# Open Library ISBN Book Metadata Scraper (`jungle_synthesizer/openlibrary-isbn-book-metadata-scraper`) Actor Bulk-enrich ISBNs with full Open Library metadata: title, authors, publishers, subjects, ratings, reading-status counts, and cross-reference identifiers (Goodreads, LibraryThing, LCCN, OCLC, Wikidata). Accepts up to thousands of ISBNs in a single run. - **URL**: https://apify.com/jungle\_synthesizer/openlibrary-isbn-book-metadata-scraper.md - **Developed by:** [BowTiedRaccoon](https://apify.com/jungle_synthesizer) (community) - **Categories:** AI, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per event 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 ## Open Library ISBN Book Metadata Scraper Bulk-enrich ISBN lists with complete Open Library metadata in a single run. Accepts up to thousands of ISBN-10 or ISBN-13 identifiers and returns a full record per book: title, authors, publishers, subjects, cover image URLs, reader ratings, reading-status counts, and the cross-reference identifier block (Goodreads, LibraryThing, LCCN, OCLC) that joins Open Library data to every other book dataset. ### What You Get Each output record includes: | Field | Description | |-------|-------------| | `isbn_input` | The ISBN you provided | | `isbn_10` / `isbn_13` | Normalized edition identifiers | | `work_key` | Open Library work key (e.g. `/works/OL45804W`) | | `title` / `subtitle` | Book title and subtitle | | `authors` | Pipe-separated author names | | `author_keys` | Pipe-separated Open Library author OLIDs | | `publishers` | Up to 10 publisher names, pipe-separated | | `publish_date` | Edition publication date | | `number_of_pages` | Median page count across editions | | `subjects` | Full subject heading list, pipe-separated | | `description` | Work description / summary | | `first_sentence` | Opening sentence of the work | | `cover_url_small/medium/large` | Direct cover image URLs (S/M/L sizes) | | `languages` | Language codes, pipe-separated (e.g. `eng|fre|spa`) | | `edition_count` | Total number of known editions | | `first_publish_year` | Year of first publication | | `ratings_average` | Average reader rating (0-5) | | `ratings_count` | Number of ratings | | `want_to_read_count` | Users who want to read this book | | `currently_reading_count` | Users currently reading | | `already_read_count` | Users who have finished the book | | `id_goodreads` | Goodreads ID(s), pipe-separated | | `id_librarything` | LibraryThing ID(s), pipe-separated | | `id_lccn` | Library of Congress Control Number(s) | | `id_oclc` | OCLC/WorldCat number(s) | | `open_library_url` | Direct link to the work on Open Library | ### Use Cases - **Catalog enrichment** - add cover art, descriptions, and subject tags to an existing book database - **RAG / LLM pipelines** - bulk-enrich a reading list with structured metadata for retrieval-augmented generation - **Library tech** - match ISBNs to LCCN/OCLC/Goodreads identifiers for cross-system deduplication - **Book recommendation systems** - pull ratings, reading-status counts, and subject headings at scale - **Research** - build datasets from ISBN lists for book market or publishing research ### Input ```json { "isbns": [ "9780140328721", "9780385121675", "0062316095" ], "maxItems": 0 } ```` | Field | Type | Description | |-------|------|-------------| | `isbns` | array (required) | List of ISBN-10 or ISBN-13 strings. Hyphens are stripped automatically. Mixed formats accepted. | | `maxItems` | integer | Maximum records to return. Set to `0` (default) for no limit. | ### How It Works The actor calls the Open Library search.json API - one request per ISBN - and extracts the top-ranked work match. Cover image URLs are composed directly from the ISBN using the Open Library Covers API (no extra HTTP call). Requests are rate-limited to approximately 1 per second to respect Open Library's guidelines. No authentication, no API key, and no proxy are required. ### Data Source Open Library (openlibrary.org) is an open, editable library catalog, part of the Internet Archive. It covers approximately 40 million editions and 30 million works. Open Library data is released under CC0 1.0 Universal - free to use, share, and adapt for any purpose. ### Notes - Results represent the best work-level match for each ISBN. Ratings and reading-status counts are aggregated at the work level across all editions. - Some fields (`description`, `first_sentence`, `subtitle`) may be absent for less-documented books. - `id_goodreads` and `id_librarything` may contain multiple IDs (one per edition) - deduplicated and pipe-separated. - For very large ISBN lists (10,000+), use `maxItems` to chunk runs or set a generous timeout in run options. # Actor input Schema ## `sp_intended_usage` (type: `string`): Please describe how you plan to use the data extracted by this crawler. ## `sp_improvement_suggestions` (type: `string`): Provide any feedback or suggestions for improvements. ## `sp_contact` (type: `string`): Provide your email address so we can get in touch with you. ## `isbns` (type: `array`): List of ISBN-10 or ISBN-13 identifiers to look up. Mixed formats are accepted. ## `maxItems` (type: `integer`): Maximum number of book records to return (0 = no limit) ## Actor input object example ```json { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "isbns": [ "9780140328721", "9780385121675", "9780062316097" ], "maxItems": 10 } ``` # 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 '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "isbns": [ "9780140328721", "9780385121675", "9780062316097" ], "maxItems": 10 }; // Run the Actor and wait for it to finish const run = await client.actor("jungle_synthesizer/openlibrary-isbn-book-metadata-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 = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "isbns": [ "9780140328721", "9780385121675", "9780062316097", ], "maxItems": 10, } # Run the Actor and wait for it to finish run = client.actor("jungle_synthesizer/openlibrary-isbn-book-metadata-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 '{ "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "isbns": [ "9780140328721", "9780385121675", "9780062316097" ], "maxItems": 10 }' | apify call jungle_synthesizer/openlibrary-isbn-book-metadata-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jungle_synthesizer/openlibrary-isbn-book-metadata-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Open Library ISBN Book Metadata Scraper", "description": "Bulk-enrich ISBNs with full Open Library metadata: title, authors, publishers, subjects, ratings, reading-status counts, and cross-reference identifiers (Goodreads, LibraryThing, LCCN, OCLC, Wikidata). Accepts up to thousands of ISBNs in a single run.", "version": "0.1", "x-build-id": "GhJuIb1UB7zQM3ibt" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jungle_synthesizer~openlibrary-isbn-book-metadata-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jungle_synthesizer-openlibrary-isbn-book-metadata-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/jungle_synthesizer~openlibrary-isbn-book-metadata-scraper/runs": { "post": { "operationId": "runs-sync-jungle_synthesizer-openlibrary-isbn-book-metadata-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/jungle_synthesizer~openlibrary-isbn-book-metadata-scraper/run-sync": { "post": { "operationId": "run-sync-jungle_synthesizer-openlibrary-isbn-book-metadata-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", "required": [ "isbns" ], "properties": { "sp_intended_usage": { "title": "What is the intended usage of this data?", "minLength": 1, "type": "string", "description": "Please describe how you plan to use the data extracted by this crawler." }, "sp_improvement_suggestions": { "title": "How can we improve this crawler for you?", "minLength": 1, "type": "string", "description": "Provide any feedback or suggestions for improvements." }, "sp_contact": { "title": "Contact Email", "minLength": 1, "type": "string", "description": "Provide your email address so we can get in touch with you." }, "isbns": { "title": "ISBNs", "type": "array", "description": "List of ISBN-10 or ISBN-13 identifiers to look up. Mixed formats are accepted.", "items": { "type": "string" } }, "maxItems": { "title": "Max Items", "type": "integer", "description": "Maximum number of book records to return (0 = no limit)", "default": 0 } } }, "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 } } } } } } } } } } ```