# Academic Paper Search API: Crossref, arXiv & DOI Lookup (`tudorlane/academic-paper-search`) Actor

Search 150M+ scholarly papers by topic or look up any DOI: title, authors, year, journal, citations, abstract, PDF/open-access links. Crossref plus arXiv, no API key.

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

## Pricing

$3.00 / 1,000 paper searches

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 API: Crossref, arXiv & DOI Lookup

**Use when:** an AI agent or research pipeline needs real, checkable citations: find papers on a topic (journals, books, preprints), or verify that a DOI exists and get its title, authors, year, venue and citation count, instead of trusting a model's memory.

**Try this input**

```json
{"requests": [{"query": "large language model hallucination", "from_year": 2024, "sort": "cited", "max": 3}, {"doi": "10.1038/nature14539"}, {"doi": "10.48550/arXiv.1706.03762"}]}
```

**Sample output** (real run, 30 Oct 2026, trimmed)

```json
{"query": "large language model hallucination", "count": 6, "results": [
  {"title": "A Survey on Evaluation of Large Language Models", "year": 2024, "venue": "ACM Transactions on Intelligent Systems and Technology", "doi": "10.1145/3641289", "citations": 3001, "via": "Crossref"},
  {"title": "A Survey on Hallucination in Large Language Models: Principles, Taxonomy, ...", "year": 2025, "doi": "10.1145/3703155", "citations": 2389, "via": "Crossref"},
  {"title": "Hallucination Detection with Small Language Models", "year": 2025, "venue": "arXiv", "doi": "10.48550/arXiv.2506.22486", "pdf_url": "https://arxiv.org/pdf/2506.22486...", "via": "arXiv"}], "error": null}
{"query": "10.1038/nature14539", "count": 1, "results": [{"title": "Deep learning", "authors": ["Yann LeCun", "Yoshua Bengio", "Geoffrey Hinton"], "year": 2015, "venue": "Nature",
  "type": "journal-article", "citations": 78904, "publisher": "Springer Science and Business Media LLC", "pdf_url": "http://www.nature.com/articles/nature14539.pdf", "via": "Crossref"}], "error": null}
{"query": "10.48550/arXiv.1706.03762", "count": 1, "results": [{"title": "Attention Is All You Need", "authors": ["Ashish Vaswani", "Noam Shazeer", "..."], "year": 2017, "publisher": "arXiv", "type": "article", "abstract": "The dominant sequence transduction models...", "via": "DOI registry (DataCite/other)"}], "error": null}
```

**Options** (per item in `requests`):

- `query`: topic, title or keywords (a DOI pasted in a query is detected and looked up).
- `doi`: look up one DOI (Crossref first, then doi.org for DataCite/other registries such as arXiv and Zenodo).
- `max`: results per source, default 10, max 50 (Crossref up to `max`, arXiv up to 10; combined list capped at 2 × `max`).
- `from_year`: only works published from this year (Crossref).
- `sort`: `relevance` (default), `newest` or `cited` (Crossref).
- `include_arxiv`: default `true`; set `false` for Crossref only.

Each paper has `title`, `authors` (first 15), `year`, `date`, `venue`, `doi`, `url`, `type`, `citations`, `publisher`, `abstract` (when deposited, up to 1500 chars), `pdf_url`, `license` and `via`. Up to 300 items per run; no API key needed.

**Pricing:** pay per event, **$0.003 per paper search** (one topic search or DOI lookup). Rows with errors (e.g. DOI not found, empty query) are not charged.

### Limits & failure modes

- `from_year` and `sort` apply to Crossref only; arXiv results are appended by relevance and may be older or unsorted.
- Citation counts are Crossref's `is-referenced-by-count` (lower than Google Scholar); arXiv and DataCite records have none.
- Many Crossref records have no abstract; publishers decide what they deposit.
- Crossref relevance is keyword-based, so broad queries can return loosely related highly cited papers. Use specific phrases.
- Crossref or arXiv rate limits can occasionally make a row fail with "source unavailable" (not charged); retry it.
- Metadata from Crossref REST API and arXiv API (CC0). Thank you to arXiv for use of its open access interoperability. Abstracts and full texts belong to their authors and publishers.

Last tested: 30 Oct 2026.

### Use from AI agents (MCP)

- **Apify MCP server:** add `https://mcp.apify.com/?tools=tudorlane/academic-paper-search` to Claude, Cursor, ChatGPT, VS Code or any MCP client.
- **REST:** `POST https://api.apify.com/v2/acts/tudorlane~academic-paper-search/run-sync-get-dataset-items?token=YOUR_TOKEN` with the input JSON.

# Actor input Schema

## `requests` (type: `array`):

Each item: {"query": "...", "max": 10, "from_year": 2020, "sort": "relevance|newest|cited"} or {"doi": "10.xxxx/..."}. Max 300. Errors are not charged.

## Actor input object example

```json
{
  "requests": [
    {
      "query": "counterfeit aircraft parts detection",
      "max": 5
    },
    {
      "doi": "10.48550/arXiv.1706.03762"
    }
  ]
}
```

# Actor output Schema

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

One item per request.

## `dataset` (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 = {
    "requests": [
        {
            "query": "counterfeit aircraft parts detection",
            "max": 5
        },
        {
            "doi": "10.48550/arXiv.1706.03762"
        }
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("tudorlane/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 = { "requests": [
        {
            "query": "counterfeit aircraft parts detection",
            "max": 5,
        },
        { "doi": "10.48550/arXiv.1706.03762" },
    ] }

# Run the Actor and wait for it to finish
run = client.actor("tudorlane/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 '{
  "requests": [
    {
      "query": "counterfeit aircraft parts detection",
      "max": 5
    },
    {
      "doi": "10.48550/arXiv.1706.03762"
    }
  ]
}' |
apify call tudorlane/academic-paper-search --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,tudorlane/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/VyJN53220ofheCwLW/builds/uVGSGXP9pExHWkI0w/openapi.json