# Academic Paper Search (`brightpath-data/academic-paper-search`) Actor

Search OpenAlex, Crossref, arXiv and PubMed in one query, with abstracts, open-access links and citation counts

- **URL**: https://apify.com/brightpath-data/academic-paper-search.md
- **Developed by:** [Nick Randall](https://apify.com/brightpath-data) (community)
- **Categories:** Developer tools
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $1.50 / 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/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

## Academic Paper Search

Stop writing four separate integrations for OpenAlex, Crossref, arXiv and PubMed just to do a literature search. One query here searches all four and hands back one normalized table.

Get clean, structured paper records from OpenAlex, Crossref, arXiv and PubMed as JSON, CSV or Excel, or call it as a tool from Claude, Cursor, ChatGPT or any MCP client. Pay only for the results you receive.

### What you get

Give it a search query and it returns matching papers from up to four open sources at once: title, authors, abstract (where the source provides one), publication year, DOI, the paper's own URL, an open-access link when one exists, and a citation count where the source tracks it. Results interleave across sources as they come in, so you are not stuck waiting on one slow source before seeing anything from the others.

Every result is a flat record with stable field names, so it drops straight into a spreadsheet, a database or an AI agent's context.

### Why use this instead of the website

- One query covers OpenAlex, Crossref, arXiv and PubMed instead of four separate searches and four response formats to parse
- Abstracts reconstructed from OpenAlex's inverted-index format and cleaned of HTML from Crossref, not left for you to decode
- Open-access links and citation counts pulled out into their own fields where the source has them
- A source that is briefly down or rate-limited is skipped, not fatal; the run still returns results from the rest
- Results in JSON, CSV, Excel or via API, or piped into Zapier, Make, n8n and Google Sheets
- Works as an MCP tool, so AI agents can pull papers for a lit review on demand
- No browser, no proxies, no personal data: fast runs and a tiny cost per result

### Input

| Field | Type | Default | Meaning |
|-------|------|---------|---------|
| `query` | string | | Search terms |
| `sources` | array | all four | Which sources to search: `openalex`, `crossref`, `arxiv`, `pubmed` |
| `maxResults` | integer | 100 | Cap on results saved. You are charged per result, so this caps your cost. |

Example input:

```json
{
  "query": "machine learning interpretability",
  "sources": ["openalex", "crossref", "arxiv", "pubmed"],
  "maxResults": 100
}
```

### Output

Sample rows from a live run for "machine learning interpretability", showing all four sources:

| source | title | year | venue | citationCount |
|---|---|---|---|---|
| openalex | Explainable AI: A Review of Machine Learning Interpretability Methods | 2020 | Entropy | 2933 |
| crossref | Bias, noise, and interpretability in machine learning | 2020 | Machine Learning | 4 |
| arxiv | Changing Data Sources in the Age of Machine Learning for Official Statistics | 2023 | arXiv | null |
| pubmed | Transforming Nanomaterials Development with Artificial Intelligence Techniques | 2026 | Nanotechnology, Science and Applications | null |

Full example result (OpenAlex):

```json
{
  "source": "openalex",
  "id": "https://openalex.org/W2896008109",
  "title": "Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)",
  "authors": ["Amina Adadi", "Mohammed Berrada"],
  "abstract": "At the dawn of the fourth industrial revolution, we are witnessing a fast and widespread adoption of Artificial Intelligence...",
  "year": 2018,
  "doi": "https://doi.org/10.1109/access.2018.2870052",
  "url": "https://openalex.org/W2896008109",
  "openAccessUrl": "https://ieeexplore.ieee.org/ielx7/6287639/8274985/08466590.pdf",
  "citationCount": 5049,
  "venue": "IEEE Access",
  "fetchedAt": "2026-09-24T18:12:03.441Z"
}
```

Field reference:

- `source`: which of the four sources this record came from (`openalex`, `crossref`, `arxiv`, `pubmed`)
- `id`: the source's own identifier for the paper (OpenAlex work ID, Crossref DOI, arXiv abstract URL, PubMed PMID)
- `title`, `authors` (list), `abstract`, `year`, `doi`, `url`, `venue` (journal, conference or "arXiv")
- `openAccessUrl`: a free full-text link where the source provides one, otherwise null
- `citationCount`: where the source tracks it (OpenAlex and Crossref), otherwise null
- `fetchedAt`: ISO timestamp of when this Actor fetched the record
- PubMed records have no `abstract` or `doi`: PubMed's summary endpoint does not return either reliably, so both are null for that source

### Pricing

Pay per event. You are charged **$1.50 per 1,000 results** saved to the dataset, plus a fraction of a cent per run start. Nothing is charged for results you do not receive. Set "Max total charge per run" in the run options to cap spending on any run. When a run reaches your cap it stops cleanly and keeps everything it already saved.

Rough guide: 1,000 results cost $1.50 and take about 400 seconds, mostly PubMed's required pacing between requests.

### Use it from an AI agent (MCP)

This Actor is available as an MCP tool through the Apify MCP server. Add it to your client, then ask the agent for the data in plain language.

Claude Desktop, Claude Code or Cursor (`mcp.json` / `claude_desktop_config.json`):

```json
{
  "mcpServers": {
    "apify": {
      "url": "https://mcp.apify.com/?actors=brightpath-data/academic-paper-search",
      "headers": { "Authorization": "Bearer YOUR_APIFY_TOKEN" }
    }
  }
}
```

ChatGPT and other clients that support remote MCP servers: add `https://mcp.apify.com/?actors=brightpath-data/academic-paper-search` as a connector with your Apify token.

Example prompt once connected: "Find recent papers on machine learning interpretability with open-access PDFs and their citation counts."

### Use it from code

```bash
curl -X POST "https://api.apify.com/v2/acts/brightpath-data~academic-paper-search/run-sync-get-dataset-items?token=YOUR_APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"query":"machine learning interpretability","sources":["openalex","crossref","arxiv","pubmed"],"maxResults":100}'
```

Python:

```python
from apify_client import ApifyClient
client = ApifyClient("YOUR_APIFY_TOKEN")
run = client.actor("brightpath-data/academic-paper-search").call(run_input={"query": "machine learning interpretability", "sources": ["openalex", "crossref", "arxiv", "pubmed"], "maxResults": 100})
items = client.dataset(run["defaultDatasetId"]).list_items().items
```

### Limits and fair use

- Up to 10,000 results per run.
- PubMed is paced more slowly than the other three sources (NCBI rate-limits hard without an API key), so runs that include it take noticeably longer per 1,000 results; drop `pubmed` from `sources` for a faster run if you do not need it.
- OpenAlex, Crossref and arXiv are open, rate-limit-friendly APIs and are queried at a normal, polite pace.
- PubMed's summary endpoint does not reliably return an abstract or DOI, so those fields are null for PubMed results.
- A source that errors or is unreachable during a run is skipped for the rest of that run rather than failing it; the other sources still produce results.

### Data source and legal

Data comes from four public, no-login metadata services: [OpenAlex](https://openalex.org) (CC0, an open catalog of scholarly works), [Crossref](https://www.crossref.org) (the DOI registration agency's public metadata API), [arXiv](https://arxiv.org) (the open preprint repository's public API), and [PubMed](https://pubmed.ncbi.nlm.nih.gov) via NCBI's E-utilities (public biomedical literature metadata). This Actor collects public, non-personal data only (paper and author metadata, no reviewer or personal contact information) and does not bypass logins, paywalls or access controls. You are responsible for how you use the data.

### Support

Found a problem or need a field added? Open an issue on the Actor's Issues tab. Fixes for broken runs are prioritized.

# Actor input Schema

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

Search terms

## `sources` (type: `array`):

Which sources to search (openalex, crossref, arxiv, pubmed)

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

Maximum number of results to save. You are charged per result saved, so this also caps the cost of a run.

## Actor input object example

```json
{
  "query": "machine learning interpretability",
  "sources": [
    "openalex",
    "crossref",
    "arxiv",
    "pubmed"
  ],
  "maxResults": 100
}
```

# Actor output Schema

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

The dataset with one flat record per result. Append ?format=csv or ?format=xlsx to the URL for other formats.

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

OUTPUT record in the key-value store: counts of results pushed and charged, requests, retries and duration.

# 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": "machine learning interpretability"
};

// Run the Actor and wait for it to finish
const run = await client.actor("brightpath-data/academic-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": "machine learning interpretability" }

# Run the Actor and wait for it to finish
run = client.actor("brightpath-data/academic-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": "machine learning interpretability"
}' |
apify call brightpath-data/academic-paper-search --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,brightpath-data/academic-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/1hybIbeAfH7IaqmeL/builds/MbwC0iNwxoJj8oiYe/openapi.json