# URL Metadata & OpenGraph Extractor (`scrapeworks/url-metadata`) Actor
Extract clean metadata from any list of URLs: title, description, preview image, favicon, site name, and more. Reconciles OpenGraph, Twitter Card, and standard meta tags into one tidy record per URL.
- **URL**: https://apify.com/scrapeworks/url-metadata.md
- **Developed by:** [Nicolas van Arkens](https://apify.com/scrapeworks) (community)
- **Categories:** Automation, Other, Open source
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks
- **User rating**: No ratings yet
## Pricing
from $1.00 / 1,000 results
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
## URL Metadata & OpenGraph Extractor ð
Give it any list of URLs and get back clean, unified metadata for each page â **title, description, preview image, favicon, site name, canonical URL, author, and published date**. It reconciles **OpenGraph**, **Twitter Card**, and standard `` tags into one tidy record per URL, so you don't have to care which tags a given site happens to use.
Perfect for building link previews, social-media tools, bookmarking apps, content curation, and SEO audits.
### Why use it
- ð **Reconciled metadata** â OpenGraph â Twitter Card â standard meta â ``, in priority order
- ðžïļ **Preview image & favicon** â resolved to absolute URLs (with a sensible favicon fallback)
- ð **Canonical URL** â and the final URL after redirects
- ð·ïļ **Rich fields** â site name, type, author, published date, theme color, Twitter card type, keywords
- ðĶ **Batch** â process many URLs in a single run
- ðŠķ **Fast & light** â metadata-only extraction, no heavy rendering
### Use cases
- **Link previews** â build rich cards like Slack, Discord, and iMessage show
- **Social media tools** â pull share metadata for scheduling and preview
- **Bookmarking & read-later apps** â auto-title and illustrate saved links
- **Content curation & newsletters** â enrich link lists with titles and images
- **SEO audits** â check OG/Twitter tags across a set of pages at once
### Input
| Field | Description |
|-------|-------------|
| **URLs** | List of page URLs (scheme added automatically if omitted). |
### Output
```json
{
"url": "https://example.com/page",
"finalUrl": "https://example.com/page",
"success": true,
"statusCode": 200,
"title": "OG Title Wins",
"description": "The OpenGraph description.",
"image": "https://cdn.example.com/img.jpg",
"siteName": "Example Site",
"type": "article",
"canonicalUrl": "https://example.com/canonical-page",
"favicon": "https://example.com/favicon.png",
"author": "Jane Author",
"publishedDate": "2026-05-01T10:00:00Z",
"twitterCard": "summary_large_image"
}
````
Export to JSON, CSV, or Excel, or pull via the Apify API.
### Notes
- Extracts metadata from publicly accessible pages only. Some sites block automated requests or require JavaScript rendering, in which case fewer fields may be available.
- Independent tool; respects each site's access policy. Please use responsibly.
# Actor input Schema
## `urls` (type: `array`):
List of page URLs to extract metadata from. A scheme is added automatically if you omit it (e.g. 'example.com' becomes 'https://example.com').
## Actor input object example
```json
{
"urls": [
"https://www.apify.com",
"https://github.com",
"https://news.ycombinator.com"
]
}
```
# 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://www.apify.com",
"https://github.com",
"https://news.ycombinator.com"
]
};
// Run the Actor and wait for it to finish
const run = await client.actor("scrapeworks/url-metadata").call(input);
// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`ðū 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);
});
// ð Want to learn more ð? Go to â 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://www.apify.com",
"https://github.com",
"https://news.ycombinator.com",
] }
# Run the Actor and wait for it to finish
run = client.actor("scrapeworks/url-metadata").call(run_input=run_input)
# Fetch and print Actor results from the run's dataset (if there are any)
print("ðū Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
print(item)
# ð Want to learn more ð? Go to â https://docs.apify.com/api/client/python/docs/quick-start
```
## CLI example
```bash
echo '{
"urls": [
"https://www.apify.com",
"https://github.com",
"https://news.ycombinator.com"
]
}' |
apify call scrapeworks/url-metadata --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=scrapeworks/url-metadata",
"--header",
"Authorization: Bearer <YOUR_API_TOKEN>"
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "URL Metadata & OpenGraph Extractor",
"description": "Extract clean metadata from any list of URLs: title, description, preview image, favicon, site name, and more. Reconciles OpenGraph, Twitter Card, and standard meta tags into one tidy record per URL.",
"version": "0.1",
"x-build-id": "bE81siDwTrSTM8Je7"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/scrapeworks~url-metadata/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-scrapeworks-url-metadata",
"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": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/scrapeworks~url-metadata/runs": {
"post": {
"operationId": "runs-sync-scrapeworks-url-metadata",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$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": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/scrapeworks~url-metadata/run-sync": {
"post": {
"operationId": "run-sync-scrapeworks-url-metadata",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$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": {
"urls": {
"title": "URLs",
"type": "array",
"description": "List of page URLs to extract metadata from. A scheme is added automatically if you omit it (e.g. 'example.com' becomes 'https://example.com').",
"items": {
"type": "string"
}
}
}
},
"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
}
}
}
}
}
}
}
}
}
}
```