# OpenAlex Work Search: Papers by Keyword, Author or Institution (`accountable_eel/openalex-work-search-lookup`) Actor

Search scholarly papers by keyword, author, or institution via OpenAlex. Fast metadata export: DOIs, citations, authors with ORCID, abstracts, and OA links.

- **URL**: https://apify.com/accountable_eel/openalex-work-search-lookup.md
- **Developed by:** [Adriana Voss](https://apify.com/accountable_eel) (community)
- **Categories:** Developer tools, Education
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $0.75 / 1,000 work returneds

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

## OpenAlex Work Search: Papers by Keyword, Author or Institution

**Search scholarly papers by keyword, topic, author, or institution with metadata and citations from OpenAlex.**

Academic researchers, bibliometricians, science intelligence teams, and R\&D analysts use this actor to discover research literature, trace author publications, map institutional research output, and extract citation metrics without rate-limit headaches or manual exports. Retrieve DOIs, open-access full-text links, author affiliations with ORCID, publication dates, and reconstructed abstracts. You pay only for works actually returned; misses and empty queries are free.

### How to use

1. **In the Apify Console.** Open the actor page and click **Start** — the `queries` field is already pre-filled with a working example. Results land in the run's dataset as soon as each item is found.
2. **Via the API.** Call it directly with a POST request — no Console needed once you have an API token:
   ```bash
   curl "https://api.apify.com/v2/acts/accountable_eel~openalex-work-search-lookup/run-sync-get-dataset-items?token=<YOUR_TOKEN>" \
     -X POST \
     -H "Content-Type: application/json" \
     -d '{"queries":["graph neural networks"]}'
   ```
3. **On a schedule.** Save this actor as an Apify **Task** with the input you want, then add a **Schedule** (hourly, daily, weekly) so it runs on its own — no server of your own required.

### Input

```json
{
  "queries": [
    "graph neural networks"
  ]
}
```

One search per line. Enter keywords (e.g. "graph neural networks"), OpenAlex author IDs (e.g. "A5057871229"), ORCIDs, or institution IDs (e.g. "I4210132348"). Accepted formats: graph neural networks, A5057871229, 0000-00-02-1963-8755, I4210132348, author:Yann LeCun, institution:Harvard University.

You can search by keywords or phrases, or filter directly by author ID, ORCID, institution ID, or ROR.

### Output

One row per work returned, for example:

| query | found | status | searchQuery | workCount | totalResults | works | openalexId | doi | title | publicationYear | publicationDate | type | venue | citedByCount | isOpenAccess | oaStatus | oaUrl | pdfUrl | landingPageUrl | authorNames | authorCount | primaryInstitution | institutions | authors | primaryTopic | concepts | abstract | sourceUrl | scrapedAt |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| graph neural networks | true | OK | <search query searched> | <works returned for this search> | <total estimated works matching query in openalex> | \<all works returned (full array)> | <openalex work id> | <doi url> | <paper title> | <publication year> | <publication date> | <work type> | \<venue / journal name> | <citations count> | \<is open access?> | <open access status> | <open access url> | <direct pdf url> | <landing page url> | <author names> | <total authors count> | <first author institution> | <affiliated institutions> | <full authorships array> | <primary topic domain> | \<concepts / key topics> | <reconstructed abstract text> | <source url> | 1970-01-01T00:00:00.000Z |

A query that returns no works yields a single row with `"found": false` and is never charged. Missing values are `null`, never `0` or empty strings.

### Pricing

- **Work returned**: $1.50 per 1,000 works ($1.00 per 1,000 on Gold)

Plus a $0.002 start fee per run. Charged per work returned — misses (`found:false`) are free.

### Example runs

Paste any of these into the Input tab and press Start. All three run keyless with no login or setup required.

**Search papers by topic or keyword (default).**

```json
{
  "queries": ["graph neural networks"],
  "maxItems": 25
}
```

**Find works by an author using their OpenAlex ID or ORCID.**

```json
{
  "queries": ["A5057871229"],
  "maxItems": 20
}
```

**Recent open-access papers from an institution.**

```json
{
  "queries": ["I4210132348"],
  "openAccessOnly": true,
  "publicationYear": "2024",
  "maxItems": 20
}
```

### Use it from Clay, n8n, Make, or an AI agent

This actor runs synchronously over plain HTTP — call it directly from a script, a workflow tool, or an AI agent, no Apify Console needed once you have an API token.

```bash
curl "https://api.apify.com/v2/acts/accountable_eel~openalex-work-search-lookup/run-sync-get-dataset-items?token=<YOUR_TOKEN>" \
  -X POST \
  -H "Content-Type: application/json" \
  -d '{"queries":["graph neural networks"]}'
```

**n8n.** Add an HTTP Request node: Method `POST`, URL `https://api.apify.com/v2/acts/accountable_eel~openalex-work-search-lookup/run-sync-get-dataset-items?token=<YOUR_TOKEN>`, Body Content Type `JSON`, JSON Body `{"queries":["graph neural networks"]}` (swap in an expression from an earlier node for a real value).

**Clay.** Add an "HTTP API" column: Method `POST`, URL `https://api.apify.com/v2/acts/accountable_eel~openalex-work-search-lookup/run-sync-get-dataset-items?token=<YOUR_TOKEN>`, Body `{"queries":["{{query}}"]}`, mapping the row's query into the `queries` array.

**MCP.** In Claude, Cursor, or any MCP client with the Apify MCP server, ask for "openalex scraper" — the agent will find and run this actor.

# Actor input Schema

## `queries` (type: `array`):

One search per line. Enter keywords (e.g. "graph neural networks"), OpenAlex author IDs (e.g. "A5057871229"), ORCIDs, or institution IDs (e.g. "I4210132348"). Accepted formats: graph neural networks, A5057871229, 0000-00-02-1963-8755, I4210132348, author:Yann LeCun, institution:Harvard University. You're only charged for the ones we actually find — a miss costs nothing.

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

Maximum works to return per search query. Capped at 200 for fast single-hop responses. Default 25.

## `testRun` (type: `boolean`):

Turn this on to test your input on a small sample before running the full list. Turn it off to process everything.

## `onlyFound` (type: `boolean`):

Only keep rows where something was actually found. Misses are always free, whether or not you show them here.

## `includeKeywords` (type: `array`):

Optional. Only keep results that mention at least one of these words (e.g. a job title, a city, a product name). Leave empty to keep everything.

## `excludeKeywords` (type: `array`):

Optional. Drop any result that mentions one of these words. Leave empty to skip nothing.

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

Optional. Stop the run once this many results have been found — useful for a quick, cheap sample. Leave blank for no limit.

## `publicationYear` (type: `string`):

Optional publication year filter (e.g. "2024", "2020-2024", or ">2020"). Leave empty for all years.

## `author` (type: `string`):

Optional author filter: author name, OpenAlex author ID (A...), or ORCID. Narrow results to works by this author.

## `institution` (type: `string`):

Optional institution filter: institution name, OpenAlex institution ID (I...), or ROR. Narrow results to works affiliated with this institution.

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

When enabled, restricts results to open-access papers only.

## `sortBy` (type: `string`):

Sort order for search results.

## `columns` (type: `array`):

Choose which pieces of information to include in each result row. All are included by default.

## `expandRows` (type: `boolean`):

When on, each work found gets its own row instead of being grouped under its query. You're still only charged once per query, no matter how many rows it produces.

## `maxConcurrency` (type: `integer`):

Parallel requests. Keep conservative — this target has no browser fallback, so getting blocked costs more than slow-and-steady.

## `proxyConfiguration` (type: `object`):

Apify Proxy config. This target is a free public API that needs no proxy — defaults to off, but you can turn one on if you prefer.

## Actor input object example

```json
{
  "queries": [
    "graph neural networks"
  ],
  "maxItems": 25,
  "testRun": false,
  "onlyFound": false,
  "includeKeywords": [],
  "excludeKeywords": [],
  "publicationYear": "",
  "author": "",
  "institution": "",
  "openAccessOnly": false,
  "sortBy": "relevance",
  "columns": [
    "searchQuery",
    "workCount",
    "totalResults",
    "works",
    "openalexId",
    "doi",
    "title",
    "publicationYear",
    "publicationDate",
    "type",
    "venue",
    "citedByCount",
    "isOpenAccess",
    "oaStatus",
    "oaUrl",
    "pdfUrl",
    "landingPageUrl",
    "authorNames",
    "authorCount",
    "primaryInstitution",
    "institutions",
    "authors",
    "primaryTopic",
    "concepts",
    "abstract",
    "sourceUrl"
  ],
  "expandRows": true,
  "maxConcurrency": 5,
  "proxyConfiguration": {
    "useApifyProxy": false
  }
}
```

# 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 = {
    "queries": [
        "graph neural networks"
    ],
    "includeKeywords": [],
    "excludeKeywords": []
};

// Run the Actor and wait for it to finish
const run = await client.actor("accountable_eel/openalex-work-search-lookup").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 = {
    "queries": ["graph neural networks"],
    "includeKeywords": [],
    "excludeKeywords": [],
}

# Run the Actor and wait for it to finish
run = client.actor("accountable_eel/openalex-work-search-lookup").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 '{
  "queries": [
    "graph neural networks"
  ],
  "includeKeywords": [],
  "excludeKeywords": []
}' |
apify call accountable_eel/openalex-work-search-lookup --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,accountable_eel/openalex-work-search-lookup"
        }
    }
}
```

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/Vvj788t4SrqdRDC1x/builds/hJ6ti8mTxbld3i8Rn/openapi.json