# Letterboxd Film Scraper (`masked_hacker/letterboxd-scraper`) Actor Scrape structured film data (rating, cast, crew, genres) from Letterboxd. - **URL**: https://apify.com/masked\_hacker/letterboxd-scraper.md - **Developed by:** [Masked Hacker](https://apify.com/masked_hacker) (community) - **Categories:** Videos - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $3.00 / 1,000 films This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Letterboxd Film Scraper: Ratings, Cast, Crew & Metadata Turn [Letterboxd](https://letterboxd.com) search terms or film URLs into a clean, structured dataset of film metadata: rating, cast, crew, genres, studios, runtime, and cross-references to TMDB and IMDB. Perfect for **film research, recommendation engines, content analytics, and ratings datasets** across any title on Letterboxd. ### What you get - 🔎 **Search by title**, or paste your own Letterboxd film URLs. - ⭐ **Rating data:** average rating, rating count and review count straight from the film page. - 🎬 **Full credits:** director(s) and cast. - 🏷️ **Genres, countries, studios and languages** for every film. - 🆔 **Cross-platform IDs:** TMDB and IMDB, ready to join with other datasets. - 📄 Clean JSON / CSV / Excel export, ready for a spreadsheet or a pipeline. ### Pricing **Pay per event:** **$0.006 per film returned**, plus a small per-run start fee. You only pay for films you actually receive; no monthly subscription. ### Input | Field | Type | Description | |---|---|---| | `searchTerms` | string[] | Film titles to search; each term's top results are scraped. | | `startUrls` | url[] | Direct Letterboxd film URLs, e.g. `https://letterboxd.com/film/parasite-2019/`. | | `maxItems` | int | Stop after this many films (default 100). | | `resultsPerSearch` | int | Films taken per search term (default 10). | | `proxyConfiguration` | proxy | Residential proxy recommended. Letterboxd blocks datacenter IPs. | Provide at least one of `searchTerms` or `startUrls`. #### Example input ```json { "searchTerms": ["parasite", "the substance"], "maxItems": 50 } ```` ### Output One record per film. | Field | Description | |---|---| | `title`, `year`, `releaseDate` | Film identity. | | `runtimeMinutes` | Runtime in minutes. | | `director`, `cast` | Credits. | | `genres`, `countries`, `studios`, `languages` | Classification and production metadata. | | `averageRating`, `ratingCount`, `reviewCount` | Letterboxd member rating data. | | `description`, `posterUrl` | Synopsis and poster image. | | `tmdbId`, `imdbId` | Cross-platform identifiers. | | `url`, `slug`, `scrapedAt` | Film URL, slug and scrape time. | #### Example output ```json { "title": "Parasite", "year": 2019, "runtimeMinutes": 132, "director": ["Bong Joon-ho"], "genres": ["Comedy", "Drama", "Thriller"], "averageRating": 4.57, "ratingCount": 1218453, "tmdbId": "496243", "imdbId": "tt6751668", "url": "https://letterboxd.com/film/parasite-2019/" } ``` ### FAQ **Do I need a Letterboxd account or API key?** No. Just provide a search term or a film URL. **Does this include user reviews?** No, only the aggregate rating and review count shown on the film page, not individual review text. **Is this legal?** The actor collects only publicly available film data. You are responsible for complying with Letterboxd's terms and applicable law in your use of the data. # Actor input Schema ## `searchTerms` (type: `array`): Film titles to search on Letterboxd. Each term's top results are scraped. ## `startUrls` (type: `array`): Direct Letterboxd film URLs to scrape, e.g. https://letterboxd.com/film/parasite-2019/. ## `maxItems` (type: `integer`): Stop after this many film records are scraped. ## `resultsPerSearch` (type: `integer`): How many films to take from each search term's results. ## `proxyConfiguration` (type: `object`): Residential proxy is recommended — Letterboxd blocks datacenter IPs. ## Actor input object example ```json { "searchTerms": [ "parasite", "the substance" ], "maxItems": 100, "resultsPerSearch": 10, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } } ``` # Actor output Schema ## `results` (type: `string`): All scraped film records in the default dataset. # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "searchTerms": [ "parasite", "dune" ] }; // Run the Actor and wait for it to finish const run = await client.actor("masked_hacker/letterboxd-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "searchTerms": [ "parasite", "dune", ] } # Run the Actor and wait for it to finish run = client.actor("masked_hacker/letterboxd-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "searchTerms": [ "parasite", "dune" ] }' | apify call masked_hacker/letterboxd-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=masked_hacker/letterboxd-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Letterboxd Film Scraper", "description": "Scrape structured film data (rating, cast, crew, genres) from Letterboxd.", "version": "0.0", "x-build-id": "YfoyZakmbBfz8F9dU" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/masked_hacker~letterboxd-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-masked_hacker-letterboxd-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/masked_hacker~letterboxd-scraper/runs": { "post": { "operationId": "runs-sync-masked_hacker-letterboxd-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/masked_hacker~letterboxd-scraper/run-sync": { "post": { "operationId": "run-sync-masked_hacker-letterboxd-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "searchTerms": { "title": "Search terms", "type": "array", "description": "Film titles to search on Letterboxd. Each term's top results are scraped.", "items": { "type": "string" } }, "startUrls": { "title": "Film URLs", "type": "array", "description": "Direct Letterboxd film URLs to scrape, e.g. https://letterboxd.com/film/parasite-2019/.", "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } }, "maxItems": { "title": "Max films", "minimum": 1, "type": "integer", "description": "Stop after this many film records are scraped.", "default": 100 }, "resultsPerSearch": { "title": "Results per search term", "minimum": 1, "type": "integer", "description": "How many films to take from each search term's results.", "default": 10 }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Residential proxy is recommended — Letterboxd blocks datacenter IPs.", "default": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```