# 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 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 ## 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 '' with your token const client = new ApifyClient({ 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 '' with your token. client = ApifyClient("") # 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 " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Internet Archive Scraper", "description": "Search archive.org and export books, audio, video, software, images, metadata, and file lists as structured JSON or CSV.", "version": "1.0", "x-build-id": "7r1oSDqiZFKtPJb6u" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/truenorth~internet-archive-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-truenorth-internet-archive-scraper", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/truenorth~internet-archive-scraper/runs": { "post": { "operationId": "runs-sync-truenorth-internet-archive-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/truenorth~internet-archive-scraper/run-sync": { "post": { "operationId": "run-sync-truenorth-internet-archive-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "query": { "title": "Search Query", "type": "string", "description": "Search query using Lucene syntax. Examples: 'frankenstein', 'title:science', 'creator:\"Mark Twain\"', 'mediatype:texts subject:history'. Leave empty to use startUrls." }, "mediaType": { "title": "Media Type Filter", "enum": [ "any", "texts", "audio", "movies", "software", "image", "etree", "data", "web", "collection" ], "type": "string", "description": "Filter results by media type.", "default": "any" }, "sortBy": { "title": "Sort By", "enum": [ "relevance", "downloads desc", "date desc", "date asc", "title asc" ], "type": "string", "description": "Sort order for results.", "default": "relevance" }, "maxItems": { "title": "Max Items", "minimum": 0, "maximum": 10000, "type": "integer", "description": "Maximum number of items to scrape. Use 0 for unlimited (up to API maximum).", "default": 50 }, "fetchItemDetails": { "title": "Fetch Item Details", "type": "boolean", "description": "If enabled, fetches full metadata and file list for each item via the metadata API. Slower but provides more data.", "default": false }, "startUrls": { "title": "Start URLs", "type": "array", "description": "Optional list of Internet Archive search or detail URLs to scrape instead of using the query.", "items": { "type": "object", "properties": { "url": { "type": "string", "title": "URL", "description": "URL of the Internet Archive item or search page" } } } } } }, "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 } } } } } } } } } } ```