# OpenAlex Paper Search (`petipeti/openalex-paper-search`) Actor

Search 250M+ scholarly works via the official OpenAlex API (CC0 data). Filter by year, open access, citations; get DOI, venue, citation counts and open-access PDF links as clean rows.

- **URL**: https://apify.com/petipeti/openalex-paper-search.md
- **Developed by:** [peti peti](https://apify.com/petipeti) (community)
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $1.50 / 1,000 paper 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 Paper Search

Search more than 250 million scholarly works (journal articles, preprints, books, datasets) and get clean rows with title, year, venue, citation count, DOI and a **free open-access link when one exists**.

Uses the **official OpenAlex API**. OpenAlex data is released under **CC0** (public domain), so the results can be used in commercial projects, reports and datasets. No website is scraped.

### Why use this instead of the API directly

- No code: fill a form, get a spreadsheet (CSV / Excel / JSON)
- Paging, retries and rate limits handled for you (up to 10,000 results per run)
- Nested API JSON flattened into one row per paper
- Easy to plug into Make, Zapier, n8n, or an LLM / RAG pipeline that needs paper lists with links

### Input

```json
{
  "query": "\"large language models\" education",
  "sort": "relevance",
  "fromYear": 2022,
  "openAccessOnly": true,
  "maxResults": 200
}
```

| Field | Notes |
|---|---|
| `query` | Use double quotes for exact phrases. |
| `searchIn` | `title_and_abstract` (default, precise) or `fulltext` (broader). |
| `sort` | `relevance`, `citations` (most cited), `newest`. |
| `fromYear` / `toYear` | Publication year range. |
| `openAccessOnly` | Only papers with a free legal copy. |
| `minCitations` | e.g. 50 for well-known papers only. |
| `type` | article, review, preprint, book, book-chapter, dissertation, dataset. |
| `language` | ISO code such as `en` or `ja`. |
| `apiKey` | Optional free OpenAlex key for bigger runs. |

### Output (one row per paper)

```json
{
  "openAlexId": "https://openalex.org/W4319460874",
  "doi": "https://doi.org/10.1016/j.lindif.2023.102274",
  "title": "ChatGPT for good? On opportunities and challenges of large language models for education",
  "publicationYear": 2023,
  "publicationDate": "2023-03-09",
  "type": "article",
  "language": "en",
  "venue": "Learning and Individual Differences",
  "landingPageUrl": "https://doi.org/10.1016/j.lindif.2023.102274",
  "citedByCount": 2500,
  "isOpenAccess": true,
  "openAccessStatus": "hybrid",
  "openAccessUrl": "https://...",
  "authors": ["Enkelejda Kasneci", "..."],
  "topic": "Artificial Intelligence in Education",
  "field": "Social Sciences"
}
```

(Values above are illustrative.)

### Use cases

- Literature reviews: the 200 most cited papers on a topic, with free PDF links
- Market / technology research: what is being published on a subject each year
- Feeding a RAG system with paper metadata and open-access links
- Monitoring new papers weekly with a schedule and `sort: newest`

### What is not included

- Abstracts and full texts are not copied (publishers may hold the copyright). Follow `openAccessUrl` or `landingPageUrl` to read them.
- Author contact details are not collected. Author names are included only as bibliographic metadata.

### Pricing

Pay per event, platform usage included:

| Event | Price |
|---|---|
| Actor start | $0.01 per run |
| `paper-result` – one paper returned | $1.50 per 1,000 papers |

Example: a search returning 200 papers costs $0.01 + $0.30 = $0.31.

### Credits

Data from [OpenAlex](https://openalex.org) (Priem, J., Piwowar, H., & Orr, R. 2022, arXiv:2205.01833). This Actor is not affiliated with or endorsed by OpenAlex / OurResearch.

### 日本語での説明

公式 OpenAlex API（データは CC0 で商用利用可）を使って、2億5千万件以上の論文を検索し、タイトル・年・掲載誌・被引用数・DOI・無料で読めるリンクを表にして返します。サイトのスクレイピングはしません。要旨や本文はコピーしません。

# Actor input Schema

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

Words to search for. Use double quotes for an exact phrase, e.g. "large language models".

## `searchIn` (type: `string`):

Search in.

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

Sort by.

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

Max results.

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

From year.

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

To year.

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

Open access only.

## `minCitations` (type: `integer`):

Minimum citations.

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

Leave empty for all types.

## `language` (type: `string`):

Language (ISO 639-1, e.g. en, ja).

## `apiKey` (type: `string`):

Free from openalex.org. Without a key the Actor uses the smaller keyless allowance, which is enough for small runs.

## Actor input object example

```json
{
  "query": "\"large language models\" education",
  "searchIn": "title_and_abstract",
  "sort": "relevance",
  "maxResults": 100,
  "openAccessOnly": false,
  "minCitations": 0
}
```

# Actor output Schema

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

Dataset items (Overview view).

# 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\" education"
};

// Run the Actor and wait for it to finish
const run = await client.actor("petipeti/openalex-paper-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\" education" }

# Run the Actor and wait for it to finish
run = client.actor("petipeti/openalex-paper-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\\" education"
}' |
apify call petipeti/openalex-paper-search --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,petipeti/openalex-paper-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/RfG7PKw638i9Dg14I/builds/FoZVGW7upHHsZwkmS/openapi.json