# Open Graph & Link Preview Extractor (`mighty_monk/open-graph-extractor`) Actor Extract Open Graph, Twitter Card, and meta tags from any URL in bulk. Build link previews, social cards, and unfurl data for your apps and automation workflows. - **URL**: https://apify.com/mighty\_monk/open-graph-extractor.md - **Developed by:** [Harsh](https://apify.com/mighty_monk) (community) - **Categories:** Developer tools, Automation - **Stats:** 3 total users, 2 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $3.00 / 1,000 url extracteds 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 ## Open Graph & Link Preview Extractor Extract **Open Graph**, **Twitter Card**, and other social meta tags from any URL in bulk. Use the results to build link previews, social cards, and unfurl data for your apps. ### Input | Field | Type | Required | Default | Description | |-------|------|----------|---------|-------------| | `urls` | `string[]` | Yes | — | URLs to extract meta tags from | | `includeTwitterCards` | `boolean` | No | `true` | Include Twitter Card meta tags | | `timeoutMs` | `integer` | No | `15000` | Per-request timeout in milliseconds | ### Output (per URL) | Field | Description | |-------|-------------| | `url` | Input URL | | `title` | Page title (`og:title` or ``) | | `description` | Page description (`og:description` or `meta description`) | | `ogImage` | Open Graph image URL (resolved to absolute) | | `ogType` | Open Graph type (`website`, `article`, etc.) | | `siteName` | Open Graph site name | | `twitterCard` | Twitter Card type | | `twitterImage` | Twitter Card image URL | | `twitterTitle` | Twitter Card title | | `twitterDescription` | Twitter Card description | | `canonicalUrl` | Canonical link URL | | `favicon` | Favicon URL | | `locale` | Open Graph locale | | `error` | Error message if extraction failed (null on success) | ### How it works 1. Fetches each URL with **axios** (configurable timeout, follows redirects) 2. Parses HTML with **cheerio** 3. Extracts `meta[property^="og:"]`, `meta[name^="twitter:"]`, `link[rel=canonical]`, and `link[rel=icon]` 4. Resolves relative image and link URLs against the page URL 5. Processes all URLs in parallel with graceful per-URL error handling ### Run locally ```bash apify run ```` Example input (`storage/key_value_stores/default/INPUT.json`): ```json { "urls": ["https://apify.com", "https://github.com"], "includeTwitterCards": true, "timeoutMs": 15000 } ``` ### Deploy to Apify ```bash apify login apify push ``` ### Resources - [Apify SDK for JavaScript](https://docs.apify.com/sdk/js/) - [Input schema](https://docs.apify.com/platform/actors/development/input-schema) - [Web scraping with Axios and Cheerio](https://blog.apify.com/web-scraping-with-axios-and-cheerio/) # Actor input Schema ## `urls` (type: `array`): List of URLs to extract Open Graph and social meta tags from. ## `includeTwitterCards` (type: `boolean`): Whether to extract Twitter Card meta tags (twitter:card, twitter:image, etc.). ## `timeoutMs` (type: `integer`): Maximum time in milliseconds to wait for each URL request. ## Actor input object example ```json { "urls": [ "https://apify.com", "https://github.com" ], "includeTwitterCards": true, "timeoutMs": 15000 } ``` # Actor output Schema ## `results` (type: `string`): No description # 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 '<YOUR_API_TOKEN>' with your token const client = new ApifyClient({ token: '<YOUR_API_TOKEN>', }); // Prepare Actor input const input = { "urls": [ "https://apify.com", "https://github.com" ] }; // Run the Actor and wait for it to finish const run = await client.actor("mighty_monk/open-graph-extractor").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 '<YOUR_API_TOKEN>' with your token. client = ApifyClient("<YOUR_API_TOKEN>") # Prepare the Actor input run_input = { "urls": [ "https://apify.com", "https://github.com", ] } # Run the Actor and wait for it to finish run = client.actor("mighty_monk/open-graph-extractor").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 '{ "urls": [ "https://apify.com", "https://github.com" ] }' | apify call mighty_monk/open-graph-extractor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=mighty_monk/open-graph-extractor", "--header", "Authorization: Bearer <YOUR_API_TOKEN>" ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Open Graph & Link Preview Extractor", "description": "Extract Open Graph, Twitter Card, and meta tags from any URL in bulk. Build link previews, social cards, and unfurl data for your apps and automation workflows.", "version": "0.1", "x-build-id": "5LY4fLSW86NSt4adD" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/mighty_monk~open-graph-extractor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-mighty_monk-open-graph-extractor", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/mighty_monk~open-graph-extractor/runs": { "post": { "operationId": "runs-sync-mighty_monk-open-graph-extractor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/mighty_monk~open-graph-extractor/run-sync": { "post": { "operationId": "run-sync-mighty_monk-open-graph-extractor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "urls" ], "properties": { "urls": { "title": "URLs", "type": "array", "description": "List of URLs to extract Open Graph and social meta tags from.", "items": { "type": "string" } }, "includeTwitterCards": { "title": "Include Twitter Cards", "type": "boolean", "description": "Whether to extract Twitter Card meta tags (twitter:card, twitter:image, etc.).", "default": true }, "timeoutMs": { "title": "Request timeout (ms)", "minimum": 1000, "maximum": 120000, "type": "integer", "description": "Maximum time in milliseconds to wait for each URL request.", "default": 15000 } } }, "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 } } } } } } } } } } ```