# OpenAlex Scholarly Works and Citation Intelligence Radar (`adaptive_nightmare/openalex-academic-graph-radar`) Actor

Search 250M+ scholarly research papers, citation metrics, author h-indices, institutional affiliations, and open access PDF links from OpenAlex.

- **URL**: https://apify.com/adaptive_nightmare/openalex-academic-graph-radar.md
- **Developed by:** [Darim Quraishi](https://apify.com/adaptive_nightmare) (community)
- **Categories:** Education, AI, Developer tools
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

$1.15 / 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 Scholarly Works and Citation Intelligence Radar

Search over 250 million scholarly research papers, citation metrics, author institutional affiliations, and open access PDF links from OpenAlex.

### Features

- Queries the comprehensive OpenAlex academic index connecting global journal publications and conference preprints.
- Captures title headlines, publication dates, primary authors, and university affiliations.
- Gathers citation counts, journal venues, and research topic classifications.
- Provides direct links to open access PDF copies and permanent DOI landing pages.
- Operates via public OpenAlex REST endpoints with zero API keys and zero proxy overhead.
- Delivers clean records formatted for export to JSON, CSV, Excel, or automated RAG ingestion pipelines.

### Input parameters

- Search Topic or Keyword: Research field, paper title, or concept phrase. Default is deep learning.
- Maximum Papers: Maximum number of academic publications to retrieve, up to 100. Default is 25.

### Output dataset fields

Each dataset record contains:

- id: Unique OpenAlex entity identifier.
- doi: Digital Object Identifier link.
- title: Full research paper headline.
- publicationYear: Calendar year of release.
- publicationDate: Full publication date.
- authors: Array of primary author names.
- institutions: Contributing academic and corporate research bodies.
- citedByCount: Total recorded academic citations.
- journal: Publishing journal or conference proceedings title.
- isOpenAccess: Boolean flag indicating free public accessibility.
- openAccessPdfUrl: Direct link to the open access PDF file.
- primaryTopic: High-level scientific subfield.
- landingPageUrl: Direct publisher landing page.
- scrapedAt: UTC timestamp when the data was extracted.

### Use cases

- Literature reviews: Compile systematic reviews and citation rankings on frontier medical and AI breakthroughs.
- RAG knowledge pipelines: Ingest open access academic abstracts into vector databases for research teams.
- Academic analytics: Track institutional publication output and citation impact across competing university faculties.

# Actor input Schema

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

Academic topic, journal title, or research concept (for example: deep learning, quantum computing, or gene therapy).

## `maxWorks` (type: `integer`):

Maximum number of scholarly publications to retrieve.

## Actor input object example

```json
{
  "query": "deep learning",
  "maxWorks": 25
}
```

# 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 = {};

// Run the Actor and wait for it to finish
const run = await client.actor("adaptive_nightmare/openalex-academic-graph-radar").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 = {}

# Run the Actor and wait for it to finish
run = client.actor("adaptive_nightmare/openalex-academic-graph-radar").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 '{}' |
apify call adaptive_nightmare/openalex-academic-graph-radar --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,adaptive_nightmare/openalex-academic-graph-radar"
        }
    }
}
```

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/jCDDuKJiI93IylOlK/builds/AwkcPI2oIvytMnIlU/openapi.json