Open Library Book Scraper — Search, Works & ISBN | from $0.50/1K

Built for reading-app developers, academic researchers, and LLM dataset builders who need structured book metadata by ISBN, keyword, or Open Library work ID. Official public API — no proxy, no auth, zero COGS.

Scrape book data from Open Library (openlibrary.org) via the official public API. No proxy. No auth. No browser. Pay per result.

Features

• Keyword search — full-text across title, author, subject, or all fields
• Work lookup — direct fetch by Open Library work ID with description + subjects
• ISBN lookup — resolve ISBN-10 or ISBN-13 to edition data + parent work link
• Ratings — optional star ratings (average + count) via the ratings API
• Cover images — cover URLs in S / M / L size
• parse_confidence — machine-readable quality score in every record
• Flat schema — 19 fields, ready for CSV/JSON/BigQuery

Why this beats the alternatives

Actor	Price	API	Ratings	parse_confidence	Proxy needed
This actor	$0.50/1k	Official OL API	✓	✓	No
Most OL scrapers	$3–10/1k	HTML scraping	—	No	Required
Generic book scrapers	$5+/1k	DOM	—	No	Required

Our edge: official Open Library JSON API (zero-auth, extremely stable), ratings support, parse_confidence field for drift detection, multi-mode batch processing.

Input modes

Mode	How to use	Returns
searchQueries	List of keyword queries	Work records matching query
workIds	List of OL work IDs (e.g. OL893415W)	Full work data + optional ratings
isbns	List of ISBN-10 or ISBN-13	Edition data + parent work link

Output schema

Field	Type	Description
work_key	string	Open Library work ID (e.g. /works/OL893415W)
title	string	Book title
authors	array	Author names (search) or author OL IDs (work/isbn)
first_publish_year	integer	Year of first publication
isbn_list	array	ISBNs (up to 20)
subjects	array	Subject / genre tags (up to 30)
edition_count	integer	Number of editions
language	array	Language codes (e.g. ["eng", "spa"])
cover_url	string	Cover image URL (S/M/L size)
ratings_avg	number	Star rating average (0–5)
ratings_count	integer	Number of ratings
number_of_pages	integer	Page count (median for search, edition for ISBN)
description	string	Work synopsis (work mode) or publisher info (isbn mode)
url	string	Canonical Open Library URL
source	string	search | work | isbn
query	string	Input that produced this record
scraped_at	string	ISO 8601 UTC run timestamp
parse_confidence	number	Quality score 0–1 (1.0 = perfect)
warnings	array	Machine-readable quality warnings

Pricing

Pay-per-result: from $0.50 per 1,000 books (book-item event). No subscription. Pay only for what you extract.

Volume	Cost
100 books	~$0.05
1,000 books	~$0.50
10,000 books	~$5.00

Open Library is a public archive — no compute-heavy rendering, minimal latency. A typical run of 1,000 search results costs approximately $0.002 in compute.

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FAQ

Do I need a proxy or API key? No. Open Library is a fully public API hosted by the Internet Archive — no authentication or proxy required.

What output formats are available? JSON, CSV, and Excel — downloadable from the Apify dataset UI or via the REST API.

Can I schedule this to run automatically? Yes. Use Apify's scheduler to refresh your book catalog on a cron schedule, and push updates via webhook.

What if a barcode or work ID returns no result? The actor logs a warning and skips the record — it does not crash. Sparse or community-contributed entries (rare/old books) may have some fields null; parse_confidence flags these automatically.

Notes

• authors field: search mode returns human-readable names. Work/ISBN mode returns Open Library author IDs (e.g. /authors/OL79034A) — a subsequent work lookup per author would be needed for names.
• fetchRatings=true adds one extra HTTP request per book — use only when ratings data is essential.
• Open Library has no official rate limit for read access. The actor makes sequential requests to stay well within reasonable limits.
• Data is community-contributed and may be incomplete for rare/old books.

Example use cases

• Academic research — find all editions of a work by ISBN or subject
• Library catalog augmentation — enrich ISBN lists with metadata
• Reading apps — build book recommendation datasets
• Data journalism — analyze publishing trends by year, language, subject

Not affiliated with the Internet Archive or openlibrary.org.

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Use with AI agents (MCP)

This actor is available as an MCP tool for Claude, GPT-4, and other AI agents that support the Model Context Protocol:

https://mcp.apify.com/?tools=bovi/openlibrary-scraper

Pass an ISBN or keyword and get back structured book records — ideal for reading-app AI features and bibliographic RAG pipelines.

Integrations

Built for reading-app developers and LLM dataset builders resolving ISBNs and keywords to structured book metadata — the JSON/dataset output drops into the tools you already run, no glue code:

• n8n / Make / Zapier — trigger a run or pipe every new dataset item into 500+ apps (Google Sheets, Airtable, Slack, HubSpot, your database) with no code: n8n, Make, Zapier.
• Webhooks — fire your own endpoint the moment a run finishes, to push results straight into your pipeline (docs).
• MCP server — expose this actor as a tool to Claude, Cursor, or any MCP client so an AI agent can pull this data mid-conversation (guide).
• API & SDKs — fetch the dataset as JSON, CSV, or Excel through the Apify REST API or the Python / JS SDKs.

See all Apify integrations.

Usage statistics

This Actor creates a small, content-free summary at the end of each run. It is used only to monitor reliability and improve this Actor. A copy is saved as USAGE_STATS in your own Apify key-value store, so you can see the exact record created for your run.

Set disableUsageStats to true in the input to opt out. Nothing is sent then; your USAGE_STATS record only says that statistics were disabled.

Only these fields are recorded:

• schema version, Actor name and build number;
• UTC start and finish hour (not a precise timestamp);
• run duration, number of results and time to the first result, each as a coarse range;
• whether the result was empty, the end status, and an error type from a fixed list;
• memory setting and counts of charged events;
• names of the input fields you set, never their values;
• the selected option for input fields that offer a fixed list of choices (for example a sort order).

We do not collect input text, search terms, URLs, domains, usernames, email addresses, names, proxy credentials, tokens, scraped records, output items, raw error messages, stack traces, or hashes of any of those values. Records are kept for no longer than 13 months, used only as aggregated operational statistics, and never sold or shared.

Additional fields (Phase 2)

This Actor also records your Apify user ID, whether Apify marks the account as paying, the size range of list inputs, the selected country when the input offers a fixed list of countries, and one category from a fixed Actor taxonomy. We use these fields only for aggregate reliability, repeat-use and cross-Actor analysis; reports suppress any cell with fewer than five distinct users.

The same disableUsageStats: true input flag turns these fields off too. The user ID is removed after 13 months; we do not export, sell, share, or attempt to re-identify this data.

Run-outcome signals (v2)

To learn whether a run did what it was asked to do, the record also holds a few more coarse ranges and yes/no flags. None of them contains content:

• the result limit you asked for (a range, when the input has one) and what share of it was delivered;
• results delivered per input item you listed (a range);
• output quality as ranges: how fully the result fields were filled, the share of rows that look like errors, the share of duplicate rows, and how many different fields appeared. These are counted in memory while results are saved; no result content is kept;
• how the run was started (console, API, schedule, webhook, another Actor);
• how it ended: stopped by you, timed out, reached the requested limit, stopped by the charge limit, and how many times the platform moved the run;
• if this Actor reports it: how many items to process worked or failed (ranges) and one failure reason from a fixed list;
• a short code made from the names of the input fields you set, never their values.

Repeat-run fingerprint (v2)

When your Apify user ID is recorded (see above), the record also holds an 8-character one-way code made from your input (proxy settings left out) and this Actor's name. It only lets us see that the same account ran the same input again soon after an unsatisfying run; we never see the input itself. It is stored only in the database, never published, and reports use it in aggregate with the same five-user minimum. It is the one exception to the statement above that no hashes are collected, and disableUsageStats: true turns it off.