# Meta Ad Library Scraper | Ad Copy & Landing Pages (`happyfhantum/meta-ad-library-intelligence-scraper`) Actor Pull competitor ads from the public Meta Ad Library with ad copy, CTAs, landing pages, and angle tags. - **URL**: https://apify.com/happyfhantum/meta-ad-library-intelligence-scraper.md - **Developed by:** [Kelsey Todd](https://apify.com/happyfhantum) (community) - **Categories:** Lead generation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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 | Ad Copy & Landing Pages Pull competitor ads from the public Meta Ad Library and turn them into a clean report of ad copy, CTAs, and landing pages. ### What it does - Searches the public Meta Ad Library using either plain keywords or full Ad Library URLs. - Returns structured rows for each ad with page info, CTA, copy, title, platform coverage, and media links. - Fetches landing pages to capture the final URL, domain, page title, and meta description. - Tags common messaging angles like `ai`, `automation`, `free_trial`, `demo`, and `discount`. - Generates a creative cluster ID so similar ads can be grouped, compared, or deduped downstream. - Adds swipe-file-friendly fields like `hook_style`, `cta_intent`, `swipe_file_label`, and `ad_takeaway`. - Writes a `SWIPE_FILE.md` artifact so the strongest ads are easy to skim without filtering the dataset first. - Produces flat output that is easy to export into CSV, Sheets, Airtable, or downstream analysis workflows. ### Current scope This version reads the first result page for each search and enriches those ads with landing-page data and angle tags. In practice, that usually means up to 30 ads per search. The value in this release is the structure and enrichment, not deep pagination. ### Best fit - agencies doing competitor ad research - founders tracking competitor hooks and offers - media buyers collecting landing-page examples - marketers building swipe files from public Meta ads # Actor input Schema ## `searchTerms` (type: `array`): Brand names or keywords to search in the Meta Ad Library. ## `adLibraryUrls` (type: `array`): Paste full Meta Ad Library URLs if you want to control the exact search or advertiser view. ## `country` (type: `string`): Meta Ad Library country code for the search. ## `activeStatus` (type: `string`): Filter ads by current activity state. ## `adType` (type: `string`): Meta Ad Library ad type filter. ## `searchType` (type: `string`): Meta Ad Library search mode. ## `maxAdsPerSearch` (type: `integer`): Caps how many ads are returned per search. The current implementation reads the first result page only, which usually contains up to 30 ads. ## `includeLandingPageIntel` (type: `boolean`): Fetch landing pages for unique ad URLs and return final domains, titles, and meta descriptions when available. ## `landingPageFetchLimit` (type: `integer`): Max number of unique landing pages to fetch per search. ## `includeInactiveVariants` (type: `boolean`): Keep ads that are returned in the result set even if their current active flag is false. ## `requestTimeoutSecs` (type: `integer`): Maximum time to wait for a Meta or landing-page request before skipping it. ## `debugMode` (type: `boolean`): Include extra debug fields and warnings in the output. ## Actor input object example ```json { "searchTerms": [ "notion", "zapier" ], "country": "US", "activeStatus": "active", "adType": "all", "searchType": "keyword_unordered", "maxAdsPerSearch": 30, "includeLandingPageIntel": true, "landingPageFetchLimit": 10, "includeInactiveVariants": true, "requestTimeoutSecs": 30, "debugMode": false } ```` # 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": [ "notion", "zapier" ] }; // Run the Actor and wait for it to finish const run = await client.actor("happyfhantum/meta-ad-library-intelligence-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": [ "notion", "zapier", ] } # Run the Actor and wait for it to finish run = client.actor("happyfhantum/meta-ad-library-intelligence-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": [ "notion", "zapier" ] }' | apify call happyfhantum/meta-ad-library-intelligence-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=happyfhantum/meta-ad-library-intelligence-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Meta Ad Library Scraper | Ad Copy & Landing Pages", "description": "Pull competitor ads from the public Meta Ad Library with ad copy, CTAs, landing pages, and angle tags.", "version": "0.1", "x-build-id": "m2yz9vicyUOUOVxha" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/happyfhantum~meta-ad-library-intelligence-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-happyfhantum-meta-ad-library-intelligence-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/happyfhantum~meta-ad-library-intelligence-scraper/runs": { "post": { "operationId": "runs-sync-happyfhantum-meta-ad-library-intelligence-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/happyfhantum~meta-ad-library-intelligence-scraper/run-sync": { "post": { "operationId": "run-sync-happyfhantum-meta-ad-library-intelligence-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": "Search terms", "type": "array", "description": "Brand names or keywords to search in the Meta Ad Library.", "items": { "type": "string" } }, "adLibraryUrls": { "title": "Ad Library URLs", "type": "array", "description": "Paste full Meta Ad Library URLs if you want to control the exact search or advertiser view.", "items": { "type": "string" } }, "country": { "title": "Country", "type": "string", "description": "Meta Ad Library country code for the search.", "default": "US" }, "activeStatus": { "title": "Active status", "enum": [ "active", "all" ], "type": "string", "description": "Filter ads by current activity state.", "default": "active" }, "adType": { "title": "Ad type", "enum": [ "all", "political_and_issue_ads" ], "type": "string", "description": "Meta Ad Library ad type filter.", "default": "all" }, "searchType": { "title": "Search type", "enum": [ "keyword_unordered", "keyword_exact_phrase" ], "type": "string", "description": "Meta Ad Library search mode.", "default": "keyword_unordered" }, "maxAdsPerSearch": { "title": "Max ads per search", "minimum": 1, "maximum": 30, "type": "integer", "description": "Caps how many ads are returned per search. The current implementation reads the first result page only, which usually contains up to 30 ads.", "default": 30 }, "includeLandingPageIntel": { "title": "Include landing page intel", "type": "boolean", "description": "Fetch landing pages for unique ad URLs and return final domains, titles, and meta descriptions when available.", "default": true }, "landingPageFetchLimit": { "title": "Landing page fetch limit", "minimum": 0, "maximum": 50, "type": "integer", "description": "Max number of unique landing pages to fetch per search.", "default": 10 }, "includeInactiveVariants": { "title": "Include inactive variants", "type": "boolean", "description": "Keep ads that are returned in the result set even if their current active flag is false.", "default": true }, "requestTimeoutSecs": { "title": "Request timeout (seconds)", "minimum": 5, "maximum": 60, "type": "integer", "description": "Maximum time to wait for a Meta or landing-page request before skipping it.", "default": 30 }, "debugMode": { "title": "Debug mode", "type": "boolean", "description": "Include extra debug fields and warnings in the output.", "default": false } } }, "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 } } } } } } } } } } ```