# Meta Ad Library Scraper - Facebook & Instagram Ads (`santhej/meta-ads-library-scraper`) Actor Scrape the Meta (Facebook & Instagram) Ad Library by keyword or advertiser. Get ad creatives, copy, CTA, run dates, days-running (winning-ad signal), platforms and landing links. No login. - **URL**: https://apify.com/santhej/meta-ads-library-scraper.md - **Developed by:** [Santhej Kallada](https://apify.com/santhej) (community) - **Categories:** Marketing, Social media - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per event 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 ## Meta Ad Library Scraper — Facebook & Instagram Ads **Spy on any advertiser's Facebook & Instagram ads.** Search the Meta Ad Library by keyword or brand and get the ad creatives, copy, CTA, landing links, platforms, and run dates — including **how long each ad has been running**, the #1 signal of a winning ad. ### What you get per ad - Advertiser (page name + ID), ad archive ID + Ad Library link - Ad copy (body), title, caption, CTA - **Run dates + days_running** — long-running ads = proven winners worth modeling - Active/inactive status, publisher platforms (FB / IG / Audience Network / Messenger) - Landing/link URL, image and video creative URLs ### Why days-running matters Advertisers kill losing ads fast. An ad that's been running for 60+ days is almost certainly profitable. Sort by `days_running` to find competitors' winning creatives to model. ### Use cases - **Ad creative research** — swipe winning hooks, angles, and offers. - **Competitor monitoring** — track what rivals are running right now. - **Dropshipping / DTC** — find proven products and creatives. - **Agencies** — build creative briefs from what's working in a niche. ### Pricing `$0.004` per ad, plus a `$0.001` start fee — cheaper than the official Meta ads scraper. No login, no API key. ### Example input ```json { "searchTerms": ["nike", "gymshark"], "country": "US", "adActiveStatus": "active", "maxAdsPerTerm": 50 } ```` *** *Tags: facebook ads scraper, meta ad library, instagram ads, ad spy, competitor ads, winning ads, ad creative research, facebook ad library, dropshipping ads, ad intelligence.* # Actor input Schema ## `searchTerms` (type: `array`): Keywords or brand/advertiser names to search the Ad Library for, one per line. ## `country` (type: `string`): Two-letter country code for the Ad Library, e.g. US, GB, DE. ## `adActiveStatus` (type: `string`): Which ads to include. ## `adType` (type: `string`): Ad category. ## `mediaType` (type: `string`): Filter by creative media. ## `maxAdsPerTerm` (type: `integer`): Maximum ads to collect per search term (1-1000). ## `proxyConfiguration` (type: `object`): Residential proxy is required for the Meta Ad Library. ## Actor input object example ```json { "searchTerms": [ "nike" ], "country": "US", "adActiveStatus": "all", "adType": "all", "mediaType": "all", "maxAdsPerTerm": 50, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } } ``` # Actor output Schema ## `dataset` (type: `string`): All platform\_check records and the optional aggregated\_report row. ## `summary` (type: `string`): High-level run summary including overall visibility score, mention rate, and platform counts. # 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 = { "searchTerms": [ "nike" ] }; // Run the Actor and wait for it to finish const run = await client.actor("santhej/meta-ads-library-scraper").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 = { "searchTerms": ["nike"] } # Run the Actor and wait for it to finish run = client.actor("santhej/meta-ads-library-scraper").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 '{ "searchTerms": [ "nike" ] }' | apify call santhej/meta-ads-library-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=santhej/meta-ads-library-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Meta Ad Library Scraper - Facebook & Instagram Ads", "description": "Scrape the Meta (Facebook & Instagram) Ad Library by keyword or advertiser. Get ad creatives, copy, CTA, run dates, days-running (winning-ad signal), platforms and landing links. No login.", "version": "0.0", "x-build-id": "nps1Vg6lyGfeb2KcZ" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/santhej~meta-ads-library-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-santhej-meta-ads-library-scraper", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/santhej~meta-ads-library-scraper/runs": { "post": { "operationId": "runs-sync-santhej-meta-ads-library-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/santhej~meta-ads-library-scraper/run-sync": { "post": { "operationId": "run-sync-santhej-meta-ads-library-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "searchTerms": { "title": "Keywords / advertisers", "type": "array", "description": "Keywords or brand/advertiser names to search the Ad Library for, one per line.", "items": { "type": "string" } }, "country": { "title": "Country", "type": "string", "description": "Two-letter country code for the Ad Library, e.g. US, GB, DE.", "default": "US" }, "adActiveStatus": { "title": "Ad status", "enum": [ "all", "active", "inactive" ], "type": "string", "description": "Which ads to include.", "default": "all" }, "adType": { "title": "Ad type", "enum": [ "all", "political_and_issue_ads", "employment_ads", "housing_ads", "credit_ads" ], "type": "string", "description": "Ad category.", "default": "all" }, "mediaType": { "title": "Media type", "enum": [ "all", "image", "video", "meme" ], "type": "string", "description": "Filter by creative media.", "default": "all" }, "maxAdsPerTerm": { "title": "Max ads per term", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Maximum ads to collect per search term (1-1000).", "default": 50 }, "proxyConfiguration": { "title": "Proxy", "type": "object", "description": "Residential proxy is required for the Meta Ad Library.", "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 } } } } } } } } } } ```