Letterboxd Scraper

Extract rich film data from Letterboxd — average rating, number of ratings, watches, likes, fans, genres, directors, cast, runtime, synopsis, countries, languages, studios, themes, streaming availability and poster — into clean JSON, CSV or Excel. Point it at individual films, a user's watchlist or diary, any list, a director or actor, a genre, a browse page, or just a search term. Fast and reliable.

Great for film datasets, recommendation engines, ranking & analytics, watchlist backups and research.

What you can scrape

• 🎬 Single films — letterboxd.com/film/parasite-2019/ (or a bare slug like parasite-2019)
• 👤 A user's watchlist or diary — letterboxd.com/USERNAME/watchlist/, .../films/diary/
• 📋 Any list — letterboxd.com/USERNAME/list/LIST-NAME/
• 🎬 A director or actor's films — letterboxd.com/director/greta-gerwig/, /actor/NAME/
• 🎭 A genre — letterboxd.com/films/genre/horror/, or the Genre field
• 🔥 Browse pages — popular this week, popular all-time, highest rated, newest
• 🔎 Search — pass a title or keyword and get matching films

Give it any mix as Start URLs (plus optional search/genre) and it pulls full metadata for every film.

Example input

{
  "startUrls": [
    "https://letterboxd.com/films/popular/this/week/",
    "https://letterboxd.com/schaffrillas/watchlist/",
    "https://letterboxd.com/film/parasite-2019/"
  ],
  "search": "blade runner",
  "sort": "FilmPopularityThisWeek",
  "maxFilms": 200,
  "includeStatistics": true
}

What you get

One row per film:

{
  "title": "Parasite",
  "year": 2019,
  "rating": 4.52,
  "ratingCount": 5664177,
  "watches": 8600000,
  "likes": 4200000,
  "fans": 210000,
  "reviewCount": 480000,
  "director": ["Bong Joon Ho"],
  "genres": ["Thriller", "Comedy", "Drama"],
  "runtimeMinutes": 133,
  "cast": ["Song Kang-ho", "Lee Sun-kyun", "Cho Yeo-jeong", "…"],
  "countries": ["South Korea"],
  "languages": ["Korean", "English"],
  "studios": ["Barunson E&A"],
  "themes": ["…"],
  "synopsis": "…",
  "poster": "https://a.ltrbxd.com/resized/film-poster/…jpg",
  "imdbUrl": "https://www.imdb.com/title/tt6751668/",
  "tmdbUrl": "https://www.themoviedb.org/movie/496243",
  "url": "https://letterboxd.com/film/parasite-2019/"
}

Output schema

Input fields

Use cases

• Build a film dataset — ratings, rating counts, genres and crew for thousands of titles
• Recommendation & ranking — use real Letterboxd ratings, rating counts and popularity as signal
• Watchlist backup — export a user's watchlist to a spreadsheet
• Popularity & rating analysis — scrape a browse page and crunch the numbers
• Enrich your movie app — pull posters, runtime, cast, synopsis and IMDb/TMDB links by title or URL

FAQ

How many films can I scrape? Set maxFilms up to 100,000. Browse pages and watchlists are paged through automatically.

Can I scrape a specific user's watchlist? Yes — use their watchlist/ URL as a start URL.

How does billing work? Pay per film scraped — you only pay for the films you actually get.

Do I need any setup? No — just add your inputs and run. Fast and reliable.

Is this only public data? Yes. It returns only publicly available Letterboxd data.

Can I connect this to other tools? The Letterboxd Scraper can be connected with almost any cloud service or web app thanks to integrations on the Apify platform. It works with Make, Zapier, Slack, Airbyte, GitHub, Google Drive and many more, plus the Apify API, JavaScript/Python clients and MCP. Or use webhooks to trigger an action whenever a run finishes.

Notes

Original clean-room implementation. Returns only public Letterboxd data; you are responsible for how you use the data and for complying with Letterboxd's terms. This is an independent tool and is not affiliated with or endorsed by Letterboxd.

You pay only for what the run returns. Set a maximum cost per run and it stops cleanly at that limit.