# Meta Ad Library Scraper (`codingfrontend/meta-ad-library-scraper`) Actor A robust, high-performance utility designed for developer automation, data integration, and AI training. Features built-in captcha bypass, headful/headless browser execution, and proxy support to scrape Meta Ad Library data seamlessly, reliably, and at scale. - **URL**: https://apify.com/codingfrontend/meta-ad-library-scraper.md - **Developed by:** [Coding Frontned](https://apify.com/codingfrontend) (community) - **Categories:** Developer tools, Automation, AI - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $4.99 / 1,000 results This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows. 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 Apify actor to scrape Facebook and Instagram ad creatives from the [Meta Ad Library](https://www.facebook.com/ads/library/). > **Important**: The Meta Ad Library requires **residential proxies** to access reliably. Configure `proxyConfiguration` with `useApifyProxy: true` and `apifyProxyGroups: ["RESIDENTIAL"]`. Without residential proxies the scraper will encounter 403 errors or login walls. ### Features - Search the Meta Ad Library by keyword or brand name - Filter by country/region, ad type (political/commercial), and platform - Extract ad text, page name, status, start date, media URL, and impressions - Supports Facebook, Instagram, Audience Network, and Messenger platforms - Automatic scrolling and "See more" handling to load additional results - Proxy support for reliable scraping ### Input | Field | Type | Default | Description | |-------|------|---------|-------------| | `query` | string | `"Nike"` | Search term to look for in the Meta Ad Library | | `country` | string | `"US"` | ISO 3166-1 alpha-2 country code | | `maxItems` | integer | `50` | Maximum number of ads to scrape (1–5000) | | `adType` | string | `"all"` | Filter by ad type: `all`, `political`, `commercial` | | `platform` | string | `"all"` | Filter by platform: `all`, `facebook`, `instagram`, `audience_network`, `messenger` | | `proxyConfiguration` | object | Apify Proxy | Proxy settings for the scraper | #### Example Input ```json { "query": "Nike", "country": "US", "maxItems": 10, "adType": "all", "platform": "all" } ```` ### Output Each scraped ad contains: | Field | Type | Description | |-------|------|-------------| | `position` | integer | Result position index | | `pageName` | string | Name of the Facebook/Instagram page running the ad | | `adText` | string | Text content of the ad creative | | `platform` | string | Platform where the ad was shown | | `startDate` | string | Date the ad started running | | `status` | string | Current status of the ad (Active, Inactive, etc.) | | `link` | string | Link to the ad in the Meta Ad Library | | `mediaUrl` | string | URL of the ad image or video creative | | `impressions` | string | Impression count or range | | `query` | string | The search query used | | `country` | string | The country filter used | | `scrapedAt` | string | ISO timestamp of when the data was scraped | #### Example Output ```json { "position": 1, "pageName": "Nike", "adText": "Just Do It. Shop the latest Nike collection.", "platform": "facebook", "startDate": "Started running on Jan 10, 2025", "status": "Active", "link": "https://www.facebook.com/ads/library/?id=123456789", "mediaUrl": "https://scontent.xx.fbcdn.net/v/example.jpg", "impressions": "10K-50K", "query": "Nike", "country": "US", "scrapedAt": "2025-01-25T10:30:00.000Z" } ``` ### Usage #### Apify Console 1. Go to the actor's page on Apify Console 2. Set the input parameters 3. Click "Start" #### API ```javascript import { ApifyClient } from 'apify-client'; const client = new ApifyClient({ token: 'YOUR_TOKEN' }); const run = await client.actor('your-username/meta-ad-library-scraper').call({ query: 'Nike', country: 'US', maxItems: 50, adType: 'all', platform: 'facebook', }); const { items } = await client.dataset(run.defaultDatasetId).listItems(); console.log(items); ``` ### Local Development ```bash npm install npm start ``` ### Project Structure ``` src/ ├── main.js # Actor entry point — reads input, configures crawler ├── constants.js # Shared constants (URLs, maps, limits) ├── routes.js # Crawler route handlers (search page) └── utils/ ├── extraction.js # Ad card data extraction from page DOM └── url.js # URL builder for Meta Ad Library search ``` # Actor input Schema ## `query` (type: `string`): The search term to look for in the Meta Ad Library. ## `country` (type: `string`): ISO 3166-1 alpha-2 country code to filter ads by region. ## `maxItems` (type: `integer`): Maximum number of ads to scrape. ## `adType` (type: `string`): Filter by ad type. ## `platform` (type: `string`): Filter ads by platform. ## `proxyConfiguration` (type: `object`): Proxy settings for the scraper. ## Actor input object example ```json { "query": "Nike", "country": "US", "maxItems": 50, "adType": "all", "platform": "all", "proxyConfiguration": { "useApifyProxy": true } } ``` # 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 = { "query": "Nike", "country": "US", "maxItems": 50, "proxyConfiguration": { "useApifyProxy": true } }; // Run the Actor and wait for it to finish const run = await client.actor("codingfrontend/meta-ad-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 = { "query": "Nike", "country": "US", "maxItems": 50, "proxyConfiguration": { "useApifyProxy": True }, } # Run the Actor and wait for it to finish run = client.actor("codingfrontend/meta-ad-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 '{ "query": "Nike", "country": "US", "maxItems": 50, "proxyConfiguration": { "useApifyProxy": true } }' | apify call codingfrontend/meta-ad-library-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=codingfrontend/meta-ad-library-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Meta Ad Library Scraper", "description": "A robust, high-performance utility designed for developer automation, data integration, and AI training. Features built-in captcha bypass, headful/headless browser execution, and proxy support to scrape Meta Ad Library data seamlessly, reliably, and at scale.", "version": "1.0", "x-build-id": "taPPEw0QBhPVgxEds" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/codingfrontend~meta-ad-library-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-codingfrontend-meta-ad-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/codingfrontend~meta-ad-library-scraper/runs": { "post": { "operationId": "runs-sync-codingfrontend-meta-ad-library-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/codingfrontend~meta-ad-library-scraper/run-sync": { "post": { "operationId": "run-sync-codingfrontend-meta-ad-library-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "required": [ "query" ], "properties": { "query": { "title": "Search Query", "type": "string", "description": "The search term to look for in the Meta Ad Library.", "default": "Nike" }, "country": { "title": "Country Code", "type": "string", "description": "ISO 3166-1 alpha-2 country code to filter ads by region.", "default": "US" }, "maxItems": { "title": "Max Items", "minimum": 1, "maximum": 5000, "type": "integer", "description": "Maximum number of ads to scrape.", "default": 50 }, "adType": { "title": "Ad Type", "enum": [ "all", "political", "commercial" ], "type": "string", "description": "Filter by ad type.", "default": "all" }, "platform": { "title": "Platform", "enum": [ "all", "facebook", "instagram", "audience_network", "messenger" ], "type": "string", "description": "Filter ads by platform.", "default": "all" }, "proxyConfiguration": { "title": "Proxy Configuration", "type": "object", "description": "Proxy settings for the scraper.", "default": { "useApifyProxy": true } } } }, "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 } } } } } } } } } } ```