# Academic Research Search (OpenAlex) (`agentictools/openalex-research-search`) Actor

Search over 250 million scholarly works from OpenAlex by topic, author, or institution, with citations, authors, venue, and open access status.

- **URL**: https://apify.com/agentictools/openalex-research-search.md
- **Developed by:** [Ken Agland](https://apify.com/agentictools) (community)
- **Categories:** Other, AI
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

$0.50 / 1,000 paper returneds

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

## Academic Research Search (OpenAlex)

Search over 250 million scholarly works from OpenAlex by topic, author, or institution, with citations, authors, venue, and open access status.

### What it does

- Full-text search across titles, abstracts, and body text using the OpenAlex works API.
- Filter by publication year, open access status, and minimum citation count.
- Returns clean, flat paper records: title, DOI, year, citation count, authors, institutions, venue, open access status and link, topics, and type.
- Exports an aggregate summary (total matched, returned, most cited) to OUTPUT.
- No API key needed.

### Example input

Recent, well-cited papers on a topic:

```json
{
  "search": "large language models",
  "fromYear": 2023,
  "minCitations": 10,
  "maxItems": 40
}
```

Open access papers only:

```json
{
  "search": "CRISPR gene editing",
  "openAccessOnly": true,
  "maxItems": 50
}
```

### Input

| Field | Type | Description |
|---|---|---|
| `search` | string | Full-text search term: a topic, author name, or institution. Required. |
| `fromYear` | integer | Only keep works published in or after this year. Leave empty for no lower bound. |
| `openAccessOnly` | boolean | Only keep works that are freely readable in some open access location. Default false. |
| `minCitations` | integer | Only keep works cited at least this many times. Default 0. |
| `maxItems` | integer | How many works to return (auto-paginated, max 10000). Default 40. |

### Output

Each dataset item is one work:

```json
{
  "title": "ChatGPT for good? On opportunities and challenges of large language models for education",
  "doi": "https://doi.org/10.1016/j.lindif.2023.102274",
  "year": 2023,
  "citedByCount": 5259,
  "authors": ["Enkelejda Kasneci", "Kathrin Seßler"],
  "institutions": ["Technical University of Munich"],
  "venue": "Learning and Individual Differences",
  "isOpenAccess": true,
  "oaUrl": "https://epub.ub.uni-muenchen.de/125071/1/ChatGPT_for_Good_v3.pdf",
  "topics": ["Artificial Intelligence in Healthcare and Education", "Topic Modeling"],
  "type": "article",
  "openAlexId": "https://openalex.org/W4323655724"
}
```

#### Run summary (OUTPUT)

The run's default key-value store record `OUTPUT` holds an aggregate for the whole result set:

```json
{
  "query": { "search": "large language models", "fromYear": 2023, "openAccessOnly": false, "minCitations": 10 },
  "totalMatched": 184213,
  "returned": 40,
  "requestedMaxItems": 40,
  "scanned": 40,
  "mostCited": {
    "title": "...",
    "citedByCount": 5259,
    "openAlexId": "https://openalex.org/W4323655724",
    "doi": "https://doi.org/10.1016/j.lindif.2023.102274"
  },
  "generatedFrom": "https://api.openalex.org/works"
}
```

### How it works

The Actor calls the public OpenAlex works API with your search term and paginates until it has `maxItems` results or runs out of matches. Year, open access, and citation filters are combined into a single OpenAlex `filter` expression and applied server-side. Every request includes a `mailto` parameter for the polite pool and a descriptive User-Agent, and retries on rate limits (429) and server errors with backoff, honoring the API's Retry-After header.

### Use cases

- Literature review: pull recent, well-cited papers on a research topic.
- Track output from an author or institution over time.
- Find open access versions of papers for a reading list.
- Feed structured citation data into research dashboards or agents.

MIT licensed.

# Actor input Schema

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

Full-text search term matched against titles, abstracts, and full text (for example a topic, author name, or institution). Required.

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

Only keep works published in or after this year. Leave empty for no lower bound.

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

Only keep works that are freely readable in some open access location.

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

Only keep works cited at least this many times.

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

How many works to return. Results are paginated automatically until this count is reached or there are no more matches.

## Actor input object example

```json
{
  "search": "CRISPR gene editing",
  "openAccessOnly": false,
  "minCitations": 0,
  "maxItems": 40
}
```

# 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 = {
    "search": "large language models"
};

// Run the Actor and wait for it to finish
const run = await client.actor("agentictools/openalex-research-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 = { "search": "large language models" }

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

```

## MCP server setup

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

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/acts/Jr7Bs1ZRFCAiHxrUa/builds/rUmZJ5gSuC2cYECai/openapi.json