# OpenAlex Academic Research Scraper - Papers and Citations (`arqlabs/openalex-works-search`) Actor

Search 250M+ scholarly works in OpenAlex and export title, abstract, authors, institutions, venue, year, DOI, citation count and open access link as JSON or CSV. A free Google Scholar alternative for research and AI agents. Pay per work.

- **URL**: https://apify.com/arqlabs/openalex-works-search.md
- **Developed by:** [Muhammad Ariq Fauzan](https://apify.com/arqlabs) (community)
- **Categories:** AI, Other
- **Security:** Limited permissions
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

Pay per event

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 - Papers and Citations

Use it as an OpenAlex academic research scraper: find papers and citation counts for literature reviews, bibliometrics and AI research agents.

OpenAlex Scraper searches more than 250 million scholarly works in OpenAlex, the free and open catalog of the world's research, and exports each paper with its title, abstract, authors, institutions, journal or venue, publication year, DOI, citation count, topics and open access link. Use it for literature reviews, citation analysis, research trend monitoring, academic lead lists and RAG over scientific literature.

### Why this Actor

- Official source. It uses the free OpenAlex REST API, with no HTML scraping and no blocking.
- Covers every field. OpenAlex indexes journals, conferences, preprints, books and datasets across all disciplines, a free alternative to Scopus and Web of Science.
- Rich fields. Abstracts are rebuilt into plain text, with citation counts, institutions and open access PDF links.
- Polite. It pages with cursors and backs off on rate limits.
- Use cases: literature reviews, bibliometrics, finding highly cited papers, tracking a topic over time, building academic datasets and RAG pipelines.

### Example run

Real input and the first output record from run WNPr12mcRD7CgCkbb on 2026-10-30. Long text fields are shortened here with "...".

Input:

```json
{
  "query": "large language models",
  "maxResults": 50,
  "sort": "relevance",
  "openAccessOnly": false
}
```

Output (first record):

```json
{
  "openalexId": "W4362515116",
  "doi": "10.1007/s11704-026-60308-3",
  "title": "A Survey of Large Language Models",
  "year": 2026,
  "publicationDate": "2026-05-09",
  "type": "article",
  "venue": "Frontiers of Computer Science",
  "citedByCount": 2085,
  "isOpenAccess": true,
  "oaUrl": "https://link.springer.com/content/pdf/10.1007/s11704-026-60308-3.pdf",
  "authors": [
    "Wayne Xin Zhao",
    "Kun Zhou",
    "Junyi Li",
    "Tianyi Tang",
    "Zican Dong",
    "Yupeng Hou",
    "..."
  ],
  "institutions": [
    "Renmin University of China",
    "Université de Montréal"
  ],
  "topics": [
    "Topic Modeling",
    "Natural Language Processing Techniques",
    "Multimodal Machine Learning Applications"
  ],
  "abstract": "Abstract The rapid evolution of large language models (LLMs) has driven a transformative shift in artificial intelligence (AI), reshaping both research paradigms and practical applications. Distinguished from their predecessors by unprecede...",
  "url": "https://openalex.org/W4362515116"
}
```

### Input

| Field | Description |
|---|---|
| `query` | Full-text search, e.g. `large language models` |
| `maxResults` | 1 to 10,000 |
| `sort` | `relevance`, `cited` (most cited first) or `newest` |
| `fromYear` / `toYear` | Optional publication year range |
| `openAccessOnly` | Only works with a free full-text version |

### Output (one item per work)

```json
{"openalexId":"W4385245566","doi":"10.48550/arxiv.2303.08774","title":"GPT-4 Technical Report","year":2023,"publicationDate":"2023-03-15","type":"preprint","venue":"arXiv","citedByCount":1234,"isOpenAccess":true,"oaUrl":"https://arxiv.org/pdf/2303.08774","authors":["..."],"institutions":["..."],"topics":["..."],"abstract":"...","url":"https://openalex.org/W4385245566"}
```

### Notes

OpenAlex data is released under CC0 by OurResearch. Abstracts may be missing for some works because of publisher restrictions. This Actor is not affiliated with OpenAlex or OurResearch.

### Pricing

Pay-per-event: a small start fee plus a fee per work returned.

# Actor input Schema

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

Full-text search over titles, abstracts and full text, e.g. large language models. Leave empty to list all works matching the filters.

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

Maximum number of works to return (1 to 10,000).

## `sort` (type: `string`):

Order of results: relevance to the query, most cited first, or newest first.

## `fromYear` (type: `integer`):

Only works published in or after this year (optional).

## `toYear` (type: `integer`):

Only works published in or before this year (optional).

## `openAccessOnly` (type: `boolean`):

Return only works with a free open access version.

## Actor input object example

```json
{
  "query": "large language models",
  "maxResults": 20,
  "sort": "relevance",
  "openAccessOnly": false
}
```

# Actor output Schema

## `results` (type: `string`):

No description

# 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 = {
    "query": "large language models",
    "maxResults": 20
};

// Run the Actor and wait for it to finish
const run = await client.actor("arqlabs/openalex-works-search").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 = {
    "query": "large language models",
    "maxResults": 20,
}

# Run the Actor and wait for it to finish
run = client.actor("arqlabs/openalex-works-search").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 '{
  "query": "large language models",
  "maxResults": 20
}' |
apify call arqlabs/openalex-works-search --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,arqlabs/openalex-works-search"
        }
    }
}
```

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/mi4iFfHM80OyQRTfQ/builds/1bzXZjEdudjxh3lZM/openapi.json