Goodreads Book Reviews Scraper

Extract reviews from any Goodreads book. Get full review text, star ratings, reviewer names, like counts, and shelves. No login required.

What you get

One row per review on the book's reviews list:

• reviewId, url — direct link to the review on goodreads.com
• rating — 1-5 stars (integer) or null if the reviewer left no rating
• text — full review body (may include simple HTML formatting from the source)
• reviewer, reviewerProfileUrl, reviewerAvatarUrl
• reviewDate — ISO timestamp of last revision (falls back to created date)
• likeCount — how many readers found the review helpful
• commentCount — replies to the review
• shelves — array of shelf / tag names the reviewer added the book to (e.g. ["read", "favorites"])
• bookTitle, bookAuthor, bookId
• scrapedAt

Plus a separate book metadata record (in the Key-value store under BOOK_INFO):

• bookId, isbn, isbn13, title, author, authors, description
• avgRating, ratingCount, reviewCount, pageCount
• publisher, publishDate, language, genres, coverImageUrl, seriesName, url

Use cases

• Market research — analyse sentiment and themes across thousands of book reviews
• Author / publisher monitoring — track new reviews and ratings on your titles
• Recommendation engines — pull labelled review datasets for ML training
• Competitor analysis — compare reception of similar titles in a genre
• Editorial discovery — find the most-liked review for a book to surface as a quote

How to use

1. Enter the Book — accepts any of:
   • Goodreads URL: https://www.goodreads.com/book/show/3735293-clean-code
   • Numeric book ID: 3735293
   • ISBN-10 or ISBN-13: 0132350882 or 9780132350884
2. Choose Sort By — Newest first, Oldest first, or Most liked / popular
3. Set Max Items — default 100, or 0 for unlimited (runs until the actor timeout, then writes a resume cursor)
4. Run the actor — reviews appear in the Dataset tab
5. Open the Key-value store tab to find the BOOK_INFO record (book metadata)

After the run finishes, open the Key-value store tab → copy the NEXT_PAGE_ID value → paste it into Page ID on your next run. If NEXT_PAGE_ID is null, you've fetched every review.

Output format

Review row:

{
  "reviewId": "kca://review:goodreads/amzn1.gr.review:goodreads.v1.iN5cdJz1YxW_sjbgeoNPcA",
  "url": "https://www.goodreads.com/review/show/1221838816",
  "rating": 5,
  "text": "Excellent book on writing maintainable code...",
  "reviewer": "Oana Sipos",
  "reviewerProfileUrl": "https://www.goodreads.com/user/show/7205156-oana-sipos",
  "reviewerAvatarUrl": "https://i.gr-assets.com/images/.../7205156._UX200_CR0,0,200,200_.jpg",
  "reviewDate": "2022-02-03T10:37:09.774Z",
  "likeCount": 368,
  "commentCount": 0,
  "shelves": ["read", "programming"],
  "bookTitle": "Clean Code: A Handbook of Agile Software Craftsmanship",
  "bookAuthor": "Robert C. Martin",
  "bookId": "3735293",
  "scrapedAt": "2026-05-27T10:00:00.000Z"
}

Book metadata record (Key-value store BOOK_INFO):

{
  "bookId": "3735293",
  "isbn": "0132350882",
  "isbn13": "9780132350884",
  "title": "Clean Code: A Handbook of Agile Software Craftsmanship",
  "author": "Robert C. Martin",
  "authors": ["Robert C. Martin"],
  "description": "Even bad code can function...",
  "avgRating": 4.35,
  "ratingCount": 23639,
  "reviewCount": 1487,
  "pageCount": 464,
  "publisher": "Pearson",
  "publishDate": "2008-08-01T07:00:00.000Z",
  "language": "English",
  "genres": ["Programming", "Computer Science", "Technology"],
  "coverImageUrl": "https://m.media-amazon.com/images/.../3735293.jpg",
  "seriesName": null,
  "url": "https://www.goodreads.com/book/show/3735293-clean-code"
}