# OpenAlex Scraper - Scholarly Works & Citations (`ninhothedev/openalex-scraper`) Actor

$0.5/1K 🔥 Fast OpenAlex scraper! Scholarly works & authors — title, year, citations, institutions & open-access links. No key. JSON, CSV, Excel or API in seconds. Pull thousands for research intel & citation analysis ⚡

- **URL**: https://apify.com/ninhothedev/openalex-scraper.md
- **Developed by:** [ninhothedev](https://apify.com/ninhothedev) (community)
- **Categories:** Developer tools, Automation, Other
- **Stats:** 3 total users, 2 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $0.50 / 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

## OpenAlex Scraper 🎓

**Scrape millions of scholarly works, author profiles and citation data from [OpenAlex](https://openalex.org) — by search query, with no API key.** Clean, structured JSON built for literature reviews, research intelligence, citation analysis and open-science datasets.

OpenAlex is a fully open index of the global research graph (250M+ works, 90M+ authors, journals, institutions and concepts). This actor turns any keyword search into a tidy dataset you can export to CSV, JSON, Excel or push straight into your data warehouse.

***

### 💡 What you get

Pick a **mode** and hand it a list of search queries.

#### Mode: `works` (papers)

Every record is one scholarly work:

| Field | Description |
|-------|-------------|
| `openalex_id` | OpenAlex work ID (e.g. `W2163605009`) |
| `doi` | Clean DOI (e.g. `10.1145/3065386`) |
| `title` | Paper title |
| `year` / `publication_date` | Year and full publication date |
| `cited_by_count` | Total citations |
| `authors` | List of author names |
| `institutions` | Unique list of affiliated institutions |
| `journal` | Source / journal / venue name |
| `is_oa` / `oa_url` / `pdf_url` | Open-access status and full-text links |
| `concepts` | List of topic / concept labels |
| `type` | Work type (article, review, dataset, …) |

#### Mode: `authors` (profiles)

Every record is one author:

| Field | Description |
|-------|-------------|
| `openalex_id` | OpenAlex author ID |
| `name` | Author display name |
| `orcid` | ORCID iD (when known) |
| `works_count` | Number of works |
| `cited_by_count` | Total citations |
| `institution` | Last known institution |

Every record also carries `source: "openalex"` and a `scraped_at` timestamp. **All fields are nullable** — missing data becomes `null` rather than breaking your pipeline.

***

### 🚀 Input

```json
{
  "mode": "works",
  "queries": ["neural networks", "crispr"],
  "maxItems": 100
}
```

| Input | Type | Description |
|-------|------|-------------|
| `mode` | select | `works` (papers) or `authors` (profiles). Default `works`. |
| `queries` | array | One or more free-text search queries. Results are merged and de-duplicated. |
| `maxItems` | integer | Total records across all queries (1–1000). The budget is split evenly per query. |

***

### 📤 Example output (works)

```json
{
  "openalex_id": "W2163605009",
  "doi": "10.1145/3065386",
  "title": "ImageNet classification with deep convolutional neural networks",
  "year": 2017,
  "publication_date": "2017-05-24",
  "cited_by_count": 75715,
  "authors": ["Alex Krizhevsky", "Ilya Sutskever", "Geoffrey E. Hinton"],
  "institutions": ["Google (United States)", "University of Toronto"],
  "journal": "Communications of the ACM",
  "is_oa": true,
  "oa_url": "http://dl.acm.org/ft_gateway.cfm?id=3065386&type=pdf",
  "pdf_url": "http://dl.acm.org/ft_gateway.cfm?id=3065386&type=pdf",
  "concepts": ["Softmax function", "Convolutional neural network"],
  "type": "article",
  "source": "openalex",
  "scraped_at": "2026-07-20T21:07:00Z"
}
```

***

### 💰 Pricing

Cheap and predictable — roughly **$0.5 per 1,000 records**. The actor uses OpenAlex's polite pool for fast, reliable responses and runs on just 512 MB, so most jobs cost a few cents.

***

### 🎯 Use cases

- **Literature review** — pull every paper on a topic with citations, authors and open-access PDFs.
- **Research intelligence** — track who is publishing what, where, and how often it is cited.
- **Citation analysis** — rank works and authors by impact across a field.
- **Open science datasets** — build reproducible corpora with DOIs and OA links for downstream NLP/ML.

***

### 🔗 Related actors

- [Semantic Scholar Scraper](https://apify.com/ninhothedev/semantic-scholar-scraper)
- [Crossref Scraper](https://apify.com/ninhothedev/crossref-scraper)
- [ORCID Scraper](https://apify.com/ninhothedev/orcid-scraper)
- [arXiv Scraper](https://apify.com/ninhothedev/arxiv-scraper)

***

### 🔍 Keywords

openalex scraper, scholarly works api, academic paper scraper, citation data, research metadata, open access papers, author profiles, doi lookup, bibliometrics, literature review tool, scholarly search, science of science, research graph, no api key.

***

*This actor uses only the public OpenAlex API and adds a polite contact email for reliability. Data © OpenAlex, [CC0](https://openalex.org/about).*

# Actor input Schema

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

What to scrape. 'works' returns scholarly papers with authors, journals, citations and open-access links. 'authors' returns author profiles with works count, citation totals and their last known institution.

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

One or more free-text search queries. Each query is searched against OpenAlex full text (works) or author names (authors). Results from all queries are merged and de-duplicated.

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

Maximum number of records to return in total across all queries. The budget is split evenly between queries. Range 1-1000.

## Actor input object example

```json
{
  "mode": "works",
  "queries": [
    "neural networks",
    "crispr"
  ],
  "maxItems": 100
}
```

# 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": [
        "neural networks",
        "crispr"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("ninhothedev/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 = { "queries": [
        "neural networks",
        "crispr",
    ] }

# Run the Actor and wait for it to finish
run = client.actor("ninhothedev/openalex-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": [
    "neural networks",
    "crispr"
  ]
}' |
apify call ninhothedev/openalex-scraper --silent --output-dataset

```

## MCP server setup

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

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/aUx7mfTeI1uqB6E1f/builds/RmSwSTZHXbhYHzXe0/openapi.json