Goodreads Books & Reviews Scraper

Extract public Goodreads book metadata, aggregate ratings, and reader reviews from Goodreads book URLs or search queries.

What does Goodreads Books & Reviews Scraper do?

Goodreads Books & Reviews Scraper collects structured data from public Goodreads book pages.

It can save book metadata rows and public review rows in the same dataset.

Use it when you need book-market signals without manually copying Goodreads pages.

Who is it for?

📚 Publishers can monitor reader reaction to backlist and frontlist titles.

✍️ Authors can analyze public review themes around comparable books.

🏫 Libraries can enrich catalog analysis with ratings and public reader feedback.

📈 Market researchers can compare reader sentiment across genres and authors.

🛒 Book marketers can build review exports for campaign planning.

Why use this Goodreads scraper?

It accepts direct Goodreads book URLs for precise targeting.

It also accepts search queries and resolves matching public book pages.

It returns clean fields such as title, authors, ratings, review counts, reviewer names, review text, and visible engagement counts.

It is designed with low default limits so first test runs stay inexpensive.

What Goodreads data can you extract?

Category	Example fields
Book identity	bookId, bookUrl, title
Author data	authors, authorUrls
Metadata	description, genres, coverUrl, bookFormat, numberOfPages, inLanguage
Ratings	averageRating, ratingsCount, reviewsCount
Reviews	reviewId, reviewUrl, reviewerName, reviewerUrl, rating, reviewText
Engagement	likesCount, commentsCount

How much does it cost to scrape Goodreads books and reviews?

This actor uses pay-per-event pricing.

There is a small start event per run.

Each saved book metadata row or review row is charged as one item.

With the default input, the run is intentionally small: one book plus a limited number of reviews.

Increase maxReviewsPerBook only after confirming the output matches your needs.

Quick start

1. Open the actor on Apify.

2. Add one or more Goodreads book URLs.

3. Optionally add search queries such as the great gatsby.

4. Choose whether to include reviews.

5. Set maxReviewsPerBook.

6. Run the actor.

7. Download the dataset as JSON, CSV, Excel, or through the API.

Input: Goodreads book URLs

Use bookUrls when you already know the exact Goodreads pages.

Example:

{
  "bookUrls": [
    { "url": "https://www.goodreads.com/book/show/41733839-the-great-gatsby" }
  ],
  "includeReviews": true,
  "maxReviewsPerBook": 10
}

Input: search queries

Use queries when you want the actor to find matching public Goodreads books first.

Example:

{
  "queries": ["the great gatsby"],
  "includeReviews": true,
  "maxReviewsPerBook": 5
}

Input options

Field	Type	Description
bookUrls	array	Goodreads book pages to scrape
queries	array	Goodreads book searches to resolve
includeReviews	boolean	Save public review rows
maxReviewsPerBook	integer	Review cap per book page
proxyConfiguration	object	Optional Apify Proxy settings

Output example

{
  "itemType": "book",
  "bookId": "41733839",
  "bookUrl": "https://www.goodreads.com/book/show/41733839-the-great-gatsby",
  "title": "The Great Gatsby",
  "authors": ["F. Scott Fitzgerald"],
  "averageRating": 3.93,
  "ratingsCount": 6067294,
  "reviewsCount": 136391
}

Review output example

{
  "itemType": "review",
  "bookId": "41733839",
  "bookTitle": "The Great Gatsby",
  "reviewerName": "Nataliya",
  "rating": 5,
  "reviewText": "Public review text visible on Goodreads..."
}

Tips for better results

Use direct Goodreads book URLs when accuracy matters most.

Keep maxReviewsPerBook low while testing.

Run several smaller jobs instead of one very large exploratory job.

Use query mode for discovery, then re-run with selected direct URLs.

Disable reviews when you only need metadata and aggregate ratings.

Integrations

Send exported Goodreads review data to Google Sheets for editorial review.

Load CSV exports into Airtable to tag books by genre, rating, and sentiment.

Push JSON results into a warehouse for trend dashboards.

Connect the actor to Make or Zapier through Apify webhooks.

Use Apify API clients to schedule recurring monitoring of selected titles.

API usage with Node.js

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const run = await client.actor('fetch_cat/goodreads-books-reviews-scraper').call({
  bookUrls: [{ url: 'https://www.goodreads.com/book/show/41733839-the-great-gatsby' }],
  includeReviews: true,
  maxReviewsPerBook: 5,
});
console.log(run.defaultDatasetId);

API usage with Python

from apify_client import ApifyClient
import os

client = ApifyClient(os.environ['APIFY_TOKEN'])
run = client.actor('fetch_cat/goodreads-books-reviews-scraper').call(run_input={
    'queries': ['the great gatsby'],
    'includeReviews': True,
    'maxReviewsPerBook': 5,
})
print(run['defaultDatasetId'])

API usage with cURL

curl -X POST 'https://api.apify.com/v2/acts/fetch_cat~goodreads-books-reviews-scraper/runs?token=APIFY_TOKEN' \
  -H 'Content-Type: application/json' \
  -d '{"queries":["the great gatsby"],"includeReviews":true,"maxReviewsPerBook":5}'

MCP usage

Use this actor from Claude Desktop or Claude Code through the Apify MCP server.

MCP URL pattern:

https://mcp.apify.com/?tools=fetch_cat/goodreads-books-reviews-scraper

Claude Code setup:

$claude mcp add apify-goodreads-books-reviews --url "https://mcp.apify.com/?tools=fetch_cat/goodreads-books-reviews-scraper"

Claude Desktop JSON config:

{
  "mcpServers": {
    "apify-goodreads-books-reviews": {
      "url": "https://mcp.apify.com/?tools=fetch_cat/goodreads-books-reviews-scraper"
    }
  }
}

Example prompt:

Run the Goodreads Books & Reviews Scraper for The Great Gatsby and summarize recurring reader complaints from the first 20 reviews.

Data quality notes

Goodreads page layouts can vary by book.

Some fields may be null when Goodreads does not show them publicly.

Review dates and engagement counts are saved only when visible on the public page.

Search queries may return several matching editions.

Legality and ethical use

This actor extracts publicly available Goodreads pages.

You are responsible for using the results in line with applicable laws, Goodreads terms, and privacy rules.

Do not use scraped data for spam, harassment, or invasive profiling.

Respect reasonable limits and avoid unnecessary high-volume runs.

Troubleshooting: no results

Check that the Goodreads URL is public and starts with /book/show/.

Try a direct book URL instead of a broad query.

Lower the review cap and run again.

If your network is blocked, enable Apify Proxy in the input.

Troubleshooting: missing reviews

Some books show fewer public review cards than their aggregate review count.

Goodreads may personalize, paginate, or hide parts of the review list.

The actor saves public review cards visible in the fetched page.

FAQ

Can I scrape private Goodreads shelves?

No. This actor is for public book pages and public review content only.

Can I scrape every review for a popular book?

The first version focuses on visible public review cards with a per-book cap. Use reasonable caps for cost control.

Can I search by ISBN?

If Goodreads autocomplete resolves the ISBN query to a public book page, the actor can scrape that page.

Does it require a Goodreads account?

No account is required for the public data targeted by this actor.

Related scrapers

Explore other Apify actors from our catalog for book, review, and web data workflows.

Use actor combinations to enrich Goodreads exports with website metadata, sentiment scoring, or notification workflows.

Changelog

Initial version: direct Goodreads book URL scraping, query resolution, book metadata rows, and public review rows.

Support

If a run does not produce the expected public book data, share the run URL and the input so we can investigate.