# Letterboxd Scraper — Films, Ratings & Reviews (`samwise.agency/letterboxd-scraper`) Actor Scrape Letterboxd: film details with average rating and rating counts, member reviews, and the popular films chart. No login. Clean JSON/CSV for film marketing, research, and sentiment analysis. - **URL**: https://apify.com/samwise.agency/letterboxd-scraper.md - **Developed by:** [samwise.agency](https://apify.com/samwise.agency) (community) - **Categories:** Videos - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $10.00 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows. 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 Scraper — Films, Ratings & Reviews Film-culture data from the platform tastemakers actually use. Scrape **Letterboxd film pages** (average rating, rating counts, directors, genres), **member reviews**, and the **popular films chart** — no login, clean rows. **What you can extract:** - 🎬 **Film details** — title, year, directors, genres, synopsis, poster - ⭐ **Ratings** — average rating and rating/review counts - ✍️ **Member reviews** — reviewer, star rating, full text - 📈 **Popular chart** — what film Twitter is actually watching ### Quick start 1. Click **Run** — default pulls Parasite (2019) with recent reviews 2. Swap in your films (slugs or URLs), or set popular pages for the chart 3. Schedule weekly = tracking sentiment through a release window ### Sample output ```json { "type": "film", "slug": "parasite-2019", "title": "Parasite", "year": 2019, "directors": ["Bong Joon-ho"], "genres": ["Thriller", "Comedy", "Drama"], "averageRating": 4.56, "ratingCount": 1204312, "url": "https://letterboxd.com/film/parasite-2019/" } ```` ### Use cases - **Film marketing & distribution** — cinephile sentiment before wide release - **Streaming acquisitions** — engagement data by title - **Journalists & academics** — reception analysis - **Recommendation engines** — ratings corpus for indie/arthouse titles ### Pricing Pay per result row (film or review). ### FAQ **Is there an official API?** Letterboxd's API is invite-only; this Actor reads the same public pages any visitor sees, politely. # Actor input Schema ## `films` (type: `array`): Film slugs (e.g. 'parasite-2019') or full letterboxd.com/film/... URLs. ## `includeReviews` (type: `boolean`): Also export recent member reviews for each film (one result per review). ## `maxReviewsPerFilm` (type: `integer`): Cap on review rows per film. ## `popularPages` (type: `integer`): Also scrape N pages of the popular-films chart (~72 films/page). 0 = skip. ## `proxyConfiguration` (type: `object`): Apify Proxy keeps requests reliable. ## Actor input object example ```json { "films": [ "parasite-2019" ], "includeReviews": true, "maxReviewsPerFilm": 20, "popularPages": 0, "proxyConfiguration": { "useApifyProxy": true } } ``` # 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 = { "films": [ "parasite-2019" ], "proxyConfiguration": { "useApifyProxy": true } }; // Run the Actor and wait for it to finish const run = await client.actor("samwise.agency/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 = { "films": ["parasite-2019"], "proxyConfiguration": { "useApifyProxy": True }, } # Run the Actor and wait for it to finish run = client.actor("samwise.agency/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 '{ "films": [ "parasite-2019" ], "proxyConfiguration": { "useApifyProxy": true } }' | apify call samwise.agency/letterboxd-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=samwise.agency/letterboxd-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Letterboxd Scraper — Films, Ratings & Reviews", "description": "Scrape Letterboxd: film details with average rating and rating counts, member reviews, and the popular films chart. No login. Clean JSON/CSV for film marketing, research, and sentiment analysis.", "version": "0.1", "x-build-id": "Zy2HaCa9KxijckRlU" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/samwise.agency~letterboxd-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-samwise.agency-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/samwise.agency~letterboxd-scraper/runs": { "post": { "operationId": "runs-sync-samwise.agency-letterboxd-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/samwise.agency~letterboxd-scraper/run-sync": { "post": { "operationId": "run-sync-samwise.agency-letterboxd-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "films": { "title": "Films", "type": "array", "description": "Film slugs (e.g. 'parasite-2019') or full letterboxd.com/film/... URLs.", "default": [ "parasite-2019" ], "items": { "type": "string" } }, "includeReviews": { "title": "Include reviews", "type": "boolean", "description": "Also export recent member reviews for each film (one result per review).", "default": true }, "maxReviewsPerFilm": { "title": "Max reviews per film", "minimum": 1, "maximum": 100, "type": "integer", "description": "Cap on review rows per film.", "default": 20 }, "popularPages": { "title": "Popular chart pages", "minimum": 0, "maximum": 20, "type": "integer", "description": "Also scrape N pages of the popular-films chart (~72 films/page). 0 = skip.", "default": 0 }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Apify Proxy keeps requests reliable.", "default": { "useApifyProxy": true } } } }, "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 } } } } } } } } } } ```