# Academic Paper Search - OpenAlex, Crossref & arXiv (`zhucl1006/academic-paper-search`) Actor Search 250M+ scholarly papers across OpenAlex, Crossref and arXiv official APIs in one run. Titles, DOIs, authors, institutions, abstracts, citation counts and open-access PDF links as clean JSON for literature reviews, RAG and research intelligence. $1 per 1,000 papers. - **URL**: https://apify.com/zhucl1006/academic-paper-search.md - **Developed by:** [leo zhu](https://apify.com/zhucl1006) (community) - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $1.00 / 1,000 paper 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 - OpenAlex, Crossref & arXiv (DOI, Abstracts, Citations) Search **scholarly papers** across **OpenAlex** (250M+ works), **Crossref** (the DOI registry) and **arXiv** (preprints) in one run and get clean JSON / CSV / Excel: title, DOI, authors, institutions, year, journal, publisher, citation count, open-access status, PDF link and the full abstract. Uses only the **official open APIs** - no login, no API key, no browser, no proxies - so 50 papers take about 2 seconds. ### Who uses it - **Researchers & students**: build a literature review list for a topic in seconds, sorted by citations or newest first. - **AI / RAG builders**: feed abstracts and open-access PDF links into an LLM, vector DB or n8n / Make / Zapier pipeline. - **R\&D, pharma & patent teams**: monitor new publications on a technology every week (`sortBy: date`, `fromYear`). - **Sales & BD to academia**: find which institutions and authors publish on your topic. - **Analysts & journalists**: measure research activity and the most-cited work on a subject. ### Input | Field | Description | |---|---| | `queries` | Topics / keywords. Each one is searched in every selected source. | | `sources` | `openalex`, `crossref`, `arxiv` (default OpenAlex + Crossref). | | `maxResultsPerSource` | 1-5000 per query per source (default 25). | | `sortBy` | `relevance`, `date` (newest first) or `citations` (most cited). | | `fromYear`, `toYear` | Publication year range (0 = no limit). | | `openAccessOnly` | Only open-access papers. | | `includeAbstract` | Abstracts (OpenAlex reconstructs them from its index; Crossref has them for many publishers; arXiv always). | | `dedupeByDoi` | Skip the same paper found in a second source (not charged). | Example: ```json { "queries": ["retrieval augmented generation", "CRISPR base editing"], "sources": ["openalex", "arxiv"], "sortBy": "citations", "fromYear": 2022, "maxResultsPerSource": 200 } ``` ### Output (one row per paper) ```json { "query": "CRISPR base editing", "source": "openalex", "title": "Unconstrained genome targeting with near-PAMless engineered CRISPR-Cas9 variants", "doi": "10.1126/science.aba8853", "url": "https://doi.org/10.1126/science.aba8853", "pdfUrl": "https://...pdf", "authors": ["Russell T. Walton", "Kathleen A. Christie", "..."], "institutions": ["Harvard University", "Massachusetts General Hospital"], "publicationYear": 2020, "publicationDate": "2020-04-17", "venue": "Science", "publisher": "American Association for the Advancement of Science", "type": "article", "citedByCount": 1230, "isOpenAccess": true, "oaStatus": "green", "topics": ["CRISPR and Genetic Engineering"], "abstract": "Manipulation of DNA by CRISPR-Cas enzymes requires ...", "openalexId": "https://openalex.org/W...", "arxivId": null } ``` A `RUN_SUMMARY` record (papers per source, duplicates, failures) is saved in the key-value store. ### Pricing (pay per event) - **$0.001 per paper saved** (`paper`) - $1 per 1,000 papers. - Tiny start fee per run (`apify-actor-start`). - Duplicates and failed source requests are not charged. Set "Maximum cost per run" to cap spend - the run stops gracefully and keeps results already saved. ### Notes - Data comes from the public OpenAlex, Crossref and arXiv APIs (polite pool, identified user agent). Not affiliated with OpenAlex / OurResearch, Crossref or arXiv / Cornell University. - Abstract and citation coverage depends on the source and publisher. Full-text PDFs are only linked, not downloaded. ### FAQ **Do I need an API key?** No. **Can I schedule a weekly alert for new papers?** Yes: `sortBy: date`, set `fromYear` to the current year, and add a schedule in Apify. **Google Scholar?** Not included - it has no official API. OpenAlex covers most of the same literature with open data. # Actor input Schema ## `queries` (type: `array`): Topics, keywords or phrases. Each query is searched in every selected source. ## `sources` (type: `array`): Which open scholarly APIs to search. arXiv is rate-limited (1 request / 3 s), so it is slower. ## `maxResultsPerSource` (type: `integer`): Max results per query per source. ## `sortBy` (type: `string`): Sort by. ## `fromYear` (type: `integer`): 0 = no limit. ## `toYear` (type: `integer`): 0 = no limit. ## `openAccessOnly` (type: `boolean`): OpenAlex: is_oa; Crossref: has a license. arXiv is always open. ## `includeAbstract` (type: `boolean`): Include abstracts. ## `dedupeByDoi` (type: `boolean`): Skip a paper already saved from another source (same DOI or title). Duplicates are not charged. ## Actor input object example ```json { "queries": [ "large language model agents" ], "sources": [ "openalex", "crossref" ], "maxResultsPerSource": 25, "sortBy": "relevance", "fromYear": 0, "toYear": 0, "openAccessOnly": false, "includeAbstract": true, "dedupeByDoi": true } ``` # Actor output Schema ## `results` (type: `string`): No description ## `runSummary` (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 '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "queries": [ "large language model agents" ] }; // Run the Actor and wait for it to finish const run = await client.actor("zhucl1006/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 '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "queries": ["large language model agents"] } # Run the Actor and wait for it to finish run = client.actor("zhucl1006/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 '{ "queries": [ "large language model agents" ] }' | apify call zhucl1006/academic-paper-search --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "type": "http", "url": "https://mcp.apify.com/?tools=fetch-actor-details,zhucl1006/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 ` 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/aExh2dMjijTXfrNYL/builds/f7dghiGwl0yaYg9eH/openapi.json