Nike sample just didn't have any.

Do I need a Google account?

No. The Transparency Center is public and nothing logs in.

Get started

Open the Google Ads Transparency Center Scraper, click Try for free and put in a competitor's domain.

Do you need competitor ad copy as text, or are the rendered pictures enough for what you do?

LinkedIn people search without login: same 3 searches, 5 matches last week and 0 this week

Agnes Maina — Mon, 05 Oct 2026 02:08:27 +0000

I ran the same three LinkedIn people searches a week apart. Stripe and Asana executives, no login, just job titles and company names.

Last week 5 of 35 results passed the title and employer checks. This week, same inputs, 0 of 30.

Then I typed CEO instead of Chief Executive Officer, and a match came back.

The problem

Searching LinkedIn by title and company without an account means going through Google's index of public profiles. What comes back is messy. Former employees. Assistants whose headline has "CEO" in it. Companies with a similar name. People whose whole headline is just the company name.

There was a thread in r/sales a few days ago, 36 comments, about doing targeted outreach instead of mass volume. Targeted only works if the list is right. A "VP of Sales" who left two years ago is a wasted message and a slightly embarrassing one.

Limits first. This only gives you what's on public profiles. No emails, no phone numbers, no private profiles, nobody Google hasn't indexed. And a headline is whatever the person typed into it.

Same searches, one week apart

Search (titles as typed)	2026-09-27 results / passed	2026-10-03 results / passed
Stripe: Chief Executive Officer, CFO, VP of Sales	10 / 1	10 / 0
Stripe: VP of Sales, Head of Sales, VP of Marketing, Head of Marketing	15 / 3	11 / 0
Asana: Chief Executive Officer, CFO, VP of Sales	10 / 1	9 / 0
Total	35 / 5	30 / 0

The matching code is the same both weeks. What changed is who Google returned.

Last week the first Stripe search had Stripe's CEO in its top 10 and he passed both checks. This week he wasn't in the results. Last week the VP and Head search returned three Heads of Sales and Marketing at Stripe. This week it returned eleven people, and only one of them mentions Stripe in the headline, a Sales Director. The Asana search came back with nine people, none with a title I asked for.

Then one word

I'd also run the two executive searches with "CEO" typed instead of "Chief Executive Officer". The checker treats those as the same title. Google doesn't. It returns different people for different words.

Search	Results / passed
Stripe: CEO, CFO, VP of Sales	10 / 0
Asana: CEO, CFO, VP of Sales	10 / 1

The one that passed: "Chief Financial Officer, Asana". Title in the headline, and the public profile lists Asana as a current employer. So across five searches today, 50 results, one passed.

Why the other 49 didn't:

Reason	Results
title not in own headline	35
current company not confirmed	10
past or support role, not a current title	2
profile is not publicly readable, so the match is not verified	2
passed, charged	1

Some real headlines from those rows, names left out:

"Chief Executive Officer at STRIPE PAYMENTS UK LTD". The checker didn't accept that as Stripe. It might be a real Stripe person. The checker doesn't guess.
"Chief Operations Officer at Stripe TV" and "Chief Executive Officer - Supreme Stripe". Not Stripe.
"Chief Financial Officer at Asana Partners". Not Asana.
"Chief of Staff to the CEO @ Asana" and "Executive Business Partner to CEO at Asana". The word CEO is right there. Neither is the CEO.
"VP of Sales @ Suger | ex-Asana, ex-Opower ...". Right title, former employer.
"Chief Executive Officer at Stripe". Title and company both in the headline, but the profile behind it wasn't publicly readable, so it was delivered free instead of charged on a claim that couldn't be checked.

It also misses people. Several headlines were just "Stripe" or "Asana", and one said "San Francisco Bay Area | Professional Profile". Some of those are probably exactly who you want, with a headline that doesn't say so. They aren't charged, but you still get the rows with the reason, so you can look.

The practical bit: if a search comes back thin, run it again with the other spelling of the title. And Head of titles did better than C level for me last week, because big company execs rarely put the literal title in their headline.

The solution

That's the LinkedIn People Search Scraper on Apify. Titles and companies go in, public profiles come out, no LinkedIn login or cookies.

Before a row is charged it has to pass two checks. The person's own headline has to name a title you searched, with your company in the employer spot. Then, with strict match on (the default), their public profile has to list that company as a current employer. Everything else is still delivered, free, with the reason.

Titles match their common forms, so a CFO search also accepts "Chief Financial Officer", and "VP of Sales" also accepts "Vice President, Sales".

The per row scrapers I compared on the Store would have billed for all 50.

Quick start (3 minutes)

Open the LinkedIn People Search Scraper and click Try for free.
Put the titles you sell to in Titles, one per line, and your target companies in Companies as names or LinkedIn company URLs. Leave strict match on.
Click Start. Filter the dataset on charged: true for the confirmed list and read reason on the rest.

Code example

Run one search, keep the confirmed rows, count why the rest were dropped:

from collections import Counter
from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")
run = client.actor("agnes.developer.queen/linkedin-people-search-scraper").call(
    run_input={"titles": ["CEO", "CFO", "VP of Sales"], "companies": ["Asana"],
               "maxResults": 10, "strictMatch": True}
)
rows = client.dataset(run["defaultDatasetId"]).list_items().items

confirmed = [r for r in rows if r["charged"]]
print(len(confirmed), "confirmed of", len(rows))
for r in confirmed:
    print(r["headline"], r["profileUrl"])

print(Counter(r["reason"] for r in rows if not r["charged"]))

Every row also has seniorityBand, department and isDecisionMaker, read from the headline, so you can sort the free rows too.

Use cases

Account based lists. Your 20 target accounts and the titles you sell to, export only the charged rows.
Checking a list someone sold you. Run their companies and titles and see how many pass.
Hiring research. Find who runs a team before you reach out about a role.
Fewer bad sends. A "VP of Sales" whose profile says otherwise never makes it into your sequence.

Pricing

$0.015 per confirmed profile, plus $0.005 per run. Every other row costs $0.

This week's five runs cost $0.04: five run fees and one confirmed profile. Last week's three runs, 35 results with 5 confirmed, would have been $0.09. 100 confirmed profiles cost $1.505. Apify's free $5 monthly credit covers more than 300 confirmed profiles.

FAQ

Does it return emails or phone numbers?

No. Public profile data only.

Why wasn't a real executive charged?

Their headline doesn't name the title, their profile doesn't list the company as current, or the profile isn't publicly readable. It only charges what it can check. The row is still in your dataset, free.

Why did the same search give different people a week later?

The results come from Google's index of public profiles, and what Google returns for a query moves. The checks were the same code both weeks.

Do I need a LinkedIn account?

No. Nothing logs in.

Get started

Open the LinkedIn People Search Scraper, click Try for free, and try your own target accounts. Keep strict match on, and try both spellings of the title.

When you build a lead list, how many rows do you actually check before you send?

I ran the same Nike search on the Meta Ad Library three times and got three different answers

Agnes Maina — Sat, 03 Oct 2026 16:51:43 +0000

I built a Meta Ad Library scraper and my own export still confused me.

Three different things in it get called "country". So I ran one advertiser through three country filters on the same morning and joined the results on ad ID. 122 distinct ads came back. Zero of them were in all three runs.

The problem

A few days ago someone in r/FacebookAds asked what tool people use to research competitor Meta ads. They were checking the Meta Ad Library against a paid tool and seeing differences in the data, "especially around countries".

My guess is both were right and they were reading different fields. There are three:

The search country. That's the filter you set before searching. country: "DE" returns ads that were shown in Germany. It says nothing about where else the ad ran.
Target locations. Where the advertiser asked Meta to show the ad. Meta publishes this only for ads shown in the EU, and sometimes not even then.
Reach by country. How many people in each EU country actually saw the ad. That's behind a second click on the Library card, and it's EU only too.

A tool that shows the first one and calls it targeting is wrong. A tool that shows the third one and calls it "countries the ad runs in" is also wrong, because an ad that only ran in the US has no entry there at all.

Limits before numbers. A Meta Ad Library export has no spend, no impressions, no clicks and no conversions. Meta publishes spend only for political and social issue ads, and Nike isn't that. A blank cell in my tables is Meta not publishing a number. It is not a zero.

What the three runs returned

Advertiser: Nike, Facebook page ID 15087023444. Same input three times, only the country changed:

{ "advertisers": ["Nike"], "country": "ALL", "activeStatus": "active", "maxAdsPerSearch": 50, "fetchEuDetails": true }

Then "DE", then "US". All three started within eight seconds of each other on 2026-10-03 at 09:38 UTC. The cap of 50 means each run is the first 50 ads the Library returned for that filter, not a full census.

Filter	Ads	With EU reach	With target locations	Top landing domains	Days running
ALL	50	5	5	nike.com 15, play.google.com 14, nike.com.br 10	3 to 544
DE	50	50	49	nike.com 49, nike.in 1	1 to 52
US	34	1	0	nike.com 33, nike.in 1	3 to 470

ALL is not the union of the other two. It returned a global mix: Brazil, Saudi Arabia, app store installs. Only 4 of its 50 ads show up in the DE run and 7 in the US run.

DE is fully published. Every ad in the DE run has EU reach, which makes sense, they all ran in the EU. 49 of 50 also have target locations and a payer name. 25 of the 50 have been running 30 days or more.

US is almost all blank. The US run stopped at 34 ads, under my cap of 50, because the Library had nothing more to return for the Nike page. Other Nike pages like Jordan aren't in it, since I searched one page. 33 of the 34 have null reach, null targeting and null payer.

Same ads, side by side:

adId	ALL	DE	US	Days running	Countries in EU reach	EU reach	Target locations
1870984290951787	yes	yes	no	31	6	6,951,768	Italy, Netherlands, Spain, Poland, France, Germany
1382287983621487	no	yes	yes	3	33	24,396	null
987178264408636	no	yes	no	25	1	108,985	Germany
1016451784037642	yes	no	yes	470	0	null	null
2088910168339416	yes	no	no	136	0	null	null

The first row is the biggest ad in the DE sample. A catalog ad (the DE copy showed the Air Monarch IV), 31 days old, 6.95 million people reached across six EU countries, France first at 1.8 million. Target locations list the same six countries, ages 18 to 65, payer NIKE, Inc. That's what a fully disclosed EU ad looks like.

The second row is a festive sale ad that lands on nike.in. The day before it had 22,032 reach. This morning it had 24,396, spread over 33 country codes (the 27 EU members plus six French overseas regions), Portugal first. Target locations are still null. Reach and targeting are separate disclosures and Meta fills them separately.

Rows four and five are the ones that cause support tickets. Every EU field is null. Both are real, active, correctly scraped ads. Meta lists the 470 day one as active with a start date of 20 June 2025, and it has no EU disclosure at all. A tool with a "countries" column shows them empty and someone files a bug. There's nothing to fix.

The solution

I use my own Actor for this, the Meta Ad Library Scraper on Apify. It reads the public Ad Library with no login and returns one row per ad: copy, picture or video links, start date, daysRunning, landing domain, and for EU ads the reach by country, age and gender, targeting and payer.

Two things matter for this comparison. fetchEuDetails opens each EU card a second time to read reach and targeting, which the other Store listings I checked don't mention. And it keeps nulls as nulls, so you can tell "not published" apart from "zero".

It isn't the cheapest option. If you only need raw ad cards in bulk there are scrapers at a fraction of the price. This one is for when you need days running and EU reach in the same row.

Quick start (3 minutes)

Open the Meta Ad Library Scraper and click Try for free.
Put a Facebook page name, page URL or page ID in Advertisers. Pick a country. Set Maximum ads per search to 50.
Click Start, then open the Ads table or download CSV or JSON.

Run it three times with three countries and you have my experiment for your competitor.

Code example

This runs all three filters and joins them on adId:

from apify_client import ApifyClient
import pandas as pd

client = ApifyClient("YOUR_APIFY_TOKEN")
frames = {}
for country in ["ALL", "DE", "US"]:
    run = client.actor("agnes.developer.queen/meta-ad-library-scraper").call(
        run_input={"advertisers": ["Nike"], "country": country,
                   "activeStatus": "active", "maxAdsPerSearch": 50,
                   "fetchEuDetails": True}
    )
    items = client.dataset(run["defaultDatasetId"]).list_items().items
    frames[country] = pd.DataFrame(items).set_index("adId")

ids = pd.DataFrame({c: f.index.to_series().notna() for c, f in frames.items()}).fillna(False)
print(ids.sum())               # ads per filter
print(ids.all(axis=1).sum())   # ads in all three, zero for me

for c, f in frames.items():
    print(c, f["euTotalReach"].isna().sum(), "of", len(f), "rows have no EU reach")

Keep the JSON export too. euReachByCountry and euReachByAgeGender are nested objects, and CSV spreads them over dozens of columns, one per country and age band.

Use cases

See where a competitor's EU budget actually lands. Sort by euTotalReach and read euReachByCountry.
Find the ads they keep paying for. Set minDaysRunning to 30 and search their page.
Watch them weekly. Turn on onlyNewSinceLastRun and you get new ads, ads that crossed 30, 60 or 90 days, and ads that stopped.
Check another tool's country column against the source before you trust it.

Pricing

$0.005 per ad, plus $0.00005 per run. 100 ads cost $0.50. My three Nike runs delivered 134 ads, so $0.67.

Rows without a start date or a creative are delivered free with the reason. EU reach and targeting are included in the ad price. Apify gives every account $5 of free credit a month, which covers about 1,000 ads.

FAQ

Can I see how much Nike spends?

No. Meta only publishes spend for political and social issue ads. Reach is people, not dollars, and only EU people.

Why is reach blank on almost every US ad?

Meta publishes reach, targeting and payer only for ads shown in the EU. An ad that never ran there has nothing to read.

Does ALL give me every country's ads?

It gives you the first ads the Library returns with no country filter. With a cap, that's a sample. Run the countries you care about separately.

Do I need a Facebook account?

No. It reads the public Ad Library without logging in.

Get started

Open the Meta Ad Library Scraper, click Try for free and paste the input above. The free credit covers the three runs with plenty left.

Which country mismatch have you hit when checking the same ad in two tools?