LinkedIn Ad Library Scraper
Pricing
from $0.28 / 1,000 ad records
LinkedIn Ad Library Scraper
Scrape public LinkedIn Ad Library ads by keyword, advertiser, country, and official search URLs.
Pricing
from $0.28 / 1,000 ad records
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0.0
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Developer
Fetch Cat
Maintained by CommunityActor stats
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21
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14
Monthly active users
8 days ago
Last modified
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Security
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Export public LinkedIn Ad Library search results and ad details for competitive advertising research. Collect advertiser and payer names, creative text, ad URLs, media links and available transparency text as JSON, CSV or Excel.
Use keywords, advertiser or payer names, or official search URLs. This Actor uses anonymous public pages, not Campaign Manager, private profiles or member-only data. Results and optional fields depend on what LinkedIn makes available.
Quick start
- Open the input form and enter a keyword, advertiser or payer, or paste an official Ad Library search URL.
- For a keyword-only search, clear the prefilled search URL. Set a small
maxAdslimit and run the Actor. - Open the default dataset to inspect and export the records.
{"keyword": "crm","countries": ["US"],"dateOption": "last-30-days","maxAds": 20}
Prefer a video guide? This short Apify walkthrough shows you how to set up and run an Actor. It uses a different Actor, but the setup steps are the same.
Ready-to-run examples
Choose a public example to see its settings before starting a run:
What you get
One public ad record with its ad ID, source search and detail link, plus available advertiser, payer, creative and transparency text. Media fields expose image links rather than downloaded files or guaranteed video assets. Optional values can be null, and published impression ranges are not measured campaign performance.
Who is it for?
- Competitive-advertising researchers reviewing publicly visible advertiser messaging.
- Agency analysts assembling source-linked advertiser, payer and transparency evidence.
- Creative teams comparing available headlines, calls to action and image references.
- Data developers maintaining their own repeated research exports without assuming built-in ad-history tracking.
How you can use the data
- Prepare a US CRM creative sheet. Use the existing crm query, US country and last-thirty-days range with a small ad cap. Keep adId, adUrl, advertiser and available creative text together before opening the public detail page.
- Review another source market deliberately. Use the existing fintech/GB example and retain the requested search context. Official URL inputs and query-builder searches are additive, so clear the prefilled URL for a keyword-only run.
- Inspect payer and transparency evidence. Preserve available payerName, activeDatesText, impressionsText and targetingText beside the ad link. A published impression range is not campaign performance or complete targeting data.
- Compare your own saved exports. Join ad IDs with collection times in your destination. ChangedSince does not filter ads or detect historical changes, and firstSeenAt/lastSeenAt refer to the current extraction rather than an ad's first or final appearance. Check final run-summary outcomes before treating a coverage gap as a creative change.
Input settings
Provide at least one startUrls entry or one query-builder field: keyword, advertiser or payer. URL searches and a query-builder search are additive when both are supplied. Detailed setting behavior is in the input-form help.
| Setting | API key | Purpose |
|---|---|---|
| Official search URLs | startUrls | Search URLs copied from LinkedIn Ad Library. |
| Keyword | keyword | Public ad-text search. |
| Advertiser / account owner | advertiser | Optional advertiser-name filter. |
| Payer name | payer | Optional paid-by filter. |
| Countries | countries | ISO codes such as US or GB. Default US. |
| Date range | dateOption | last-30-days, last-6-months, last-year or any-time. |
| Changed since | changedSince | Labels based on extraction time, not historical ad changes. Does not filter records. |
| Maximum ads | maxAds | Run-wide unique-record cap, from 1 to 1000. Set explicitly for API calls. |
| Detail delay | requestDelayMs | Requested spacing between detail requests. Default 750 ms. |
| Proxy configuration | proxyConfiguration | Existing proxy controls. The form defaults to managed proxy access. |
The form/schema defaults maxAds to 20. An input read without that schema-applied default uses the existing runtime limit of 50, so explicit bounded API examples are recommended. The default date range is the last 30 days. No field is globally required because URLs and query-builder fields are alternatives.
Example inputs
Use the Quick start input as a small first run. Adjust the fields described above for your own data.
Output data
Each dataset item represents one public LinkedIn ad. Optional values may be null, and mediaUrls may be empty. The following is a shape-only illustration, not a real ad or a promise of field availability:
{"adId": "<public-ad-id>","adUrl": "https://www.linkedin.com/ad-library/detail/<public-ad-id>","advertiserName": null,"payerName": null,"creativeText": null,"mediaUrls": [],"changeType": "new"}
| Fields | Meaning |
|---|---|
adId, adUrl, searchUrl | Ad identity and the search where it was found. |
advertiserName, advertiserUrl, payerName | Available public advertiser and paid-by details. |
creativeText, headline, callToAction, creativeType | Available creative text and format signals. |
mediaUrls | Exposed image URLs, not downloaded files or guaranteed video assets. |
activeDatesText, impressionsText, targetingText | Available public transparency text. Impressions are a published range, not measured campaign performance. |
firstSeenAt, lastSeenAt | Current extraction timestamps, not an ad's historical first/last appearance. |
changeType | unchanged only when extraction occurs before the supplied changedSince timestamp. Otherwise new. |
Records follow discovered search/detail order, not a guaranteed ranking or date sort. RUN-SUMMARY in the default key-value store reports saved and charged counts, completed and pending searches, per-search outcomes and a completionReason. Its searchUrls list contains requested searches, not a claim that all were processed. bootstrapRecoveryAttempts and bootstrapRecovered describe the single permitted initial connection recovery. Temporary initial HTTP 403/429/server or proxy tunnel failures may trigger one fresh session within your selected managed proxy configuration before extraction; custom proxy pools, direct connections and omitted proxy settings are not switched to a managed route.
Processing is limited to 270 seconds, or less when needed to finish before your run timeout. Useful partial results can succeed with partial: true and completionReason: runtime_budget, charge_capacity or max_ads. Pending counts describe only work already known, not all possible matching ads. Confirmed no-ad searches still succeed empty. A zero-output runtime limit without confirmed empty results fails, as do essential storage and billing errors. Inspect run status as well as the dataset: partial records can remain after a failure. Runs use at most 512 MB. Automatic replay of an already-started run without an extraction checkpoint is rejected to avoid duplicate work and charges.
Download your results
Open your run's dataset, choose Export and select JSON, CSV or Excel. JSON keeps nested fields together. CSV and Excel are useful when you want to review the records in a spreadsheet.
See Apify's download and export guide for the available formats and field options.
Pricing
Pay-per-event pricing charges start once per run and item for each ad record saved. A run with no saved ads can still incur the start charge.
Use a small run-wide unique-ad cap and focused searches. See the current pricing and plan discounts.
Tips for best results
- Clear unwanted URL or query inputs before running. Searches are additive rather than one mode overriding the other.
- Set the ad cap explicitly in API calls. Schema/form defaults and raw runtime fallback differ.
- Use a focused keyword, country and date range. A valid search with few matches can be broadened after reviewing it.
- Keep source ad URLs with transparency text. Optional payer, impression and targeting fields depend on what LinkedIn publishes.
- Read completion reason and pending work before repeating a search. Requested searches are not proof all were processed, and larger limits do not unlock blocked pages.
Limits and caveats
Only anonymous public Ad Library pages are used. There is no LinkedIn login, cookie, Campaign Manager, private-account or member-only access. Media is returned as exposed links rather than downloaded files, and complete ad or creative coverage is not guaranteed.
LinkedIn can change pages or restrict access. Confirmed no-ad searches can succeed empty, while blocked or unrecognized pages can fail. Isolated detail failures can be skipped with other records preserved. A zero-output runtime stop without confirmed empty results fails, as do essential storage or billing errors.
Processing is bounded as described in Output data, with useful partial rows possible after time, charge or row limits. Pending counts cover already-known work rather than all matching ads. Review platform status as well as RUN-SUMMARY, since saved partial records can remain after failure. An already-started run without an extraction checkpoint cannot be automatically replayed to repeat work and charges.
Discovered order is not a guaranteed rank or date sort. Timestamp and change labels describe the current extraction rather than historical ad monitoring. Impression ranges are source transparency text, optional values can be null, and comparison or change detection belongs in your own workflow. Request spacing can help with rate limits but does not guarantee access.
Automation and integrations
Save your settings for next time
Once you have an input that works for you, save it as an Apify task. This keeps your settings together so you can use them again without filling in the form from scratch.
Run on a schedule
Need fresh results regularly? You can schedule the Actor or a saved task to run at a time that suits you.
Each scheduled run collects the data again. If you want to compare results or highlight changes, add that step in your own workflow.
Send results to your other tools
Use n8n, Make or Zapier to connect this scraper to the rest of your workflow. For example, you could pass its results to an AI assistant or add records to a spreadsheet. Follow the Apify integration guides for your chosen tool.
For a custom connection, an Apify webhook can notify your app when a run finishes or fails. Your app can then fetch the results or handle the failure.
API usage
Want to collect this data from your own app or automation? The examples below send input to the scraper and show how to collect its results.
The Node.js and Python examples wait for the run to finish before reading its results. The cURL example starts the run and returns its details. Wait for that run to finish, then use its defaultDatasetId to fetch the results.
For setup instructions, see Apify's JavaScript client guide, Python client guide and Actor runs API reference.
cURL
curl -X POST 'https://api.apify.com/v2/acts/fetch_cat~linkedin-ad-library-scraper/runs' \-H "Authorization: Bearer $APIFY_TOKEN" \-H 'Content-Type: application/json' \--data @- <<'JSON'{"keyword": "crm","countries": ["US"],"dateOption": "last-30-days","maxAds": 20}JSON
Node.js
import { ApifyClient } from 'apify-client';const client = new ApifyClient({ token: process.env.APIFY_TOKEN });const run = await client.actor('fetch_cat/linkedin-ad-library-scraper').call({"keyword": "crm","countries": ["US"],"dateOption": "last-30-days","maxAds": 20});const { items } = await client.dataset(run.defaultDatasetId).listItems();console.log(items);
Python
import osfrom apify_client import ApifyClientclient = ApifyClient(os.environ['APIFY_TOKEN'])run = client.actor('fetch_cat/linkedin-ad-library-scraper').call(run_input={'keyword': 'crm', 'countries': ['US'], 'dateOption': 'last-30-days', 'maxAds': 20})print(client.dataset(run['defaultDatasetId']).list_items().items)
MCP and AI agents
You can ask your AI assistant to collect data with LinkedIn Ad Library Scraper and help you work with the results. The scraper returns the data, and your assistant can then summarize it, compare records or prepare notes.
Connect your assistant through the Apify MCP server. MCP gives your assistant a way to find this scraper, run it and read the results. Follow the official setup and sign-in guide for your app.
Easier setup with an Apify plugin
If you use a supported coding assistant, the official Apify plugin can set up the connection for you. It connects your assistant to Apify and includes instructions for common tasks. Follow the guide for your particular app.
Use Apify AI in Console
Prefer to keep everything in Apify? Open Apify AI in Console and ask it to find Fetch Cat's LinkedIn Ad Library Scraper. You can describe the data you want and ask questions about the results.
Apify AI is currently in beta. It asks for confirmation before starting an Actor, and normal run charges apply.
Choose your assistant
- Claude and Claude Code (Anthropic): Apify setup guide.
- ChatGPT and Codex (OpenAI): Apify setup guides.
- Grok Bot (xAI): Apify plugin guide.
- Gemini CLI (Google): MCP server setup guide. Use the server URL below with the HTTP configuration supported by Gemini CLI.
Connect just this scraper
To keep your setup focused on this scraper, add this server configuration to a compatible MCP client:
{"mcpServers": {"apify": {"url": "https://mcp.apify.com?tools=fetch_cat/linkedin-ad-library-scraper"}}}
Sign in to Apify when your client asks. If your client uses a Bearer token instead, follow its setup guide and keep the token in its secure configuration. Configuration formats vary by app, so use the linked guide if your client needs a different format.
Use Apify's general server
Already connected to the general Apify MCP server at https://mcp.apify.com? You can use it to find this scraper without adding another server. Ask your assistant to use LinkedIn Ad Library Scraper by Fetch Cat, or give it the identifier fetch_cat/linkedin-ad-library-scraper.
Your assistant can check the available settings and retrieve the results from the returned run. If the run is still working, it should follow that run rather than start it again. Both connection options use Apify's hosted service.
Example requests
- “Use Fetch Cat’s LinkedIn Ad Library Scraper to collect up to 5 crm ads for country US over the last 30 days, clearing prefilled search URLs. Keep ad links, advertiser names and available creative text without downloading media.”
- “Collect up to 5 fintech ads for country GB over the last 30 days using the query builder only. Show available payer and transparency text with source ad links, without treating impressions as measured campaign performance.”
- “Retrieve up to 5 crm ads for country US over the last 30 days with search URLs cleared. Keep ad IDs and extraction timestamps for my own later comparison, and check final completion and pending outcomes rather than using change labels as historical tracking.”
Normal Apify charges apply. Check the live Pricing tab before asking your assistant to run a larger batch.
Pay with x402
If your AI agent has a funded wallet, it can pay for Apify usage through x402. This experimental option lets an agent use Apify without a conventional account.
Apify AGI provides a prepaid API token with a spending cap after payment. Your agent can use that token to run this scraper through the Apify API or an authenticated MCP connection.
Some MCP setups also support paying for each request directly. That is a different payment option, so check the official guide for its availability.
Follow the official guide for wallet setup, supported payment options and current terms. Apify handles the payments. Keep wallet keys and payment tokens in your agent's secure configuration.
Other ways for agents to pay
Apify also supports MPP for prepaid access through AGI and Skyfire for eligible Actors. If your agent already uses one of these, see the agentic payment guide to choose the right setup.
FAQ
How can I scrape LinkedIn Ad Library ads by keyword?
Set keyword, choose countries and dateOption, and explicitly set maxAds. Alternatively paste an official search URL into startUrls, clearing query fields for a URL-only run.
Can I export LinkedIn ad creatives and payer data?
Yes, when publicly exposed. Output includes ad URLs, advertiser details, creative text and image links, plus available payer and transparency text. Export the default dataset as JSON, CSV or Excel.
Does changedSince find only new competitor ads?
No. It neither filters records nor compares earlier runs. Both timestamp fields describe this extraction, and changeType compares that extraction time with the supplied cutoff. Compare separate exports yourself for historical research.
Does this require a LinkedIn login or provide an official LinkedIn API?
No login or cookies are required. This is an independent public-page scraper run through Apify, not LinkedIn's official advertising API. It does not access Campaign Manager or private accounts.
Related Actors
- Facebook Ads Library Scraper — Research public Meta ad creatives.
- Google Ads Transparency Scraper — Research Google's public ad creatives.
- TikTok Creative Center Top Ads Scraper — Compare public TikTok Creative Center Top Ads.
- LinkedIn Company Posts Scraper — Research public company posts alongside advertising.
- LinkedIn Company Profiles Scraper — Add public company-profile context.
Support and feedback
For custom additions or integrations, email hello@fetchcat.dev.
Found an error or have a feature request? Open an issue with the run URL, a non-sensitive input example and what you expected.
Need help sharing it? See Apify’s guide to sharing a run when reporting an issue. Sharing a run can give the developer access to its input, logs and results, so use a run that contains no sensitive data. Please leave out API tokens, passwords, cookies, private documents and personal data. Use a public sample URL or a redacted example instead.
Tried this scraper? We'd love to hear how it worked for you. Leave a review to help others choose the right tool and help us improve it. Your feedback means a lot to us.
Made with care for curious people, AI agents and the possibilities in data. ♥