# Papers with Code Scraper (`crawlerbros/papers-with-code-scraper`) Actor

Scrape Papers with Code like search ML papers, fetch paper details with repos and results, browse ML tasks and leaderboards, search datasets, and find ML methods.

- **URL**: https://apify.com/crawlerbros/papers-with-code-scraper.md
- **Developed by:** [Crawler Bros](https://apify.com/crawlerbros) (community)
- **Categories:** AI, Developer tools, Automation
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $3.00 / 1,000 results

This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage.
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?

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

## Papers with Code Scraper

Scrape [Papers with Code](https://paperswithcode.com) — the free, open resource for machine learning papers with code implementations. Search papers, browse ML tasks and leaderboards, find datasets, and look up ML methods. Uses the official Papers with Code public REST API — no authentication required.

### What you can scrape

- **Papers**: title, abstract, authors, arXiv ID, published date, PDF/abstract URLs, top GitHub repo stars, linked tasks and datasets
- **Tasks**: ML benchmark tasks with category hierarchy, paper/dataset/method counts
- **Leaderboard results**: ranked model performance on benchmark datasets
- **Datasets**: ML datasets with modality info and paper count
- **Methods**: ML methods/architectures with paper links and collections

### Supported modes

| Mode | Description |
|---|---|
| `searchPapers` | Search ML papers by keyword (e.g. "large language models", "BERT") |
| `byPaperId` | Fetch a single paper with full details, repositories, and results |
| `searchTasks` | Search ML tasks/benchmarks (e.g. "image classification") |
| `leaderboard` | Ranked results for a specific task (e.g. "image-classification") |
| `searchDatasets` | Search ML datasets (e.g. "ImageNet", "CIFAR") |
| `searchMethods` | Search ML methods/architectures (e.g. "transformer", "attention") |

### Input

| Field | Type | Description |
|---|---|---|
| `mode` | select | Scrape mode (default: `searchPapers`) |
| `query` | string | Search keyword (modes: searchPapers, searchTasks, searchDatasets, searchMethods) |
| `paperId` | string | Paper slug ID (mode: byPaperId, e.g. `attention-is-all-you-need`) |
| `taskId` | string | Task slug ID (mode: leaderboard, e.g. `image-classification`) |
| `ordering` | select | Sort order for paper search (newest published, most stars, alphabetical, etc.) |
| `minStars` | integer | Only include papers with at least N GitHub stars on top repo |
| `hasCode` | boolean | Only include papers that have a linked GitHub repository |
| `maxItems` | integer | Maximum records to emit (default: 50) |

#### Example input

```json
{
  "mode": "searchPapers",
  "query": "large language models",
  "ordering": "-github_stars",
  "minStars": 1000,
  "maxItems": 20
}
```

### Output

Each record includes `recordType`, `siteName` = `"Papers with Code"`, and `scrapedAt` timestamp.

#### Paper record

```json
{
  "paperId": "attention-is-all-you-need",
  "arxivId": "1706.03762",
  "title": "Attention Is All You Need",
  "abstract": "We propose a new simple network architecture...",
  "authors": ["Ashish Vaswani", "Noam Shazeer", "Niki Parmar"],
  "primaryAuthor": "Ashish Vaswani",
  "publishedDate": "2017-06-12",
  "urlAbstract": "https://arxiv.org/abs/1706.03762",
  "urlPdf": "https://arxiv.org/pdf/1706.03762.pdf",
  "paperswithcodeUrl": "https://paperswithcode.com/paper/attention-is-all-you-need",
  "stars": 14500,
  "repoUrl": "https://github.com/tensorflow/tensor2tensor",
  "taskNames": ["Machine Translation", "Language Modelling"],
  "datasetNames": ["WMT 2014 English-German"],
  "numResults": 12,
  "hasCode": true,
  "recordType": "paper",
  "siteName": "Papers with Code",
  "scrapedAt": "2026-05-10T12:00:00+00:00"
}
```

#### Leaderboard result record

```json
{
  "taskName": "Image Classification",
  "datasetName": "ImageNet",
  "metricName": "Top-1 Accuracy",
  "rank": 1,
  "paperTitle": "Deep Residual Learning for Image Recognition",
  "modelName": "ResNet-152",
  "score": 78.57,
  "repoUrl": "https://github.com/KaimingHe/deep-residual-networks",
  "paperUrl": "https://paperswithcode.com/paper/resnet",
  "recordType": "leaderboardResult",
  "siteName": "Papers with Code",
  "scrapedAt": "2026-05-10T12:00:00+00:00"
}
```

#### Task record

```json
{
  "taskId": "image-classification",
  "name": "Image Classification",
  "description": "Assigning an input image one or more labels from a fixed set.",
  "categories": ["Computer Vision"],
  "numPapers": 3500,
  "numDatasets": 280,
  "numMethods": 1200,
  "taskUrl": "https://paperswithcode.com/task/image-classification",
  "recordType": "task",
  "siteName": "Papers with Code",
  "scrapedAt": "2026-05-10T12:00:00+00:00"
}
```

### FAQs

**Does this require an API key or login?**
No. The Papers with Code public API is completely free and requires no authentication.

**How do I find a paper's ID for `byPaperId` mode?**
The paper ID is the slug in the URL on paperswithcode.com. For example, `https://paperswithcode.com/paper/attention-is-all-you-need` → ID is `attention-is-all-you-need`.

**How do I find a task ID for the leaderboard mode?**
Look at the URL on the Papers with Code task page. For example, `https://paperswithcode.com/task/image-classification` → task ID is `image-classification`.

**What does `ordering` do for paper search?**
Controls sort order: newest published first, most GitHub stars first, alphabetical by title, or their reverses.

**Can I filter to only papers with code?**
Yes — set `hasCode: true`. This filters to papers that have at least one linked GitHub repository.

**What is the `stars` field?**
The number of GitHub stars on the paper's top-ranked repository. Only present when the paper has linked code.

# Actor input Schema

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

What to scrape.

## `query` (type: `string`):

Keyword query (modes: searchPapers, searchTasks, searchDatasets, searchMethods).

## `paperId` (type: `string`):

arXiv paper ID (e.g. `1706.03762`) or HuggingFace paper slug. The HuggingFace API is used for paper lookup.

## `taskId` (type: `string`):

Papers with Code task slug, e.g. `image-classification`, `language-modelling`.

## `ordering` (type: `string`):

Sort order for paper search results.

## `minStars` (type: `integer`):

Only include papers whose top repo has at least this many stars.

## `hasCode` (type: `boolean`):

Only include papers that have a linked GitHub repository.

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

Hard cap on emitted records.

## Actor input object example

```json
{
  "mode": "searchPapers",
  "query": "large language models",
  "ordering": "-published",
  "minStars": 0,
  "hasCode": false,
  "maxItems": 5
}
```

# Actor output Schema

## `records` (type: `string`):

Dataset containing all scraped records (papers, tasks, leaderboard results, datasets, methods).

# 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 = {
    "mode": "searchPapers",
    "query": "large language models",
    "ordering": "-published",
    "minStars": 0,
    "hasCode": false,
    "maxItems": 5
};

// Run the Actor and wait for it to finish
const run = await client.actor("crawlerbros/papers-with-code-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 = {
    "mode": "searchPapers",
    "query": "large language models",
    "ordering": "-published",
    "minStars": 0,
    "hasCode": False,
    "maxItems": 5,
}

# Run the Actor and wait for it to finish
run = client.actor("crawlerbros/papers-with-code-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 '{
  "mode": "searchPapers",
  "query": "large language models",
  "ordering": "-published",
  "minStars": 0,
  "hasCode": false,
  "maxItems": 5
}' |
apify call crawlerbros/papers-with-code-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,crawlerbros/papers-with-code-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/ocG3r6VG7dkZBxfEc/builds/5V3chcNw25Ig1b3Y5/openapi.json