# Internet Archive Public Domain Media and Texts Radar (`adaptive_nightmare/internet-archive-metadata-radar`) Actor

Extract Internet Archive Public Domain Media and Texts data in clean JSON or CSV. Query by query, mediaType, maxItems for records, engagement metrics, and metadata. Export datasets, run via API or Python/Node.js SDK, schedule runs, or integrate with webhooks with zero proxy fees.

- **URL**: https://apify.com/adaptive_nightmare/internet-archive-metadata-radar.md
- **Developed by:** [Darim Quraishi](https://apify.com/adaptive_nightmare) (community)
- **Categories:** Education, For creators, AI
- **Security:** Limited permissions
- **Stats:** 2 total users, 1 monthly users, 0.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

$1.10 / 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/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

## Internet Archive Public Domain Media and Texts Radar

Search millions of public domain audio recordings, digitized historical texts, vintage videos, and software items from the Internet Archive without API keys.

### Features

- Queries the comprehensive Internet Archive database spanning millions of open cultural media items.
- Captures verified item identifiers, work titles, primary creators, and historical publication years.
- Gathers cumulative download counts and sanitized descriptive annotations.
- Filters media items by format including books, audio recordings, films, software, and images.
- Connects directly to item detail portals and metadata JSON manifests.
- Operates via public REST endpoints with zero API keys and zero proxy overhead.
- Delivers clean records formatted for export to JSON, CSV, Excel, or digital humanities pipelines.

### Input parameters

- Search Query: Title, author, or keyword phrase (such as classic literature, old time radio, or silent film). Default is classic literature.
- Media Format Filter: Format type (books, audio, movies, software, images, or all). Default is texts.
- Maximum Items: Maximum number of records to retrieve, up to 100. Default is 25.

### Output dataset fields

Each dataset record contains:

- identifier: Unique Internet Archive resource path key.
- title: Full title headline.
- creator: Author, performer, or publishing institution.
- mediaType: Classification category (texts, audio, movies, software, image).
- year: Release or publication year.
- downloads: Total historical community download count.
- description: Sanitized summary text.
- detailsUrl: Direct link to view the item on archive.org.
- metadataApiUrl: Direct link to full technical metadata manifest.
- scrapedAt: UTC timestamp when the data was extracted.

### Use cases

- Digital humanities: Compile historical corpora across public domain literature collections.
- AI training: Retrieve open source texts and historical transcripts for language model pre-training.
- Media curation: Find vintage copyright-free audio recordings and educational films for video production.

# Actor input Schema

## `query` (type: `string`):

Keyword, title, or creator to search across Internet Archive collections (for example: classic literature, old time radio, or silent film).

## `mediaType` (type: `string`):

Filter by media classification.

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

Maximum number of items to retrieve.

## Actor input object example

```json
{
  "query": "classic literature",
  "mediaType": "texts",
  "maxItems": 25
}
```

# 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 = {};

// Run the Actor and wait for it to finish
const run = await client.actor("adaptive_nightmare/internet-archive-metadata-radar").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 = {}

# Run the Actor and wait for it to finish
run = client.actor("adaptive_nightmare/internet-archive-metadata-radar").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 '{}' |
apify call adaptive_nightmare/internet-archive-metadata-radar --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,adaptive_nightmare/internet-archive-metadata-radar"
        }
    }
}
```

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/ctMYKES0nOQipaWlf/builds/2FPS0H3ScKIoxx8IJ/openapi.json