# Letterboxd Scraper - Diary, Ratings & Reviews (`ninhothedev/letterboxd-scraper`) Actor

$0.5/1K 🔥 Fast Letterboxd scraper! A user's watched films, ratings & reviews from their diary. No key. JSON, CSV, Excel or API in seconds. Pull thousands of entries for film data & recommendation datasets ⚡

- **URL**: https://apify.com/ninhothedev/letterboxd-scraper.md
- **Developed by:** [ninhothedev](https://apify.com/ninhothedev) (community)
- **Categories:** Videos, Social media, Automation
- **Stats:** 3 total users, 2 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $0.50 / 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.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — 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 🎬

**Scrape any Letterboxd user's diary — watched films, star ratings, reviews, rewatch flags, watched dates, posters and TMDB IDs — straight from their public RSS feed. No API key, no login, no cookies.**

Fast, lightweight and datacenter-proxy friendly. Point it at one or more usernames and get clean, structured JSON of everything in their recent film diary.

***

### What it does

Give the actor a list of Letterboxd usernames (e.g. `dave`, `davidehrlich`) and it returns every recent diary entry as a structured record:

- 🎞️ **Film title & year**
- ⭐ **Star rating** (numeric, e.g. `4.5`)
- 📝 **Review text** (HTML stripped, capped at 5,000 chars)
- 🔁 **Rewatch flag** (true/false)
- 📅 **Watched date** and original **publish date** (ISO 8601)
- 🖼️ **Poster image URL**
- 🎬 **TMDB movie ID** — join directly to The Movie Database
- 🔗 **Direct Letterboxd URL** and stable GUID

Because it reads the official public RSS feed, it is quick, reliable and gentle on resources.

***

### Example output

```json
{
  "username": "dave",
  "film_title": "Obsession",
  "film_year": "2025",
  "rating": 3.5,
  "rewatch": false,
  "watched_date": "2026-07-09",
  "review": "A genuinely great thriller.",
  "poster": "https://a.ltrbxd.com/resized/film-poster/1/2/3/obsession.jpg",
  "tmdb_id": "1339713",
  "url": "https://letterboxd.com/dave/film/obsession-2025/",
  "published": "2026-07-09T16:55:16+00:00",
  "source": "letterboxd",
  "guid": "letterboxd-watch-1388436545",
  "scraped_at": "2026-07-20T00:00:00+00:00"
}
```

***

### Input

| Field | Type | Description |
|-------|------|-------------|
| `mode` | select | `diary` — fetch each username's recent diary/review entries. |
| `usernames` | array | Letterboxd usernames (part after `letterboxd.com/`). Full profile URLs are accepted too. Prefilled with `["dave", "davidehrlich"]`. |
| `maxItems` | integer | Max entries across all usernames (default `100`, max `1000`). RSS exposes ~50 most recent per user. |

```json
{
  "mode": "diary",
  "usernames": ["dave", "davidehrlich"],
  "maxItems": 100
}
```

***

### Use cases

- **Film data** — build a dataset of films, ratings and metadata linked to TMDB.
- **Critic & creator tracking** — monitor what film critics and creators are watching and rating over time.
- **Recommendation datasets** — feed ratings and rewatch signals into recommender models.
- **Taste analysis** — analyze a user's viewing habits, rating distribution and rewatch patterns.

***

### Pricing

Roughly **$0.5 per 1,000 entries** — one of the cheapest ways to pull structured film-diary data at scale. Pay-as-you-go on the Apify platform.

***

### Related actors

- [TVmaze TV Scraper](https://apify.com/ninhothedev/tvmaze-tv-scraper)
- [Anime Scraper](https://apify.com/ninhothedev/anime-scraper)
- [MusicBrainz Scraper](https://apify.com/ninhothedev/musicbrainz-scraper)
- [Reddit Scraper](https://apify.com/ninhothedev/reddit-scraper)

***

### Notes & FAQ

- **No API key required.** The actor reads each user's public RSS feed (`letterboxd.com/{username}/rss/`).
- **Private diaries** and unknown usernames are skipped with a warning.
- **How many entries per user?** Letterboxd RSS exposes roughly the 50 most recent diary items.
- **Ethics & compliance.** Only publicly available data is collected. Respect Letterboxd's terms and applicable laws.

**Keywords:** letterboxd scraper, film diary, movie ratings, film reviews, letterboxd api, tmdb, movie data, film critic tracking, watched films, cinema dataset, recommendation data, taste analysis.

# Actor input Schema

## `mode` (type: `string`):

What to scrape. 'diary' fetches each username's recent diary entries, ratings and reviews from their public Letterboxd RSS feed.

## `usernames` (type: `array`):

One or more Letterboxd usernames (the part after letterboxd.com/). Full profile URLs are also accepted and reduced to the username automatically.

## `maxItems` (type: `integer`):

Maximum number of diary entries to return across all usernames combined. RSS feeds expose roughly the 50 most recent entries per user.

## Actor input object example

```json
{
  "mode": "diary",
  "usernames": [
    "dave",
    "davidehrlich"
  ],
  "maxItems": 100
}
```

# 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 '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "usernames": [
        "dave",
        "davidehrlich"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("ninhothedev/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 '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = { "usernames": [
        "dave",
        "davidehrlich",
    ] }

# Run the Actor and wait for it to finish
run = client.actor("ninhothedev/letterboxd-scraper").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "usernames": [
    "dave",
    "davidehrlich"
  ]
}' |
apify call ninhothedev/letterboxd-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,ninhothedev/letterboxd-scraper"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/WSFiZ5WWL9Up6lHeg/builds/DD2fXT0uFNPdwu2ro/openapi.json