🎞️ Letterboxd Film Database Scraper

🚀 Pull Letterboxd films with cast, directors, writers, themes, languages, studios, rating, watch counts, TMDB and IMDB IDs. 38 fields per film.

The Letterboxd Film Database Scraper pulls film records from Letterboxd, the social network for cinephiles. Output includes poster URL, slug, title + original title, year, runtime, tagline, description, directors + writers + editors + producers + cinematography + composers, full cast (names) + cast with character roles, genres + themes, primary language + spoken languages, production companies + studios, countries, alternative titles, average rating, rating count, watched-by count, liked-by count, appears-in-lists count, trailer URL, TMDB + IMDB IDs, and the Letterboxd URL.

Direct lookup only - feed a list of slugs (e.g. the-godfather, pulp-fiction), get rich records back. Each record is parsed from the film's HTML page. The scraper uses Chrome TLS fingerprinting via impit to bypass Cloudflare without needing a residential proxy, so 100 lookups complete in under a minute.

🎯 Target Audience	💡 Primary Use Cases
Film recommender builders, cinephiles, ML researchers, journalists, content marketers, fan-app developers	Film-recommender training, cinephile content, retrospective writing, watch-history analytics, fan apps

📋 What the Letterboxd Film Database Scraper does

Five filtering workflows in a single run:

• 🆔 Direct slug lookup. One slug per line, full film record per slug.
• 🎭 Full cast + crew. Cast (with character roles), directors, writers, editors, producers, composers.
• 🏷️ Genres + themes. Both Letterboxd's genres tab and themes tab.
• 📊 Member stats. Watched-by, liked-by, and appears-in-lists counts.
• ⚡ Fast no-proxy bypass. Chrome TLS fingerprint via impit. 100 films in under a minute.

💡 Why it matters: clean, server-side filtering and fresh data on every run.

📊 Data fields

Each record includes: averageRating, directorsText, genresText, imdbId, letterboxdUrl, posterUrl, runtimeMinutes, title, tmdbId, watchCount, year. These field names come straight from the actor's dataset schema, so what you see here is what lands in your dataset.

🚀 How to use

1. 📝 Sign up. Create a free account with $5 credit (takes 2 minutes).
2. 🌐 Open the Actor. Find the Letterboxd Film Database Scraper on the Apify Store.
3. 🎯 Set input. Pick filters and maxItems.
4. 🚀 Run it. Click Start.
5. 📥 Download. Grab results in the Dataset tab as CSV, Excel, JSON, or XML.

⏱️ Total time from signup to dataset: 3-5 minutes. No coding required.

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💡 Pro Tip: browse the complete ParseForge collection for more reference-data scrapers.

⚠️ Disclaimer: this Actor is an independent tool and is not affiliated with, endorsed by, or sponsored by Letterboxd, its maintainers, the Letterboxd community, or any film studio, distributor, director, or actor. All trademarks mentioned are the property of their respective owners. Only publicly available open data is collected.

🆘 Need Help?

If you hit a bug, have questions about setup, or need a scraper we haven't built yet, open our contact form or write to parseforge@protonmail.com. We also take on paid custom data projects.

For faster answers, join our Discord. It's the best place to get support and suggest new actors.