# Internet Archive Scraper (`truenorth/internet-archive-scraper`) Actor

Search archive.org and export books, audio, video, software, images, metadata, and file lists as structured JSON or CSV.

- **URL**: https://apify.com/truenorth/internet-archive-scraper.md
- **Developed by:** [TrueNorth](https://apify.com/truenorth) (community)
- **Categories:** Developer tools, AI, Other
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $2.50 / 1,000 dataset items

This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage.

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

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
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.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — 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

## Internet Archive Scraper

Extract structured records from the Internet Archive for research, discovery, and bulk data export.

Use it to search archive.org by keyword or open a direct item/search URL and get clean dataset rows with optional full item metadata.

### Pricing

This Actor uses Pay-Per-Event pricing. Each record written to the default dataset triggers one `dataset-item` event at **USD 0.0025 per item (USD 2.50 per 1,000 items)**.

Platform usage costs are passed through separately and remain enabled. The minimum maximum total charge is **USD 2.00**. When a run reaches its charge limit, the current successfully stored item is included and the Actor stops further requests and dataset writes.

### What this Actor does

- Searches the Internet Archive public advanced search API.
- Supports keyword search, Lucene-style filters, and direct `startUrls`.
- Extracts normalized item fields ready for CSV, JSON, or downstream AI workflows.
- Optionally fetches full item metadata and file lists for each result.
- Handles pagination until the requested limit or end of results.

### Best for

- Digital humanities and archival research
- Media discovery and catalog exports
- Dataset building for AI and RAG pipelines
- Monitoring collections, creators, or topic-based archive search results

### Input example

```json
{
  "query": "frankenstein",
  "mediaType": "texts",
  "sortBy": "relevance",
  "maxItems": 50,
  "fetchItemDetails": false,
  "startUrls": []
}
```

#### Direct URL example

```json
{
  "startUrls": [
    { "url": "https://archive.org/details/frankenstein00shel" }
  ],
  "maxItems": 1,
  "fetchItemDetails": true
}
```

### Output sample

```json
{
  "identifier": "frankenstein00shel",
  "title": "Frankenstein",
  "url": "https://archive.org/details/frankenstein00shel",
  "creator": "Shelley, Mary",
  "date": "2022-01-01T00:00:00Z",
  "mediatype": "texts",
  "description": "Classic horror novel",
  "subject": ["Fiction", "Horror"],
  "language": "eng",
  "downloads": 1234,
  "collection": ["gutenberg", "opensource"],
  "licenseurl": "https://creativecommons.org/licenses/by/4.0/",
  "publicdate": "2020-01-01T00:00:00Z",
  "item_size": 1048576,
  "year": 2022,
  "format": ["PDF", "EPUB"],
  "files": null,
  "detailMetadata": null
}
```

### AI & LLM integration

The Actor outputs normalized JSON records with stable fields that are easy to index, summarize, cluster, or enrich.

Use the dataset for:

- RAG corpora built from archive metadata
- Topic and collection discovery
- Research automation and catalog QA
- Bulk exports into BI tools, vector databases, or ETL pipelines

### Notes

- `maxItems = 0` means unlimited scraping until the API result set is exhausted.
- `fetchItemDetails` adds full metadata requests and slows the run.
- Search queries can use Internet Archive Lucene syntax.
- When a search URL is provided, the Actor applies the configured media type filter.
- Every successful output record is written once to the default dataset and billed once as `dataset-item`.

# Actor input Schema

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

Search query using Lucene syntax. Examples: 'frankenstein', 'title:science', 'creator:"Mark Twain"', 'mediatype:texts subject:history'. Leave empty to use startUrls.

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

Filter results by media type.

## `sortBy` (type: `string`):

Sort order for results.

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

Maximum number of items to scrape. Use 0 for unlimited (up to API maximum).

## `fetchItemDetails` (type: `boolean`):

If enabled, fetches full metadata and file list for each item via the metadata API. Slower but provides more data.

## `startUrls` (type: `array`):

Optional list of Internet Archive search or detail URLs to scrape instead of using the query.

## Actor input object example

```json
{
  "query": "frankenstein",
  "mediaType": "any",
  "sortBy": "relevance",
  "maxItems": 50,
  "fetchItemDetails": false
}
```

# Actor output Schema

## `defaultDataset` (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("truenorth/internet-archive-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 = {}

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

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://mcp.apify.com/?tools=truenorth/internet-archive-scraper",
                "--header",
                "Authorization: Bearer <YOUR_API_TOKEN>"
            ]
        }
    }
}

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/9JUfkG4AYyKfojVJW/builds/7r1oSDqiZFKtPJb6u/openapi.json