# Academic Paper Search: Research Papers & Literature Search (`api-co/academic-papers-search`) Actor

Academic paper and literature search across 250M+ scholarly works in OpenAlex, Crossref, arXiv and PubMed in one run. Research papers merged and de-duplicated by DOI, with abstracts, citations, venues and free PDF links. A Google Scholar alternative.

- **URL**: https://apify.com/api-co/academic-papers-search.md
- **Developed by:** [Emilio Cisneros](https://apify.com/api-co) (community)
- **Categories:** AI, Developer tools, Other
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $0.80 / 1,000 papers

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

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: Research Papers & Literature Search

Search **250M+ scholarly papers across four open databases at once**: OpenAlex, Crossref, arXiv and PubMed. You get one clean, **de-duplicated** list (merged by DOI and title) with authors, abstracts, citation counts, venues and **free PDF links** where they exist.

It's an open alternative to Google Scholar scraping. It uses only official public APIs, so it needs no API keys, no proxies and no CAPTCHAs, and it doesn't break when a site changes its layout.

### How to use

1. Type your search **query** (topic, title words or keywords).
2. Pick sources (OpenAlex, Crossref, arXiv, PubMed), a year range and sort order.
3. Click **Start** and get de-duplicated papers with abstracts, citations, DOIs and free PDF links.

### What you get per paper

| Field | Example |
|---|---|
| `title`, `authors`, `year`, `publicationDate` | "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks", \["Patrick Lewis", …], 2020 |
| `venue`, `type` | "NeurIPS", "article" / "preprint" |
| `doi`, `url` | `10.48550/arxiv.2005.11401`, `https://doi.org/...` |
| `pdfUrl`, `isOpenAccess` | Direct open-access PDF (arXiv, PMC, OpenAlex best OA location) |
| `citedByCount` | 1187. This is the highest count reported by OpenAlex or Crossref. |
| `abstract` | Full abstract text. OpenAlex inverted indexes are rebuilt into plain text, and Crossref JATS tags are stripped. |
| `topics` | OpenAlex topics, arXiv categories, Crossref subjects |
| `ids` | `openalex`, `arxiv`, `pmid`, `pmcid` |
| `sources`, `foundIn` | Which databases returned the paper. Papers found in several databases rank higher. |

### Why this Actor

- **Four databases, one schema, no duplicates.** The same paper in OpenAlex, PubMed and Crossref comes back as a single row, with the best value for each field.
- **Relevance that holds up.** Loose keyword matches are dropped, and results are ranked by query-term coverage, agreement across databases and citations. You can also sort by **newest** or **most cited**.
- **Filters**: year range, open access only, and only papers with abstracts (handy for RAG and LLM pipelines).
- **Fair pay-per-result pricing**: **$1.00 per 1,000 unique papers**. Error records are free, and if a source is down you still get the other sources' results without paying for the failure.
- **Fast**: 300 merged papers in about 10 seconds in our tests.

### Use cases

- Literature reviews and systematic-review screening (export to CSV/Excel)
- Building RAG corpora and knowledge bases from abstracts and OA PDFs
- Research monitoring: schedule a daily run sorted by newest
- Citation and trend analysis for a topic, author or venue
- Feeding AI agents (MCP-friendly, structured JSON)

### Input example

```json
{
  "query": "retrieval augmented generation",
  "sources": ["openalex", "crossref", "arxiv", "pubmed"],
  "maxItems": 200,
  "sort": "relevance",
  "yearFrom": 2023,
  "openAccessOnly": false,
  "requireAbstract": true
}
```

### Output example

```json
{
  "rank": 1,
  "query": "retrieval augmented generation",
  "title": "Ragas: Automated Evaluation of Retrieval Augmented Generation",
  "authors": ["Shahul Es", "Jithin James", "Luis Espinosa-Anke", "Steven Schockaert"],
  "year": 2023,
  "venue": "arXiv",
  "doi": "10.48550/arxiv.2309.15217",
  "pdfUrl": "https://arxiv.org/pdf/2309.15217v2",
  "isOpenAccess": true,
  "citedByCount": 612,
  "abstract": "We introduce Ragas (Retrieval Augmented Generation Assessment), a framework for reference-free evaluation...",
  "sources": ["arxiv", "openalex"],
  "foundIn": 2
}
```

### Pricing

Pay per event: **$0.001 per unique paper** delivered, which is $1 per 1,000. Apify's small start event applies. Use `maxItems` and your run's maximum charge to cap spend.

### How much does it cost?

You only pay for results, never for compute time:

| You get | You pay (Starter/Bronze plan price) |
|---|---|
| 100 papers | $0.10 |
| 1,000 papers | $1.00 |
| 10,000 papers | $10.00 |

Apify's **free plan includes $5 of usage every month**, and free-plan users pay **half price** on this Actor, so that covers about **10,000 papers** at no cost. Paid plans get volume discounts (Silver -10%, Gold -20%). Set a *maximum cost per run* and the Actor stops cleanly at your limit.

### FAQ

**Is this legal and stable?** Yes. It uses only the official open APIs of OpenAlex (CC0 data), Crossref, arXiv and NCBI E-utilities, and respects their rate limits. It doesn't scrape Google Scholar.

**Why do some papers have no abstract?** Not every publisher deposits abstracts. Turn on `requireAbstract` to skip those papers.

**Citation counts differ from Google Scholar.** Scholar counts more document types. This Actor reports the higher of the OpenAlex and Crossref counts.

**Need a field or source added (Semantic Scholar, DOAJ, bioRxiv)?** Open an issue on the Actor page.

### Run it from code (API, Python, JavaScript)

```bash
curl -X POST "https://api.apify.com/v2/acts/api-co~academic-papers-search/run-sync-get-dataset-items?token=YOUR_APIFY_TOKEN" \
  -H "Content-Type: application/json" -d '{"query": "retrieval augmented generation", "maxItems": 20}'
```

```python
from apify_client import ApifyClient
client = ApifyClient("YOUR_APIFY_TOKEN")
run = client.actor("api-co/academic-papers-search").call(run_input={"query": "retrieval augmented generation", "maxItems": 20})
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item)
```

```javascript
import { ApifyClient } from 'apify-client';
const client = new ApifyClient({ token: 'YOUR_APIFY_TOKEN' });
const run = await client.actor('api-co/academic-papers-search').call({"query": "retrieval augmented generation", "maxItems": 20});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
```

It also works from **AI agents through the Apify MCP server**, and with Zapier, Make, n8n, Google Sheets and webhooks. You can schedule it in the Apify Console.

# Actor input Schema

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

Keywords, a topic or a paper title, e.g. "retrieval augmented generation" or "CRISPR off-target effects".

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

Which open scholarly databases to search. Results are merged and de-duplicated by DOI and title.

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

Stop after this many unique papers. You pay only per unique paper delivered.

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

Relevance (query-term match + cross-database agreement + citations), newest first, or most cited.

## `yearFrom` (type: `integer`):

Only papers published in or after this year.

## `yearTo` (type: `integer`):

Only papers published in or before this year.

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

Only papers with a free full text.

## `requireAbstract` (type: `boolean`):

Skip records without an abstract (useful for LLM/RAG pipelines).

## `includeAbstract` (type: `boolean`):

Add the abstract text to each result.

## `strictMatch` (type: `boolean`):

Drop loose matches: keep a paper only if most query words appear in its title or abstract. Turn off for very broad exploration.

## Actor input object example

```json
{
  "query": "retrieval augmented generation",
  "sources": [
    "openalex",
    "arxiv"
  ],
  "maxItems": 20,
  "sort": "relevance",
  "openAccessOnly": false,
  "requireAbstract": false,
  "includeAbstract": true,
  "strictMatch": true
}
```

# Actor output Schema

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

Merged, de-duplicated papers.

# 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": "retrieval augmented generation",
    "sources": [
        "openalex",
        "arxiv"
    ],
    "maxItems": 20
};

// Run the Actor and wait for it to finish
const run = await client.actor("api-co/academic-papers-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": "retrieval augmented generation",
    "sources": [
        "openalex",
        "arxiv",
    ],
    "maxItems": 20,
}

# Run the Actor and wait for it to finish
run = client.actor("api-co/academic-papers-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": "retrieval augmented generation",
  "sources": [
    "openalex",
    "arxiv"
  ],
  "maxItems": 20
}' |
apify call api-co/academic-papers-search --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,api-co/academic-papers-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/LoON3h7PbkkVWrrpZ/builds/PVU2afLJnFa8iojaQ/openapi.json