# Leetcode Problems Scraper (`real-estate-api/leetcode-problems-scraper`) Actor

LeetCode Problems Scraper is a Python tool designed to extract problem titles, difficulty levels, acceptance rates, tags, and problem descriptions from the LeetCode platform. It exports structured coding challenge datasets into clean CSV or JSON format

- **URL**: https://apify.com/real-estate-api/leetcode-problems-scraper.md
- **Developed by:** [real-estate-api](https://apify.com/real-estate-api) (community)
- **Security:** Limited permissions
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $2.99 / 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.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
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.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## 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.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

### LeetCode Problems Scraper — Coding Challenges, Difficulty, and Topic Tags

![LeetCode Problems Scraper cover](https://images.bigibyte.com/cdn-cgi/image/width=1260,height=540,fit=cover,format=webp/apify-actors-covers/leetcode-problems-scraper-cover.png)

Collect LeetCode problem information in a structured dataset. Retrieve problem titles, difficulty levels, acceptance rates, paid-only indicators, and topic tags to build searchable coding challenge catalogs and support interview preparation.

### Overview

LeetCode Problems Scraper helps developers and analysts organize coding problems by difficulty, topic, and acceptance rate. Configure the maximum number of records and optionally filter problems by difficulty to create a dataset suited to your needs.

### Features

- **Problem Collection:** Collect available coding problem records.
- **Difficulty Filtering:** Focus on Easy, Medium, or Hard problems.
- **Acceptance Rates:** Capture reported acceptance rates when available.
- **Topic Tags:** Organize problems by subjects such as Arrays, Hash Tables, Dynamic Programming, and Graphs.
- **Paid-Only Indicators:** Identify problems marked as requiring a paid subscription.
- **Structured Exports:** Export collected data in JSON, CSV, Excel, and other supported formats.
- **Configurable Collection:** Set a maximum item count and request timing options.

### Use Cases

- **Interview Preparation:** Build a spreadsheet of coding problems organized by difficulty.
- **Topic Coverage:** Find problems associated with specific data structures and algorithms.
- **Acceptance Rate Analysis:** Compare reported acceptance rates across problem categories.
- **Study Planning:** Create custom problem lists for learning and practice.
- **Problem Cataloging:** Maintain a structured collection for personal research and analysis.

### Input Parameters

| Parameter | Type | Description |
|---|---|---|
| `mode` | String | Collection mode. Use `problems`. |
| `difficulty` | String | Optional difficulty filter: `EASY`, `MEDIUM`, or `HARD`. |
| `maxItems` | Integer | Maximum number of problems to collect. |
| `requestDelaySecs` | Number | Delay between requests, in seconds. |
| `userAgent` | String | Optional client identification string. |
| `timeoutSecs` | Number | Maximum overall actor runtime, in seconds. |
| `requestTimeoutSecs` | Number | Maximum wait time for an individual request, in seconds. |

### Example Input

```json
{
  "mode": "problems",
  "difficulty": "EASY",
  "maxItems": 50,
  "requestDelaySecs": 2,
  "userAgent": "ActorResearch/1.0",
  "timeoutSecs": 300,
  "requestTimeoutSecs": 30
}
```

### What Data Does This Actor Extract?

The actor collects available problem information, including the following fields:

| Field | Description |
|---|---|
| `frontendQuestionId` | Problem's displayed question number. |
| `problemTitle` | Problem title. |
| `titleSlug` | URL-friendly problem identifier. |
| `difficulty` | Problem difficulty, such as Easy, Medium, or Hard. |
| `acRate` | Reported acceptance rate. |
| `paidOnly` | Indicates whether the problem is marked as paid-only. |
| `topicTags` | List of associated topics and tags. |
| `scrapedAt` | Timestamp when the record was collected. |

### Example Output

```json
{
  "frontendQuestionId": "1",
  "problemTitle": "Two Sum",
  "titleSlug": "two-sum",
  "difficulty": "Easy",
  "acRate": 58.002514942627904,
  "paidOnly": false,
  "topicTags": [
    {
      "name": "Array"
    },
    {
      "name": "Hash Table"
    }
  ],
  "scrapedAt": "2026-10-26T00:00:00+00:00"
}
```

This is an illustrative record. Actual values and available fields depend on the collected data.

### Working with Your Dataset

Once the actor finishes, open the dataset to review and export the collected problem records.

You can:

- Export the results as JSON, CSV, Excel, or another supported format.
- Sort problems by difficulty or acceptance rate.
- Filter records by topic tags.
- Create study lists for interview preparation.
- Analyze problem distributions with Python, Pandas, or spreadsheet tools.

### FAQ

**Who is this actor for?**

It is designed for developers, students, interview candidates, and analysts who want structured LeetCode problem information.

**Can I collect only Easy, Medium, or Hard problems?**

Yes. Set `difficulty` to `EASY`, `MEDIUM`, or `HARD` to filter the collection.

**What export formats are available?**

You can export the dataset in JSON, CSV, Excel, and other formats supported by Apify.

**Can I run the actor on a schedule?**

Yes. Configure an Apify schedule to refresh your dataset periodically.

**Can every problem be accessed without a subscription?**

Not necessarily. Some problems may be marked as paid-only, as indicated by the `paidOnly` field.

### Disclaimer

This actor is an independent tool and is not affiliated with, endorsed by, sponsored by, or officially associated with LeetCode or its owners. Users are responsible for complying with applicable terms of use and laws when collecting and using data.

### Is Web Scraping Legal?

Collecting information that is already public is generally legal. There is no single law that bans all forms of web scraping. The limits are the usual ones: do not break into accounts, do not take private or confidential data, and be careful with personal data and copyrighted works. A site's terms can also restrict how you access it. Facts such as a public name, price, or rating are not the same thing as republishing someone's article or photo.

This actor collects information from public pages. It does not log in and it does not bypass access controls.

For a longer explanation, see Apify's article [Is web scraping legal?](https://blog.apify.com/is-web-scraping-legal/).

# Actor input Schema

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

What to scrape.

## `difficulty` (type: `string`):

Optional filter: EASY, MEDIUM, or HARD. Leave empty for all.

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

Stop after this many successful rows. Hard max is 1000.

## `requestDelaySecs` (type: `number`):

Seconds to wait between requests.

## `userAgent` (type: `string`):

User-Agent header sent with each request.

## `timeoutSecs` (type: `integer`):

Overall run budget in seconds.

## `requestTimeoutSecs` (type: `integer`):

Per-request timeout in seconds.

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

Optional proxy. Leave off unless you need it.

## Actor input object example

```json
{
  "mode": "problems",
  "difficulty": "",
  "maxItems": 100,
  "requestDelaySecs": 2,
  "userAgent": "ActorResearch/1.0",
  "timeoutSecs": 300,
  "requestTimeoutSecs": 30,
  "proxyConfiguration": {
    "useApifyProxy": false
  }
}
```

# Actor output Schema

## `overview` (type: `string`):

Dataset rows in the overview table.

## `dataset` (type: `string`):

Full dataset for this run.

## `items` (type: `string`):

Dataset items as JSON.

# 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 = {};

// Run the Actor and wait for it to finish
const run = await client.actor("real-estate-api/leetcode-problems-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 = {}

# Run the Actor and wait for it to finish
run = client.actor("real-estate-api/leetcode-problems-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 '{}' |
apify call real-estate-api/leetcode-problems-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,real-estate-api/leetcode-problems-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/WX1YQKSkZ9fP7i4uU/builds/GNcgZSI112kJTIXKH/openapi.json