Letterboxd Scraper — Film Ratings, Reviews & User Data (No API / No login)

Scrape film ratings, user reviews, cast & crew, genres and list data from Letterboxd — the world's leading social film-discovery platform with 17M+ members. Extract each film's average rating, ratings count, director, cast, genres, runtime, synopsis and poster, plus every user review as its own row with reviewer, star rating, full review text, like count, spoiler flag and review date. Also pull user watch histories and curated lists in clean structured JSON. Static HTML parsing — no browser, no API key, no login.

🏆 Why this Letterboxd scraper?

4 scrape modes · reviews saved 1-per-row · thousands of reviews per run · direct HTTP + HTML (no browser) · residential-proxy ready · export to JSON / CSV / Excel. The unofficial Letterboxd API alternative for film sentiment analysis, ratings datasets and audience research.

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✨ What this Actor does / Key features

• 🎬 4 scrape modes — film (details + reviews), user (watch history + personal ratings), list (curated lists), and search (find films by keyword, then scrape their pages).
• ⭐ Ratings & reviews — Letterboxd average rating (0–5 scale), total ratings count, and full user reviews with reviewer name, username, star rating and like count.
• 📝 Reviews as their own rows — every review is saved as a separate dataset record (type: "review"), ready to export straight to CSV/Excel or feed an NLP pipeline.
• 📖 Full review text — Letterboxd truncates long reviews with a "…more" link; the scraper fetches the complete text from the /s/full-text/ endpoint so nothing is cut off.
• 🎭 Rich film metadata — director(s), top cast (up to 10), genres, runtime, primary language, country, synopsis and poster image URL.
• 👤 User watch history — extract a user's full watched-film list along with their personal star ratings (type: "user_film").
• 📋 List scraping — scrape any public Letterboxd list (Official Top 250, decade lists, genre collections) with each film's position and curator note.
• 🔍 Keyword search — search films by title/keyword and optionally follow through to full film details and reviews.
• 📄 Automatic pagination — every mode paginates multi-page results; the reviews handler queues review pages ahead of time so a single blocked page never kills the rest.
• 🛡️ Proxy support — Apify residential proxy integration; required, because Letterboxd actively blocks datacenter IPs.

🚀 Quick start (3 steps)

1. Configure — pick a mode and paste a Letterboxd URL (film, user profile or list) into Letterboxd URLs, or add keywords for search mode. Toggle Scrape Reviews and set Max Reviews Per Film if you want.
2. Run — click Start. The Actor fetches each page, parses the HTML, saves film metadata and streams reviews into your dataset.
3. Get your data — open the Output tab and export to JSON, CSV, Excel or XML, or pull it via the Apify API.

📥 Input

Pick a mode, then give the Actor one or more startUrls (or searchQueries for search mode). Everything else is optional.

Example — film details + reviews

{
  "mode": "film",
  "startUrls": [
    { "url": "https://letterboxd.com/film/the-godfather/" },
    { "url": "https://letterboxd.com/film/inception/" }
  ],
  "scrapeReviews": true,
  "fetchFullText": true,
  "maxReviews": 100,
  "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": ["RESIDENTIAL"] }
}

Example — a user's watch history & personal ratings

{
  "mode": "user",
  "startUrls": [
    { "url": "https://letterboxd.com/username/" }
  ],
  "maxFilms": 200
}

Example — scrape a curated list

{
  "mode": "list",
  "startUrls": [
    { "url": "https://letterboxd.com/dave/list/official-top-250-narrative-feature-films/" }
  ],
  "maxFilms": 250
}

Example — search films by keyword

{
  "mode": "search",
  "searchQueries": ["stanley kubrick", "neon noir 2020s"],
  "maxFilms": 30,
  "scrapeReviews": true,
  "maxReviews": 50
}

Field	Type	Description
mode	string	What to scrape: film, user, list or search. Required.
startUrls	array	Letterboxd URLs — film pages (/film/the-godfather/), user profiles (/username/) or list pages (/username/list/listname/). The scraper auto-detects the URL type and routes it.
searchQueries	array	Film titles/keywords to search for (used when mode is search). Each query scrapes the results page, then visits each matching film.
scrapeReviews	boolean	Save each film's user reviews as their own dataset rows (reviewer, rating, full text, likes, spoiler flag, date, URL). Default true.
fetchFullText	boolean	Fetch the complete text of long/truncated reviews (one extra request per long review). Default true.
maxReviews	integer	Cap reviews extracted per film (1–1000). The reviews page is paginated until this is reached. Default 20.
maxFilms	integer	Cap total films for user/list/search modes (1–1000). Does not apply to individual film URLs. Default 50.
proxyConfiguration	object	Apify Proxy settings. Residential is required — Letterboxd blocks datacenter IPs. Default RESIDENTIAL.

Finding a list URL: open the list on letterboxd.com and copy the full path including the list slug (e.g. /dave/list/official-top-250-narrative-feature-films/). For user modes, paste the profile root (letterboxd.com/username/) — the scraper reads /username/films/ automatically, so the profile must be public. A film's slug is the segment after /film/ (e.g. the-godfather); pasting a /reviews/ URL works too, it is normalised back to the film page.

📤 Output

Records are typed by a type field: film, review, user_film, list_film or search_result. Each is exportable to JSON, CSV, Excel or XML. Here is a trimmed film record plus a review row:

{
  "type": "film",
  "filmSlug": "the-godfather",
  "title": "The Godfather",
  "year": "1972",
  "director": ["Francis Ford Coppola"],
  "cast": ["Marlon Brando", "Al Pacino", "James Caan", "Robert Duvall"],
  "genres": ["Crime", "Drama"],
  "runtime": "175 mins",
  "averageRating": 4.55,
  "ratingsCount": 1284310,
  "language": "English",
  "country": "USA",
  "synopsis": "The story spans the years from 1945 to 1955 and chronicles the Corleone crime family…",
  "posterUrl": "https://a.ltrbxd.com/resized/…/the-godfather.jpg",
  "filmUrl": "https://letterboxd.com/film/the-godfather/",
  "scrapedAt": "2026-07-06T12:00:00.000Z"
}

{
  "type": "review",
  "filmTitle": "The Godfather",
  "filmYear": "1972",
  "filmSlug": "the-godfather",
  "filmUrl": "https://letterboxd.com/film/the-godfather/",
  "reviewer": "Philbert Dy",
  "username": "philbertdy",
  "rating": 5,
  "reviewText": "A perfect film. Every frame is composed like a painting, every performance is…",
  "isSpoiler": false,
  "likes": 842,
  "reviewUrl": "https://letterboxd.com/philbertdy/film/the-godfather/",
  "reviewDate": "2026-06-28",
  "scrapedAt": "2026-07-06T12:00:00.000Z"
}

💡 Use cases

• Sentiment analysis — extract thousands of user reviews with star ratings for NLP training, sentiment classification and opinion mining, each review already on its own row.
• Recommendation engines — build training datasets from community ratings widely considered more nuanced and cinephile-accurate than mainstream platforms.
• Ratings datasets — collect averageRating and ratingsCount across films, genres and decades for market and reception research.
• Audience & taste profiling — scrape user watch histories (userRating) to map viewing patterns and genre affinities for collaborative filtering.
• List intelligence — turn famous community lists (Top 250, decade/genre lists) into structured datasets for editorial or research use.
• Marketing & audience research — measure how a title, director or genre is received by a highly engaged film community.

👥 Who uses it

AI/ML engineers building recommendation & sentiment models · data scientists & researchers · film journalists & critics · streaming/marketing analysts · academics studying audience reception · data teams assembling movie ratings & review datasets.

💰 Pricing

This Actor runs on a simple pay-per-result model — you pay for the film and review records you extract, with no separate Apify platform fees to calculate. Try it on the free tier first, then scale up. See the Pricing tab on this page for the current rate.

❓ Frequently Asked Questions

Is it legal to scrape Letterboxd? The Actor only collects publicly available film, review and list data. You are responsible for using the data in compliance with Letterboxd's terms and applicable laws such as GDPR.

Does Letterboxd have a public API? Letterboxd has no open public data API — its website is the only source of ratings and reviews. This Actor parses Letterboxd's static HTML pages instead, giving you structured film data without any API key.

Do I need an API key or a login? No. There is no Letterboxd account, login or API key required — only an Apify account. The Actor reads public Letterboxd pages directly over HTTP.

Do I need a proxy? Yes — residential. Letterboxd actively blocks datacenter IPs, so the Actor defaults to the Apify RESIDENTIAL proxy group. The reviews path (/reviews/by/activity/) occasionally returns 403 on a few IPs in a row, so the Actor uses a generous retry budget to rotate onto a clean IP without burning the run.

How much data can I get? Thousands of reviews per run. Set maxReviews up to 1000 per film and run across many films; user and list modes paginate up to maxFilms (up to 1000). For big sentiment datasets, set maxReviews to 100–200 across 50+ films.

Is this a Letterboxd API alternative?

Yes. Letterboxd offers no open public API, so this Actor works as an unofficial Letterboxd API alternative — it parses the site's static HTML and returns structured film ratings, reviews, user watch histories and lists as JSON, with no API key.

Can I scrape Letterboxd without an API or login?

Yes. No Letterboxd account, login or API key is needed — only an Apify account and a residential proxy. The Actor pulls publicly available pages directly.

How do I export Letterboxd reviews and ratings to CSV or JSON?

Every film, review, user-film and list-film record is saved as its own dataset row, so you can export the full Letterboxd dataset to CSV, JSON, Excel or XML straight from the run — ready for spreadsheets or ML pipelines.

How do I scrape all reviews for a specific film on Letterboxd?

Use mode: "film" with the film URL and scrapeReviews on. The Actor paginates the film's reviews page and saves each review as its own row with reviewer, star rating and full text — and with fetchFullText on, long reviews aren't truncated.

Can I scrape a Letterboxd user's watchlist or watch history?

Yes. Run mode: "user" with a public profile URL (letterboxd.com/username/) to extract that user's watched film history and personal star ratings as a structured export.

Can I scrape a Letterboxd Top 250 list into a dataset?

Yes. Set mode: "list" and paste the full list URL including its slug to export every film in the list with its position, title, year and curator note.

🔗 More scrapers by logiover

Building a film, entertainment or social-media dataset? Pair Letterboxd with the rest of the media & review suite:

Source	Actor
🎬 IMDb	IMDb Scraper
📺 TV shows	TVmaze Scraper
🍥 Anime	MyAnimeList Scraper
💿 Music	Discogs Scraper · Resident Advisor Scraper
🎮 Games	Steam Game Reviews Scraper
🟣 Live streaming	Twitch Scraper
🎧 Podcasts	Apple Podcasts Episode Scraper
▶️ YouTube	YouTube Video Details Scraper · YouTube Comments Scraper
👽 Reddit	Reddit Search Scraper
✍️ Newsletters	Substack Newsletter Scraper

👉 Browse all logiover scrapers on Apify Store — 180+ actors across real estate, jobs, crypto, social media & B2B data.

⏰ Scheduling & integration

Schedule this Actor on Apify to track new reviews or ratings daily or weekly. Export results to JSON, CSV or Excel, sync to Google Sheets, or push to your database, BI tools and webhooks through the Apify API. Connect it to Make, n8n or Zapier to build automated film-data and sentiment pipelines.

⭐ Support & feedback

Found a bug or need an extra field? Open an issue on the Issues tab — response is usually fast. If this Actor saves you time, a ★★★★★ review on the Store page genuinely helps and is hugely appreciated. 🙏

⚖️ Legal

This Actor extracts only publicly available data and is intended for legitimate research, analytics and dataset-building use. You are responsible for complying with Letterboxd's terms of service, GDPR and any applicable local laws.

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📝 Changelog

2026-07-06

• ✨ README overhaul: richer output sample, ready-to-run example scenarios, cross-suite links, and clearer quick-start.

2026-07-01

• Maintenance pass: re-verified end-to-end on live data and confirmed successful runs within the 5-minute quality window on the default input.
• Sharpened Store metadata (SEO title & description) and expanded the FAQ with high-intent, long-tail questions for easier discovery in Google and Apify Store search.
• Added ready-to-run example tasks that cover common real-world use cases.

2026-06-15

• Reliability pass: re-verified end-to-end on live data with real-world inputs. Routine maintenance build.

2026-06-07

• Docs: added coverage for Letterboxd unofficial API / API alternative, exporting reviews and ratings to CSV/JSON, and scraping without API or login.

2026-06-05

• 🛡️ Reliability fix: results are no longer dropped by strict output validation — runs now complete cleanly even at high volume (thousands of results).
• ⚡ Stability & performance hardening; fresh rebuild.

2026-06-04

• Verified live & refreshed build — reliability/maintenance pass.