Is it legal to scrape IMDb?

Scraping public data from IMDb is generally permissible for personal and research purposes. However, you should review IMDb's Terms of Service and avoid scraping copyrighted images or using the data for commercial redistribution without a license.

Does IMDb provide an official API?

Yes, IMDb offers an official API through the AWS Data Exchange. It is a commercial-grade GraphQL API aimed at enterprises, providing high-fidelity datasets with official support, though it requires a subscription fee.

How can I bypass the 'Request Forbidden' error?

This error usually occurs because your scraper lacks proper headers. Ensure you are sending a modern User-Agent and use high-quality rotating proxies to prevent your IP address from being blacklisted by Amazon's WAF.

What is the best way to handle pagination for reviews?

IMDb reviews often use a 'Load More' button or specific URL parameters. You can either use a browser-based scraper like Automatio to click the button or identify the dynamic URL pattern (e.g., using pagination tokens) to fetch multiple pages of reviews via direct requests.

Can I scrape box office data for historical movies?

Absolutely. IMDb maintains comprehensive 'Box Office' sections for most titles, including opening weekend, gross revenue in various territories, and budget figures. You can extract these by targeting the 'Box office' section on individual movie pages.

What data format is recommended for export?

For IMDb data, JSON is highly recommended because of the nested nature of cast and crew lists. CSV is also a popular choice for simpler datasets like movie lists and average ratings, making it easy to open in Excel or Google Sheets.

How frequently does IMDb update its data?

Popularity metrics and box office figures are updated daily. User ratings and review counts update in near real-time as users submit feedback. For tracking trends, a daily or weekly scraping schedule is usually sufficient.

Do I need to log in to scrape IMDb?

No, almost all relevant movie and celebrity data on IMDb is publicly accessible without an account. It is recommended to scrape without logging in to avoid risking a personal account and to simplify the scraping architecture.

How to Scrape IMDb: The Complete Guide to Movie Data Extraction

Learn how to extract movie ratings, cast details, box office stats, and reviews from IMDb. Discover tools and techniques for entertainment market research.

Start Scraping Free

imdb.comHard

Coverage:Global

Available Data9 fields

TitlePriceLocationDescriptionImagesSeller InfoPosting DateCategoriesAttributes

All Extractable Fields

Movie TitleRelease YearIMDb User RatingMetascoreNumber of User ReviewsNumber of Critic ReviewsPopularity RankGenre CategoriesDirector NameLead Cast MembersCharacter NamesPlot SummaryProduction BudgetWorldwide Gross RevenueRuntimeContent Rating (MPAA)Production CompaniesFilming LocationsAwards and NominationsOfficial Trailer URL

Technical Requirements

JavaScript Required

No Login

Has Pagination

Official API Available

Anti-Bot Protection Detected

Amazon WAFIP BlockingBrowser FingerprintingUser-Agent Filtering

View API Documentation

About IMDb

Learn what IMDb offers and what valuable data can be extracted from it.

The World's Movie Database

IMDb (Internet Movie Database) is the premier global source for movie, television, and celebrity content. Owned by Amazon, it houses an unparalleled collection of structured data ranging from historical cinematic records to real-time box office performance and trending popularity metrics.

Data Depth and Structure

The platform offers a granular view of the entertainment industry, including technical specifications like aspect ratios, complex financial data such as worldwide gross revenue, and extensive credit lists for cast and crew. It also serves as a hub for audience sentiment through millions of user reviews and ratings.

Strategic Value for Scraping

For businesses and researchers, IMDb data is essential for competitive analysis, sentiment tracking, and the development of recommendation algorithms. Whether monitoring a film's reception or building a comprehensive media database, scraping IMDb provides the high-fidelity data needed for deep industry insights.

Why Scrape IMDb?

Discover the business value and use cases for extracting data from IMDb.

Conduct entertainment market research and trend analysis for film production.

Build movie recommendation engines using genres, cast, and plot data.

Monitor audience sentiment via automated scraping of user and critic reviews.

Aggregate box office and budget data for financial performance modeling.

Track celebrity popularity and career trajectories for talent management.

Create niche entertainment blogs or news sites with up-to-date metadata.

Scraping Challenges

Technical challenges you may encounter when scraping IMDb.

Aggressive IP blocking and rate limiting managed by Amazon's security infrastructure.

Dynamic class names that change frequently, requiring stable data-testid selectors.

Heavy reliance on JavaScript for rendering modern page elements and reviews.

Complex URL structures for pagination and filtered search results.

Strict User-Agent validation that blocks requests from standard library headers.

Scrape IMDb with AI

No coding required. Extract data in minutes with AI-powered automation.

How It Works

Describe What You Need

Tell the AI what data you want to extract from IMDb. Just type it in plain language — no coding or selectors needed.

AI Extracts the Data

Our artificial intelligence navigates IMDb, handles dynamic content, and extracts exactly what you asked for.

Get Your Data

Receive clean, structured data ready to export as CSV, JSON, or send directly to your apps and workflows.

Why Use AI for Scraping

No-code interface allows users to map complex movie pages without writing scripts.

Built-in proxy rotation and fingerprint management bypasses Amazon WAF.

Scheduled scraping features allow for automated tracking of daily box office changes.

Cloud execution ensures large-scale movie database extraction without local resource drain.

Seamless integration with Google Sheets and Webhooks for real-time data processing.

Start Scraping Free

No credit card requiredFree tier availableNo setup needed

No-Code Web Scrapers for IMDb

Point-and-click alternatives to AI-powered scraping

Several no-code tools like Browse.ai, Octoparse, Axiom, and ParseHub can help you scrape IMDb. These tools use visual interfaces to select elements, but they come with trade-offs compared to AI-powered solutions.

Typical Workflow with No-Code Tools

Install browser extension or sign up for the platform

Navigate to the target website and open the tool

Point-and-click to select data elements you want to extract

Configure CSS selectors for each data field

Set up pagination rules to scrape multiple pages

Handle CAPTCHAs (often requires manual solving)

Configure scheduling for automated runs

Export data to CSV, JSON, or connect via API

Common Challenges

Learning curve

Understanding selectors and extraction logic takes time

Selectors break

Website changes can break your entire workflow

Dynamic content issues

JavaScript-heavy sites often require complex workarounds

CAPTCHA limitations

Most tools require manual intervention for CAPTCHAs

IP blocking

Aggressive scraping can get your IP banned

Code Examples

import requests
from bs4 import BeautifulSoup

# IMDb blocks default requests; use a modern User-Agent
headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36'}
url = 'https://www.imdb.com/title/tt0111161/'

def scrape_imdb_basic(url):
    try:
        response = requests.get(url, headers=headers)
        response.raise_for_status()
        soup = BeautifulSoup(response.text, 'html.parser')
        
        # Use data-testid as it is more stable than dynamic classes
        title = soup.find('span', {'data-testid': 'hero__primary-text'}).text
        rating = soup.find('span', {'class': 'sc-bde20123-1'}).text # Note: check for selector updates
        
        print(f'Title: {title} | Rating: {rating}')
    except Exception as e:
        print(f'Scraping failed: {e}')

scrape_imdb_basic(url)

When to Use

Best for static HTML pages where content is loaded server-side. The fastest and simplest approach when JavaScript rendering isn't required.

Advantages

●Fastest execution (no browser overhead)
●Lowest resource consumption
●Easy to parallelize with asyncio
●Great for APIs and static pages

Limitations

●Cannot execute JavaScript
●Fails on SPAs and dynamic content
●May struggle with complex anti-bot systems

from playwright.sync_api import sync_playwright

def run():
    with sync_playwright() as p:
        browser = p.chromium.launch(headless=True)
        page = browser.new_page()
        
        # Navigate to a movie page
        page.goto('https://www.imdb.com/title/tt0111161/')
        
        # Wait for the specific data element to ensure JS is rendered
        page.wait_for_selector('[data-testid="hero__primary-text"]')
        
        # Extract data
        movie_title = page.locator('[data-testid="hero__primary-text"]').inner_text()
        rating_val = page.locator('[data-testid="hero-rating-bar__aggregate-rating__score"] > span').first.inner_text()
        
        print({'title': movie_title, 'rating': rating_val})
        
        browser.close()

run()

When to Use

Use when content loads dynamically via JavaScript, or when you need to interact with the page (clicks, scrolls, form fills). Handles modern anti-bot detection better.

Advantages

●Executes JavaScript like a real browser
●Handles SPAs and dynamic content
●Better anti-bot evasion with stealth plugins
●Can take screenshots and PDFs

Limitations

●Slower than HTTP requests
●Higher memory/CPU usage
●More complex to set up

import scrapy

class ImdbSpider(scrapy.Spider):
    name = 'imdb_spider'
    allowed_domains = ['imdb.com']
    start_urls = ['https://www.imdb.com/chart/top/']
    
    def parse(self, response):
        # Iterate through the list of top movies
        for movie in response.css('.ipc-metadata-list-summary-item'):
            yield {
                'title': movie.css('.ipc-title__text::text').get(),
                'rating': movie.css('.ipc-rating-star--rating::text').get(),
                'year': movie.css('.sc-b189961a-8::text').get(),
            }
            
        # Handle pagination if applicable
        next_page = response.css('a.next-page::attr(href)').get()
        if next_page:
            yield response.follow(next_page, self.parse)

When to Use

Ideal for large-scale crawling projects that need to scrape thousands of pages. Built-in support for rate limiting, retries, and data pipelines.

Advantages

●Built for scale (millions of pages)
●Automatic request throttling
●Built-in data export pipelines
●Middleware system for proxies/headers

Limitations

●Steeper learning curve
●Overkill for small projects
●No native JavaScript rendering

const puppeteer = require('puppeteer');

async function scrapeIMDb() {
  const browser = await puppeteer.launch({ headless: true });
  const page = await browser.newPage();
  
  // Mimic real browser headers
  await page.setUserAgent('Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36');
  
  await page.goto('https://www.imdb.com/title/tt0111161/', { waitUntil: 'domcontentloaded' });

  const movieInfo = await page.evaluate(() => {
    const title = document.querySelector('[data-testid="hero__primary-text"]')?.innerText;
    const rating = document.querySelector('[data-testid="hero-rating-bar__aggregate-rating__score"]')?.innerText;
    return { title, rating };
  });

  console.log(movieInfo);
  await browser.close();
}

scrapeIMDb();

When to Use

Choose this if you're in a Node.js/JavaScript ecosystem or need tight integration with frontend tools. Similar capabilities to Playwright.

Advantages

●Native JavaScript/TypeScript support
●Chrome DevTools Protocol access
●Large ecosystem and community
●Good for JS-heavy projects

Limitations

●Chrome-only (vs Playwright's multi-browser)
●Similar overhead to Playwright
●Less mature stealth options

How to Scrape IMDb with Code

Python + Requests

import requests
from bs4 import BeautifulSoup

# IMDb blocks default requests; use a modern User-Agent
headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36'}
url = 'https://www.imdb.com/title/tt0111161/'

def scrape_imdb_basic(url):
    try:
        response = requests.get(url, headers=headers)
        response.raise_for_status()
        soup = BeautifulSoup(response.text, 'html.parser')
        
        # Use data-testid as it is more stable than dynamic classes
        title = soup.find('span', {'data-testid': 'hero__primary-text'}).text
        rating = soup.find('span', {'class': 'sc-bde20123-1'}).text # Note: check for selector updates
        
        print(f'Title: {title} | Rating: {rating}')
    except Exception as e:
        print(f'Scraping failed: {e}')

scrape_imdb_basic(url)

Python + Playwright

from playwright.sync_api import sync_playwright

def run():
    with sync_playwright() as p:
        browser = p.chromium.launch(headless=True)
        page = browser.new_page()
        
        # Navigate to a movie page
        page.goto('https://www.imdb.com/title/tt0111161/')
        
        # Wait for the specific data element to ensure JS is rendered
        page.wait_for_selector('[data-testid="hero__primary-text"]')
        
        # Extract data
        movie_title = page.locator('[data-testid="hero__primary-text"]').inner_text()
        rating_val = page.locator('[data-testid="hero-rating-bar__aggregate-rating__score"] > span').first.inner_text()
        
        print({'title': movie_title, 'rating': rating_val})
        
        browser.close()

run()

Python + Scrapy

import scrapy

class ImdbSpider(scrapy.Spider):
    name = 'imdb_spider'
    allowed_domains = ['imdb.com']
    start_urls = ['https://www.imdb.com/chart/top/']
    
    def parse(self, response):
        # Iterate through the list of top movies
        for movie in response.css('.ipc-metadata-list-summary-item'):
            yield {
                'title': movie.css('.ipc-title__text::text').get(),
                'rating': movie.css('.ipc-rating-star--rating::text').get(),
                'year': movie.css('.sc-b189961a-8::text').get(),
            }
            
        # Handle pagination if applicable
        next_page = response.css('a.next-page::attr(href)').get()
        if next_page:
            yield response.follow(next_page, self.parse)

Node.js + Puppeteer

const puppeteer = require('puppeteer');

async function scrapeIMDb() {
  const browser = await puppeteer.launch({ headless: true });
  const page = await browser.newPage();
  
  // Mimic real browser headers
  await page.setUserAgent('Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36');
  
  await page.goto('https://www.imdb.com/title/tt0111161/', { waitUntil: 'domcontentloaded' });

  const movieInfo = await page.evaluate(() => {
    const title = document.querySelector('[data-testid="hero__primary-text"]')?.innerText;
    const rating = document.querySelector('[data-testid="hero-rating-bar__aggregate-rating__score"]')?.innerText;
    return { title, rating };
  });

  console.log(movieInfo);
  await browser.close();
}

scrapeIMDb();

What You Can Do With IMDb Data

Explore practical applications and insights from IMDb data.

Movie Recommendation Engine

Build personalized film suggestion systems using scraped genres, cast lists, and plot summaries.

How to implement:

1Scrape the IMDb Top 250 movies with genres and cast details.
2Apply NLP techniques to analyze plot summaries for thematic keywords.
3Map actors and directors to create a relational graph of cinematic connections.
4Export to a recommendation algorithm for real-time user matching.

Use Automatio to extract data from IMDb and build these applications without writing code.

More than just prompts

Supercharge your workflow with AI Automation

Automatio combines the power of AI agents, web automation, and smart integrations to help you accomplish more in less time.

AI Agents

Web Automation

Smart Workflows

Get Started Free

Pro Tips for Scraping IMDb

Expert advice for successfully extracting data from IMDb.

Use stable data-testid attributes for selectors instead of dynamic CSS classes like 'sc-xyz'.

Rotate high-quality residential proxies to bypass Amazon's sophisticated IP-based blocking.

Randomize your request delays (1-5 seconds) to mimic human behavior and avoid rate limits.

Set a valid 'Accept-Language' header to ensure you receive data in your preferred language.

Clean box office strings by stripping currency symbols ($) and commas (,) before database entry.

Scrape the 'Full Cast & Crew' subpages separately to avoid overloading a single title request.

Testimonials

What Our Users Say

Join thousands of satisfied users who have transformed their workflow

Jonathan Kogan

Co-Founder/CEO, rpatools.io

Automatio is one of the most used for RPA Tools both internally and externally. It saves us countless hours of work and we realized this could do the same for other startups and so we choose Automatio for most of our automation needs.

Mohammed Ibrahim

CEO, qannas.pro

I have used many tools over the past 5 years, Automatio is the Jack of All trades.. !! it could be your scraping bot in the morning and then it becomes your VA by the noon and in the evening it does your automations.. its amazing!

Ben Bressington

CTO, AiChatSolutions

Automatio is fantastic and simple to use to extract data from any website. This allowed me to replace a developer and do tasks myself as they only take a few minutes to setup and forget about it. Automatio is a game changer!

Sarah Chen

Head of Growth, ScaleUp Labs

We've tried dozens of automation tools, but Automatio stands out for its flexibility and ease of use. Our team productivity increased by 40% within the first month of adoption.

David Park

Founder, DataDriven.io

The AI-powered features in Automatio are incredible. It understands context and adapts to changes in websites automatically. No more broken scrapers!

Emily Rodriguez

Marketing Director, GrowthMetrics

Automatio transformed our lead generation process. What used to take our team days now happens automatically in minutes. The ROI is incredible.

Jonathan Kogan

Co-Founder/CEO, rpatools.io

Mohammed Ibrahim

CEO, qannas.pro

Ben Bressington

CTO, AiChatSolutions

Sarah Chen

Head of Growth, ScaleUp Labs

We've tried dozens of automation tools, but Automatio stands out for its flexibility and ease of use. Our team productivity increased by 40% within the first month of adoption.

David Park

Founder, DataDriven.io

The AI-powered features in Automatio are incredible. It understands context and adapts to changes in websites automatically. No more broken scrapers!

Emily Rodriguez

Marketing Director, GrowthMetrics

Automatio transformed our lead generation process. What used to take our team days now happens automatically in minutes. The ROI is incredible.

Related Web Scraping

Frequently Asked Questions About IMDb

Find answers to common questions about IMDb

How to Scrape IMDb: The Complete Guide to Movie Data Extraction

About IMDb

The World's Movie Database

Data Depth and Structure

Strategic Value for Scraping

Why Scrape IMDb?

Scraping Challenges

Scrape IMDb with AI

How It Works

Why Use AI for Scraping

No-Code Web Scrapers for IMDb

Typical Workflow with No-Code Tools

Common Challenges

Code Examples

What You Can Do With IMDb Data

Movie Recommendation Engine

Sentiment Analysis Dashboard

Box Office Prediction Tool

Talent Scouting & Casting

Supercharge your workflow with AI Automation

Pro Tips for Scraping IMDb

What Our Users Say

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Frequently Asked Questions About IMDb

Is it legal to scrape IMDb?

Does IMDb provide an official API?

How can I bypass the 'Request Forbidden' error?

What is the best way to handle pagination for reviews?

Can I scrape box office data for historical movies?

What data format is recommended for export?

How frequently does IMDb update its data?

Do I need to log in to scrape IMDb?

How to Scrape IMDb: The Complete Guide to Movie Data Extraction

About IMDb

The World's Movie Database

Data Depth and Structure

Strategic Value for Scraping

Why Scrape IMDb?

Scraping Challenges

Scrape IMDb with AI

How It Works

Why Use AI for Scraping

How to scrape with AI:

Why use AI for scraping:

No-Code Web Scrapers for IMDb

Typical Workflow with No-Code Tools

Common Challenges

No-Code Web Scrapers for IMDb

Typical Workflow with No-Code Tools

Common Challenges

Code Examples

How to Scrape IMDb with Code

Python + Requests

Python + Playwright

Python + Scrapy

Node.js + Puppeteer

What You Can Do With IMDb Data

Movie Recommendation Engine

Sentiment Analysis Dashboard

Box Office Prediction Tool

Talent Scouting & Casting

What You Can Do With IMDb Data

Supercharge your workflow with AI Automation

Pro Tips for Scraping IMDb

What Our Users Say

Related Web Scraping

How to Scrape Biluppgifter.se: Vehicle Data Extraction Guide

How to Scrape The AA (theaa.com): A Technical Guide for Car & Insurance Data

How to Scrape CSS Author: A Comprehensive Web Scraping Guide

How to Scrape Bilregistret.ai: Swedish Vehicle Data Extraction Guide

How to Scrape Car.info | Vehicle Data & Valuation Extraction Guide

How to Scrape GoAbroad Study Abroad Programs

How to Scrape ResearchGate: Publication and Researcher Data

How to Scrape Statista: The Ultimate Guide to Market Data Extraction

Frequently Asked Questions About IMDb

Is it legal to scrape IMDb?

Does IMDb provide an official API?

How can I bypass the 'Request Forbidden' error?

What is the best way to handle pagination for reviews?

Can I scrape box office data for historical movies?

What data format is recommended for export?

How frequently does IMDb update its data?

Do I need to log in to scrape IMDb?