# OpenAlex Academic Research Scraper - Scholarly Papers (`cloud9_ai/openalex-scraper`) Actor

Search and extract academic papers, authors, institutions, and research topics from OpenAlex. Free open API covering 250M+ scholarly works. Get citations, abstracts, open access URLs.

- **URL**: https://apify.com/cloud9_ai/openalex-scraper.md
- **Developed by:** [cloud9](https://apify.com/cloud9_ai) (community)
- **Categories:** AI, Automation
- **Features:** Limited permissions
- **Stats:** 4 total users, 0 monthly users, 100.0% runs succeeded, 1 bookmarks
- **User rating**: No ratings yet

## Pricing

from $2.00 / 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/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

## OpenAlex Academic Research Scraper - Scholarly Papers

Search and extract academic papers, authors, institutions, and research topics from OpenAlex. Free open API covering 250M+ scholarly works. Get citations, abstracts, open access URLs.

### Use cases

- Map a research field's citation network and open-access share
- Evaluate an author's or institution's output
- Build a bibliometrics dashboard
- Find open-access full-text links for a reading list
- Feed a research RAG pipeline with abstracts

### Input

| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
| `mode` | string | Yes | `"searchWorks"` | What to search for Allowed: `searchWorks`, `searchAuthors`, `searchInstitutions`, `searchTopics`. |
| `searchQuery` | string | Yes | `"machine learning"` | Search term |
| `filterYear` | integer | No | — | Filter by publication year (optional) |
| `filterOpenAccess` | boolean | No | `false` | Only return open access papers |
| `sortBy` | string | No | `"relevance"` | Sort order for results Allowed: `relevance`, `cited_by_count`, `publication_date`. |
| `maxResults` | integer | No | `50` | Maximum number of results |
| `email` | string | No | — | Your email for polite pool (faster rate limits). Optional but recommended. |

#### Example input

```json
{
  "mode": "searchWorks",
  "searchQuery": "machine learning",
  "filterOpenAccess": false,
  "sortBy": "relevance",
  "maxResults": 5
}
```

### Output

The exact fields depend on the mode you run. This is real output from an actual run of this Actor:

```json
{
  "id": "https://openalex.org/W2101234009",
  "doi": "https://doi.org/10.48550/arxiv.1201.0490",
  "title": "Scikit-learn: Machine Learning in Python",
  "authorNames": "Fabián Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Müller, Andreas, Nothman, Joe…",
  "authors": [
    {
      "name": "Fabián Pedregosa",
      "institution": "Commissariat à l'Énergie Atomique et aux Énergies Alternatives"
    },
    {
      "name": "Gaël Varoquaux",
      "institution": "Commissariat à l'Énergie Atomique et aux Énergies Alternatives"
    }
  ],
  "publicationYear": 2012,
  "publicationDate": "2012-01-02",
  "journal": "ORBi (University of Liège)",
  "citedByCount": 64029,
  "isOpenAccess": true,
  "openAccessUrl": "https://orbi.uliege.be/handle/2268/225787",
  "abstract": "Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised …",
  "concepts": [
    "Python (programming language)",
    "Documentation"
  ],
  "type": "article"
}
```

| Field | Type |
|---|---|
| `id` | string |
| `doi` | string |
| `title` | string |
| `authorNames` | string |
| `authors` | array of object — each with `name`, `institution` |
| `publicationYear` | number |
| `publicationDate` | string |
| `journal` | string |
| `citedByCount` | number |
| `isOpenAccess` | boolean |
| `openAccessUrl` | string |
| `abstract` | string |
| `concepts` | array |
| `type` | string |

The dataset also ships a preset table view (**Overview**), so the key columns are readable straight away in Apify Console, and exportable to JSON, CSV, Excel, or XML.

### How to run it

**In Apify Console** — open the Actor, fill in the input form, click **Start**, then download the results from the **Dataset** tab.

**With the JavaScript client**

```javascript
import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'YOUR_APIFY_TOKEN' });

const run = await client.actor('cloud9_ai/openalex-scraper').call({
  "mode": "searchWorks",
  "searchQuery": "machine learning",
  "filterOpenAccess": false,
  "sortBy": "relevance",
  "maxResults": 5
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);
```

**With the Python client**

```python
from apify_client import ApifyClient

client = ApifyClient('YOUR_APIFY_TOKEN')

run = client.actor('cloud9_ai/openalex-scraper').call(run_input={
  "mode": "searchWorks",
  "searchQuery": "machine learning",
  "filterOpenAccess": False,
  "sortBy": "relevance",
  "maxResults": 5
})

for item in client.dataset(run['defaultDatasetId']).iterate_items():
    print(item)
```

**With the API** — `POST https://api.apify.com/v2/acts/cloud9_ai~openalex-scraper/run-sync-get-dataset-items?token=YOUR_APIFY_TOKEN` with the input JSON as the body.

### Notes and limits

- No API key, account, or login is needed — just the input above.
- `maxResults` caps how much a single run collects, which is also what caps the run's cost.
- Requests are paced and failed requests are retried automatically, so runs stay inside the source's rate limits.
- Only publicly available data is collected. How you use the output is your responsibility, including the source's terms of use and any applicable data-protection law.

### Support

Found a bug, or need a field that isn't in the output? Open an issue on the **Issues** tab of this Actor in Apify Console. Issues there are read and answered.

### License

Apache-2.0

# Actor input Schema

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

What to search for

## `searchQuery` (type: `string`):

Search term

## `filterYear` (type: `integer`):

Filter by publication year (optional)

## `filterOpenAccess` (type: `boolean`):

Only return open access papers

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

Sort order for results

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

Maximum number of results

## `email` (type: `string`):

Your email for polite pool (faster rate limits). Optional but recommended.

## Actor input object example

```json
{
  "mode": "searchWorks",
  "searchQuery": "machine learning",
  "filterOpenAccess": false,
  "sortBy": "relevance",
  "maxResults": 5
}
```

# 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": "searchWorks",
    "searchQuery": "machine learning",
    "maxResults": 5
};

// Run the Actor and wait for it to finish
const run = await client.actor("cloud9_ai/openalex-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": "searchWorks",
    "searchQuery": "machine learning",
    "maxResults": 5,
}

# Run the Actor and wait for it to finish
run = client.actor("cloud9_ai/openalex-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": "searchWorks",
  "searchQuery": "machine learning",
  "maxResults": 5
}' |
apify call cloud9_ai/openalex-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,cloud9_ai/openalex-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/qV80mokfdjJrj76PG/builds/GUz47CRlTcdHImhjx/openapi.json