# IMDb Scraper — Titles, Cast, Ratings, Reviews & Search (`khadinakbar/imdb-scraper`) Actor

Extract IMDb titles, people, ratings, reviews, and search or discovery records from IDs, public URLs, keywords, and catalog filters. Returns canonical identities, credits, genres, plots, and images for research and AI workflows.

- **URL**: https://apify.com/khadinakbar/imdb-scraper.md
- **Developed by:** [Khadin Akbar](https://apify.com/khadinakbar) (community)
- **Categories:** Videos, MCP servers, Developer tools
- **Stats:** 1 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $5.00 / 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.

- **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

## IMDb Scraper for Titles, People, Reviews, Search, and Discovery

Turn IMDb title IDs, person IDs, public URLs, keywords, or discovery filters into structured dataset records for entertainment research, catalog enrichment, audience analysis, and AI workflows. Each result represents a title, person, or review and can include canonical IMDb identity, ratings, votes, cast, genres, plot, review text, and image URLs.

### Best fit for this Actor

Choose this Actor when a workflow begins with IMDb identifiers, public IMDb URLs, title or person names, or a discovery brief based on genre, title type, release year, and rating. Its automatic mode is designed for mixed input lists and routes each value to the matching title, person, or search workflow.

### Focused standalone workflow

This Actor is designed as a focused standalone workflow for IMDb title, person, search, discovery, and review data through one consistent input and dataset contract.

### A practical catalog scenario

A streaming catalog analyst starts with a mixed list of IMDb title IDs and film names. The Actor resolves the inputs, returns canonical IDs, titles, release years, ratings, genres, directors, stars, plots, and image URLs, and the analyst then joins those records to an internal catalog for enrichment and editorial review.

### Quick start input

Use `auto` for a mixed list of IDs, URLs, names, and keywords.

```json
{
  "queries": ["tt1375666", "nm0000138", "Breaking Bad"],
  "mode": "auto",
  "maxResults": 10
}
```

Discovery mode uses the filter fields as a research brief:

```json
{
  "mode": "discover",
  "genres": ["Sci-Fi"],
  "titleType": "movie",
  "minRating": 7,
  "sortBy": "RATING",
  "maxResults": 10
}
```

### What data you receive

| Field | Meaning |
| --- | --- |
| `type` | Dataset entity: `title`, `person`, or `review` |
| `id`, `url` | IMDb identifier and canonical public URL |
| `title`, `name`, `year`, `titleType` | Title or person identity fields |
| `rating`, `votes`, `certificate`, `runtimeMinutes` | Public title metrics and classification |
| `plot`, `genres`, `directors`, `stars` | Editorial and credit fields |
| `authorRating`, `reviewText` | Public review fields on review records |
| `imageUrl` | Primary public poster or profile image |

```json
{
  "type": "title",
  "id": "tt1375666",
  "url": "https://www.imdb.com/title/tt1375666/",
  "title": "Inception",
  "year": 2010,
  "titleType": "Movie",
  "rating": 8.8,
  "genres": ["Action", "Sci-Fi"],
  "directors": ["Christopher Nolan"],
  "imageUrl": "https://m.media-amazon.com/images/example.jpg"
}
```

### Run through the Apify API

```bash
curl -X POST "https://api.apify.com/v2/acts/khadinakbar~imdb-scraper/runs" \
  -H "Authorization: Bearer $APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"queries":["tt1375666","Christopher Nolan"],"mode":"auto","maxResults":10}'
```

Read the run's default dataset for the structured records.

### Use with AI agents through Apify MCP

Example prompt:

> Research these IMDb titles and people. Return canonical IDs, URLs, title or person names, ratings, genres, directors, stars, and image URLs. Read the dataset after completion and preserve IMDb URLs as source provenance.

Give the agent the mode, query list, and result scope. Ask it to read the dataset, group records by `type`, retain canonical URLs, and summarize the outcome with the collection cost shown by Apify.

### Pricing

This Actor uses Pay per event plus Apify platform usage. Title, person, search, discovery, and review records use the events configured for the Actor. Open the live Pricing tab for current billing details and use `maxResults` or Apify run cost controls to keep the scope aligned with your budget.

### Best results

Provide canonical IMDb IDs when identity precision matters, use `auto` for mixed research lists, and choose `discover` for a focused genre or title-type brief. Keep `maxResults` aligned with the decision you plan to make from the dataset.

### Builder's note

I designed automatic input routing because IMDb research often begins with a mixed collection of title IDs, person IDs, URLs, and names. A single normalized contract makes that mixed research easier to call from an agent and easier to join downstream.

### Responsible use

Collect public data you are authorized to access and follow applicable laws, IMDb terms, and your organization's data-use requirements.

# Actor input Schema

## `queries` (type: `array`):

What to scrape. Accepts IMDb title IDs (e.g. 'tt1375666'), person IDs ('nm0000138'), full IMDb URLs (e.g. 'https://www.imdb.com/title/tt0903747/'), or free-text keywords ('Breaking Bad', 'Christopher Nolan'). In auto mode each item is routed automatically: tt->title, nm->person, anything else->search. NOT a CSV file path — paste the values directly.

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

How to interpret 'queries'. Use 'auto' (default) to detect title/person/search per item. Use 'reviews' to pull user reviews for each given title (ID, URL, or keyword resolved to its top title). Use 'discover' to ignore queries and search by the filters below (genres, titleType, year range, minRating).

## `maxResults` (type: `integer`):

Upper bound on rows returned per search, discover, or reviews query (title and person lookups always return one record). Defaults to 50. Range 1-5000. Caps cost: each row is a billable result or review event.

## `genres` (type: `array`):

Discover-mode genre filter. Titles must match ALL listed genres. Use IMDb genre names like 'Action', 'Drama', 'Sci-Fi', 'Comedy', 'Horror'. Ignored unless mode is 'discover' (or filters are set with no queries).

## `titleType` (type: `string`):

Discover-mode type filter. Restricts results to one kind of title. Choose from the list (e.g. movie, tvSeries). Leave blank to include all types.

## `releaseYearMin` (type: `integer`):

Discover-mode lower bound on release year (inclusive), e.g. 2000. Leave blank for no lower bound. Ignored outside discover mode. Pair with 'releaseYearMax' for a window.

## `releaseYearMax` (type: `integer`):

Discover-mode upper bound on release year (inclusive), e.g. 2024. Leave blank for no upper bound. Ignored outside discover mode. Pair with 'releaseYearMin' for a window.

## `minRating` (type: `integer`):

Discover-mode minimum aggregate IMDb rating, 1-10 (e.g. 7 keeps titles rated 7.0+). Leave blank for no rating floor. Ignored outside discover mode. Combine with genres and year range for curated lists.

## `sortBy` (type: `string`):

Discover-mode result ordering. POPULARITY = current IMDb popularity (default), RATING = highest user rating first, VOTES = most-voted first, NEWEST = most recent release first. Ignored outside discover mode.

## `proxyConfiguration` (type: `object`):

Proxy settings. Defaults to Apify datacenter proxy, which is sufficient for IMDb's public API. Leave as default unless you have a reason to change it.

## Actor input object example

```json
{
  "queries": [
    "tt1375666",
    "nm0000138",
    "Breaking Bad"
  ],
  "mode": "auto",
  "maxResults": 50,
  "genres": [
    "Sci-Fi"
  ],
  "titleType": "",
  "sortBy": "POPULARITY",
  "proxyConfiguration": {
    "useApifyProxy": true
  }
}
```

# Actor output Schema

## `results` (type: `string`):

All scraped IMDb records (titles, people, reviews, search/discover rows).

## `resultsCsv` (type: `string`):

Same dataset as CSV.

# 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 = {
    "queries": [
        "tt1375666",
        "nm0000138",
        "Breaking Bad"
    ],
    "genres": [
        "Sci-Fi"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("khadinakbar/imdb-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 = {
    "queries": [
        "tt1375666",
        "nm0000138",
        "Breaking Bad",
    ],
    "genres": ["Sci-Fi"],
}

# Run the Actor and wait for it to finish
run = client.actor("khadinakbar/imdb-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 '{
  "queries": [
    "tt1375666",
    "nm0000138",
    "Breaking Bad"
  ],
  "genres": [
    "Sci-Fi"
  ]
}' |
apify call khadinakbar/imdb-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://mcp.apify.com/?tools=khadinakbar/imdb-scraper",
                "--header",
                "Authorization: Bearer <YOUR_API_TOKEN>"
            ]
        }
    }
}

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/acts/QqnIAbwVwdUU4moIs/builds/q2YQaqw8TzuXzc5af/openapi.json