# Met Museum Scraper - Artworks, Artists & Images (`logiover/met-museum-collection-scraper`) Actor

Scrape the Metropolitan Museum of Art collection in bulk. Extract object title, artist, nationality, dates, medium, dimensions, department, classification, culture, credit line, public-domain status and image URLs to CSV/JSON. No API key, no login.

- **URL**: https://apify.com/logiover/met-museum-collection-scraper.md
- **Developed by:** [Logiover](https://apify.com/logiover) (community)
- **Categories:** Developer tools
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $3.50 / 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.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
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.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## 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.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## Met Museum Scraper - Artworks, Artists & Images

![Apify Actor](https://img.shields.io/badge/Apify-Actor-00A67E?logo=apify\&logoColor=white) ![No API key](https://img.shields.io/badge/No%20API%20key-required-2ea44f) ![Pay per result](https://img.shields.io/badge/Pricing-Pay%20per%20result-1C7ED6) ![Export](https://img.shields.io/badge/Export-JSON%20%7C%20CSV%20%7C%20Excel-F59E0B)

**Scrape the Metropolitan Museum of Art collection in bulk. Extract object title, artist, nationality, dates, medium, dimensions, department, classification, culture, credit line, public-domain status and image URLs to CSV/JSON. No API key, no login.**

***

### What does the Met Museum Scraper do?

This Actor turns Met Museum data into a structured dataset. You point it at one department or a search across the collection, it walks the result pages one after another, and it writes one clean row per object into your dataset — ready to export as JSON, CSV or Excel, or to pull straight from the Apify API.

The Metropolitan Museum of Art publishes its entire 500,000-object catalogue through a keyless API, with much of it released into the public domain. The index endpoint returns object IDs only, so the Actor resolves each record in a bounded parallel pool — that is what turns a list of numbers into rows carrying artist, medium, provenance and image links. Pagination is followed automatically until it runs out of results, hits your page limit or hits your **Max items** cap, whichever comes first. Every row is de-duplicated across the whole run, so you are never billed twice for the same object.

There is **no API key, no login and no browser** involved. That keeps runs fast and cheap, and it means you can schedule the Actor without worrying about credentials expiring.

### Who is it for?

- **AI and ML teams** sourcing public-domain imagery with provenance.
- **Publishers and documentary researchers** finding reproducible artwork.
- **Art historians** analysing a collection quantitatively.
- **Educators** assembling primary-source image sets.
- **Developers** building discovery tools over open museum data.

### Use cases

- Export a department's holdings with artist and date for every object.
- Filter to public-domain works you can legally reproduce.
- Analyse how a collection is distributed by culture, period or medium.
- Build a labelled art image dataset with full attribution.
- Find which objects are currently on view in the galleries.

### Why use this Met Museum Scraper?

- 🔑 **Keyless** — no account, no API token, no cookies to paste.
- 📦 **34 fields per object** — everything the source exposes, already typed.
- 📄 **Real pagination** — it walks page after page instead of returning the first screen.
- 🎯 **Precise caps** — **Max items** stops the run exactly where you want it, so the bill is predictable.
- 📊 **Export anywhere** — JSON, CSV, Excel or HTML, plus the Apify API and integrations.
- 💸 **Pay per result** — you pay for rows you actually receive, with no platform fees to calculate.

### What data can you extract?

Every run produces one row per object, with these fields:

| Field | Type | Description |
|-------|------|-------------|
| `objectId` | string | Met object identifier |
| `title` | string | Object title |
| `objectUrl` | string | Met collection page for the object |
| `artistDisplayName` | string | Artist as displayed by the museum |
| `artistNationality` | string | Artist nationality when recorded |
| `artistBeginDate` | string | Artist's birth year as recorded |
| `artistEndDate` | string | Artist's death year as recorded |
| `objectDate` | string | Date as displayed, often a range or circa |
| `objectBeginDate` | number | Earliest year the museum assigns |
| `objectEndDate` | number | Latest year the museum assigns |
| `medium` | string | Materials and technique |
| `dimensions` | string | Dimensions as recorded |
| `department` | string | Curatorial department |
| `classification` | string | Object classification, e.g. Paintings or Ceramics |
| `culture` | string | Culture of origin when recorded |
| `period` | string | Period when recorded |
| `dynasty` | string | Dynasty when recorded |
| `country` | string | Country of origin when recorded |
| `creditLine` | string | Acquisition credit line |
| `accessionNumber` | string | Museum accession number |
| `accessionYear` | string | Year the object entered the collection |
| `isHighlight` | boolean | Whether the museum marks it as a highlight |
| `isPublicDomain` | boolean | Whether the image is released into the public domain |
| `isOnView` | boolean | Whether a gallery number is assigned |
| `galleryNumber` | string | Gallery the object is displayed in |
| `primaryImage` | string | Full-resolution image URL |
| `primaryImageSmall` | string | Web-resolution image URL |
| `additionalImageCount` | number | Number of additional images |
| `tags` | string | Subject tags, separated by a pipe |
| `objectWikidataUrl` | string | Wikidata link for the object when recorded |
| `artistWikidataUrl` | string | Wikidata link for the artist when recorded |
| `query` | string | Search term or department this row came from |
| `page` | number | Batch the object was resolved in |
| `scrapedAt` | string | ISO timestamp of extraction |

#### Output example

```json
{
  "objectId": "435948",
  "title": "Portrait of a Bearded Man in Black",
  "objectUrl": "https://www.metmuseum.org/art/collection/search/435948",
  "artistDisplayName": "Corneille de Lyon",
  "artistNationality": "Netherlandish",
  "artistBeginDate": "1533",
  "artistEndDate": "1575",
  "objectDate": null,
  "objectBeginDate": 1533,
  "objectEndDate": 1575,
  "medium": "Oil on wood",
  "dimensions": "6 3/4 x 6 1/4 in. (17.1 x 15.9 cm)",
  "department": "European Paintings",
  "classification": "Paintings",
  "culture": null,
  "period": null,
  "dynasty": null,
  "country": null,
  "creditLine": "Bequest of George D. Pratt, 1935",
  "accessionNumber": "1978.301.6",
  "accessionYear": "1978",
  "isHighlight": false,
  "isPublicDomain": true,
  "isOnView": false,
  "galleryNumber": null,
  "primaryImage": "https://images.metmuseum.org/CRDImages/ep/original/DP-13587-001.jpg",
  "primaryImageSmall": "https://images.metmuseum.org/CRDImages/ep/web-large/DP-13587-001.jpg",
  "additionalImageCount": 0,
  "tags": "Men | Portraits",
  "objectWikidataUrl": "https://www.wikidata.org/wiki/Q19913191",
  "artistWikidataUrl": "https://www.wikidata.org/wiki/Q720941",
  "query": "department 11",
  "page": 1,
  "scrapedAt": "2026-09-21T10:18:44.902Z"
}
```

### How to use

#### Option A — one department

```json
{
  "departmentId": 11,
  "imagesOnly": true,
  "maxItems": 300,
  "searchQuery": ""
}
```

1. Open the Actor and fill in the first field.
2. Set the page limit and **Max items** to bound the run.
3. Click **Start**, then export from the **Output** tab.

#### Option B — a search across the collection

```json
{
  "departmentId": 0,
  "imagesOnly": true,
  "maxItems": 500,
  "searchQuery": "monet"
}
```

Everything in the first field is processed independently, so you can batch several targets into one run and split them apart afterwards.

### Input parameters

| Parameter | Type | Default | Description |
|-----------|------|---------|-------------|
| `searchQuery` | string | `–` | Search the collection, e. |
| `departmentId` | integer | `0` | Restrict to one curatorial department, e. |
| `imagesOnly` | boolean | `true` | Only include objects that have a photograph. |
| `maxItems` | integer | `300` | Stop after this many objects. |
| `maxConcurrency` | integer | `2` | Parallel requests. |
| `proxyConfiguration` | object | `{"useApifyProxy": true}` | Proxy used to fetch pages. |

### Tips for best results

- Leave the search term empty to walk the catalogue in object-ID order — about 500,000 records.
- **Department** narrows the walk to one curatorial area; there are nineteen of them.
- **Images only** restricts a search to objects that actually have a photograph.
- `isPublicDomain` true is the filter to use when you intend to republish an image.
- `primaryImage` is full resolution and can be tens of megabytes; `primaryImageSmall` is the web copy.
- The index endpoint returns IDs only, so each object costs one request — roughly a sixth of a second.
- `objectBeginDate` and `objectEndDate` are numeric and sortable; `objectDate` is the human label.
- Many records are catalogue entries without photographs, so image fields are legitimately empty there.
- Always keep `objectUrl` and `creditLine` with any image you republish, for attribution.
- Pair with the Art Institute and Cleveland Museum scrapers for a wider open-collection set.

### Run outcomes: an empty result is not the same as a blocked run

A crawl that was refused and a query that genuinely has no matches both produce an empty dataset. Treating them as the same thing makes the output unusable for monitoring, so every run is classified and an unverified emptiness is never reported as success.

| Outcome | Run status | Meaning |
|---|---|---|
| `COMPLETED` | Succeeded | Everything requested was fetched and rows were saved. |
| `PARTIAL` | Succeeded | Rows were saved, but some pages were unreachable. |
| `EMPTY_RESULT` | Succeeded | Pages were fetched and parsed and this query really has no objects. |
| `BLOCKED` | **Failed** | Every request was refused with HTTP 403/429/503. |
| `NOT_FOUND` | **Failed** | The source answered 404; check the address or filters. |
| `FETCH_FAILED` | **Failed** | Nothing could be fetched for transport reasons. |
| `INCOMPLETE_NO_ROWS` | **Failed** | Pages were skipped and nothing was saved, so an empty result cannot be trusted. |

The rule behind the table: **a run that fetched nothing can never finish as Succeeded.** Each run also writes a `SUMMARY` record to the key-value store with pages requested, pages fetched, pages skipped, HTTP request and retry counts, a per-status breakdown, transport errors and the termination reason — so the row count is always reconcilable with what the run actually did.

### Integrations

Send results straight into the tools you already use: **Google Sheets**, **Slack**, **Zapier**, **Make**, **Airtable** or any **Webhook**. You can also **schedule** the Actor to run hourly, daily or weekly and have each run append to the same dataset, which is how you build a price or availability history rather than a one-off snapshot.

### API usage

Run the Actor and collect results from any language. Replace `<YOUR_TOKEN>` with your Apify API token.

**cURL**

```bash
curl -X POST "https://api.apify.com/v2/acts/logiover~met-museum-collection-scraper/run-sync-get-dataset-items?token=<YOUR_TOKEN>" \
  -H "Content-Type: application/json" \
  -d '{"departmentId": 11, "imagesOnly": true, "maxItems": 300, "searchQuery": ""}'
```

**Node.js**

```javascript
import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: '<YOUR_TOKEN>' });
const run = await client.actor('logiover/met-museum-collection-scraper').call({"departmentId": 11, "imagesOnly": true, "maxItems": 300, "searchQuery": ""});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);
```

**Python**

```python
from apify_client import ApifyClient

client = ApifyClient('<YOUR_TOKEN>')
run = client.actor('logiover/met-museum-collection-scraper').call(run_input={"departmentId": 11, "imagesOnly": true, "maxItems": 300, "searchQuery": ""})
for item in client.dataset(run['defaultDatasetId']).iterate_items():
    print(item)
```

### Use with AI agents (MCP)

This Actor is available through the Apify MCP server, so an AI agent can call it as a tool. Point your agent at `https://mcp.apify.com` and it can run the Met Museum Scraper on demand — for example: *"Pull the first 500 objects from Met Museum and summarise what you find."* The agent receives the same structured rows you would get from the UI.

### FAQ

#### Do I need a Met Museum account or API key?

No. The Actor reads publicly available data only. There is nothing to authenticate and no credentials to rotate.

#### How many objects can I get in one run?

As many as the source exposes. Raise the page limit and **Max items** together; the run stops at whichever limit it reaches first.

#### Why did I get fewer rows than I asked for?

The source ran out of objects. That is normal for narrow searches — broaden the query or add more targets to one run.

#### Are results de-duplicated?

Yes. Each object is emitted once per run, even when it appears on several pages, so you are never billed twice for the same record.

#### Why are some fields empty?

Met Museum does not publish every attribute for every object. Empty means the source did not supply it, not that extraction failed.

#### What export formats are supported?

JSON, CSV, Excel, HTML and RSS from the **Output** tab, plus the Apify API and any integration you connect.

#### How fast is it?

It is pure HTTP with no browser, so a page of results typically takes a second or two. Raise **Max concurrency** carefully — the source rate-limits aggressive crawling.

#### Can I schedule it?

Yes. Use the Apify scheduler to run it on any interval and append each run to the same dataset for time-series analysis.

#### Does it work behind a proxy?

It uses Apify Proxy automatically. You can switch groups or supply your own proxies in **Proxy configuration**.

#### How often does the data change?

Met Museum updates continuously. Re-run whenever you need current data; the Actor always reads the live source, never a cache.

#### Is the output schema stable?

Yes. Field names and types are fixed, so downstream pipelines will not break between runs.

#### What if the source changes its format?

Open an issue on the **Issues** tab and it gets fixed. The Actor is actively maintained.

### Is it legal?

This Actor reads only publicly available data from Met Museum — the same content any visitor or client sees without logging in. It does not bypass authentication, does not collect private data and does not attempt to defeat access controls. You are responsible for how you use the output: respect the source's terms of service, applicable copyright, and data-protection law such as GDPR where personal data is involved. Scraping public data is generally lawful in the EU and the US, but the responsibility for the downstream use of that data sits with you.

### Related scrapers

- [Art Institute Scraper](https://apify.com/logiover/artic-artwork-scraper) — Open-access artworks
- [Cleveland Museum Scraper](https://apify.com/logiover/cleveland-art-scraper) — CC0 collection
- [Library of Congress Scraper](https://apify.com/logiover/loc-digital-collections-scraper) — Historical imagery
- [Openverse Scraper](https://apify.com/logiover/openverse-scraper) — Openly licensed media

# Actor input Schema

## `searchQuery` (type: `string`):

Search the collection, e.g. monet, vase, samurai. Leave empty to walk the catalogue in object-ID order.

## `departmentId` (type: `integer`):

Restrict to one curatorial department, e.g. 11 for European Paintings. Set 0 for all departments.

## `imagesOnly` (type: `boolean`):

Only include objects that have a photograph. Applies when a search term is given.

## `maxItems` (type: `integer`):

Stop after this many objects. Set 0 for no limit.

## `maxConcurrency` (type: `integer`):

Parallel requests. Lower this if the source rate-limits you.

## `proxyConfiguration` (type: `object`):

Proxy used to fetch pages. Defaults to Apify Proxy (automatic). You usually do not need to change this.

## Actor input object example

```json
{
  "searchQuery": "",
  "departmentId": 0,
  "imagesOnly": true,
  "maxItems": 300,
  "maxConcurrency": 2,
  "proxyConfiguration": {
    "useApifyProxy": true
  }
}
```

# Actor output Schema

## `results` (type: `string`):

All objects extracted in this run.

# 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 = {
    "departmentId": 0,
    "maxItems": 300,
    "maxConcurrency": 2
};

// Run the Actor and wait for it to finish
const run = await client.actor("logiover/met-museum-collection-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 '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "departmentId": 0,
    "maxItems": 300,
    "maxConcurrency": 2,
}

# Run the Actor and wait for it to finish
run = client.actor("logiover/met-museum-collection-scraper").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "departmentId": 0,
  "maxItems": 300,
  "maxConcurrency": 2
}' |
apify call logiover/met-museum-collection-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,logiover/met-museum-collection-scraper"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/6R7dKX29KqrwqKabd/builds/WYqrBQDs0dv8QFWxV/openapi.json