All Academic Papers Search

All Academic Papers Search is an academic paper search API that queries 12 scholarly indexes in a single request and returns one normalized row per paper. A work that more than one index carries is folded into a single row, so a twelve-index search gives you a reading list rather than twelve overlapping result sets.

• Twelve indexes, one query. OpenAlex, Crossref, PubMed, DOAJ, arXiv, Europe PMC, OpenAIRE, CORE, DataCite, OpenReview, Semantic Scholar and CQVIP each receive the same search string and each is paged until your count is met.
• 20 fields per row, including DOI, title, abstract, author list, venue, publication year and date, citation count, open-access flag, PDF link, work type, language, licence, subject terms and author affiliations.
• One row per work across all twelve. The DOI is the identity key; where an index publishes none, the normalized title plus year is, so a preprint carried by arXiv and OpenReview still collapses into one row.
• No account and no API key. Every index this Actor reads answers a public endpoint, so there is nothing to register for and nothing to rotate.

max_results is a per-index budget. Asking for one paper from one index costs $0.002 for the row plus $0.01 for the Actor Start — $0.012 in total on the FREE tier. Leaving sources empty runs all twelve, so the prefilled example returned 11 rows for $0.032, and max_results: 200 across twelve indexes returned 2,116 rows for $4.24.

Why Choose All Academic Papers Search

• One request instead of twelve. Writing a literature search against twelve indexes separately means twelve response shapes, twelve paging parameters and twelve field vocabularies — including one that answers with a hydration payload rather than JSON. This Actor sends one query and returns one flat table, so a downstream step reads title, doi and citations regardless of which index answered.
• Cross-index folding on two keys. The DOI is the primary key, lowercased and stripped of its https://doi.org/ prefix before comparison. Where an index publishes no DOI — arXiv preprints, OpenReview submissions, OpenAIRE repository deposits — the fold is the normalized title plus the publication year, so a title differing only in case, punctuation or accents still matches.
• Coverage that reaches past English-language journals. DataCite adds datasets and theses with DOIs, OpenReview adds conference submissions and their venue decisions, CORE adds repository full text, and CQVIP adds the Chinese-language journal record with its own author affiliations on 84% of rows.
• Records arrive complete, whatever the index publishes. A PubMed search answers with identifiers, so identifiers are resolved into full records and their MeSH descriptors and author keywords before publication; OpenAlex ships each abstract as a word-to-position map, and that map is read back into the sentence it encodes. Both fields land in the same columns as everyone else's.
• Open access is a fact on every row. Rows carry is_open, license and pdf_url as the index published them, so a reading list narrows to what a reader can open with one filter on the Dataset.

Quick Start Guide

1. Configure

Open the Actor's input form. It arrives prefilled with a working example: the keyword CRISPR gene editing, max_results of 1, and all twelve indexes selected. Replace keyword with your own search string and raise max_results to the number of papers you want back from each index.

2. Run

Press ▷ Start. The twelve indexes are queried in parallel and each is paged until your max_results is satisfied or that index runs out of matches. The opening status line names the index count and the per-index budget, and the closing lines name any index that did not answer, so a partial run is never silent.

3. Collect

Results land in the run's default dataset as one row per paper. Export as JSON, CSV, Excel or XML from the Storage tab, or read them straight from the dataset endpoint with your Apify token.

Input Parameters

All Academic Papers Search takes three inputs, two of them required, and the form arrives prefilled with a runnable example so a first run needs no configuration at all.

Parameter	Type	Required	Description	Example
keyword	string	Yes	Words, a phrase, an author name or a DOI. Every index you select receives this same query.	CRISPR gene editing
max_results	integer	Yes	Papers to publish from each index you select. Minimum 1, no upper limit.	25
sources	array of enum	No	Which indexes to query. The enum carries 12 values; leave it empty and all twelve are queried.	["openalex","pubmed","arxiv"]

sources selects which indexes run; it is applied before the request rather than as a filter each index has to honour, so every value in the enum is served by the index it names. What each index contributes, measured on 462 rows from a single twelve-index run on 2026-09-07:

Index	Rows	doi	abstract	citations	is_open	pdf_url	affiliations
OpenAlex	40	98%	68%	100%	100%	72%	78%
Crossref	40	100%	30%	100%	—	5%	—
PubMed	40	100%	95%	42%	—	—	—
DOAJ	40	95%	98%	—	100%	5%	—
arXiv	40	20%	100%	—	100%	100%	—
Europe PMC	40	100%	82%	100%	100%	32%	80%
OpenAIRE	39	—	97%	100%	54%	—	—
CORE	24	62%	96%	100%	62%	62%	—
DataCite	40	100%	92%	100%	—	—	—
OpenReview	29	—	90%	—	100%	100%	—
Semantic Scholar	40	88%	65%	100%	30%	30%	—
CQVIP	50	76%	98%	100%	98%	—	84%

A dash marks a column that index publishes nothing into, so the value is a fact about the source rather than a gap in the row. Read the columns rather than a total: citations is filled on 71% of all rows because seven indexes publish a count and five publish none.

The scenario used throughout this page:

{
  "keyword": "CRISPR gene editing",
  "max_results": 25,
  "sources": ["openalex", "crossref", "pubmed", "doaj", "arxiv", "europe_pmc", "openaire", "core", "datacite", "openreview", "semantic_scholar", "cqvip"]
}

Output Data Schema

One row is one paper, carrying 20 fields.

Field	Type	Description
title	string	Title as the index published it
source	string	Which of the twelve indexes supplied this row
work_url	string	The record's own address on that index
doi	string	The DOI, and the first key papers are folded on
pdf_url	string	Direct link to the open-access file
published_year	integer	Publication year
published_date	string	Publication date as YYYY-MM-DD
citations	integer	Citation count as that index counts it
is_open	boolean	Whether the index records the paper as openly readable
venue	string	Journal, conference or publisher
work_type	string	Article, review, preprint, dataset, chapter and so on
language	string	Language code or name
license	string	Licence the index records
authors	array	Author list as the index published it
category	array	Subject, topic or keyword terms
affiliations	array	Institution names the index published for the authors
abstract	string	Abstract as the index published it
source_id	string	That index's own identifier for the record
processor	string	The Actor that produced the row
processed_at	string	UTC timestamp of the run

A field the index did not publish is left empty rather than filled with unknown or 0, so an empty column means the source was silent, never that the value is zero.

{
  "source": "openalex",
  "source_id": "https://openalex.org/W2556159813",
  "doi": "10.1038/nature.2016.20988",
  "title": "CRISPR gene-editing tested in a person for the first time",
  "abstract": null,
  "published_year": 2016,
  "published_date": "2016-11-15",
  "authors": ["David Cyranoski"],
  "venue": "Nature",
  "citations": 443,
  "is_open": false,
  "pdf_url": null,
  "work_url": "https://openalex.org/W2556159813",
  "work_type": "article",
  "language": "en",
  "license": null,
  "category": ["Biotechnology and Related Fields", "CRISPR", "Genome editing", "Biology"],
  "affiliations": null,
  "processor": "https://apify.com/",
  "processed_at": "2026-09-08T02:37:51+00:00"
}

The category array is truncated in this sample for readability; the real row carries six terms, and a PubMed row carries up to sixteen. Export formats are JSON, CSV, Excel, XML and RSS from the Storage tab, and the same dataset is readable through the API.

Integration Examples

Every example on this page runs the same search — CRISPR gene editing, 25 papers from each of the twelve indexes — so you can paste any one of them and compare the output with the sample row. The Actor's name form, agentx/all-academic-papers-search, goes wherever an Actor ID is expected.

Actor ID

CxbecA1Ig5sUnEiDo

CxbecA1Ig5sUnEiDo is the Actor's API ID. The agentx/all-academic-papers-search name form resolves through the same API routes and works anywhere the ID does.

HTTP

Start the run, poll it until status is SUCCEEDED, then read the Dataset; the start call answers at once with data.id and data.defaultDatasetId, and an ACTOR.RUN.SUCCEEDED webhook replaces the polling step in production.

# 1. Start the run
curl -X POST "https://api.apify.com/v2/acts/agentx~all-academic-papers-search/runs?token=APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"keyword":"CRISPR gene editing","max_results":25}'

# 2. Poll until data.status is SUCCEEDED
curl "https://api.apify.com/v2/actor-runs/RUN_ID?token=APIFY_TOKEN"

# 3. Read the papers
curl "https://api.apify.com/v2/datasets/DATASET_ID/items?token=APIFY_TOKEN"

Leaving sources out of the body runs all twelve indexes, which is what the empty default means.

Python

from apify_client import ApifyClient

client = ApifyClient("APIFY_TOKEN")
run = client.actor("agentx/all-academic-papers-search").call(run_input={
    "keyword": "CRISPR gene editing",
    "max_results": 25,
    "sources": ["openalex", "crossref", "pubmed", "arxiv"],
})
for paper in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(paper["source"], paper["doi"], paper["title"], paper["citations"])

JavaScript

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'APIFY_TOKEN' });
const run = await client.actor('agentx/all-academic-papers-search').call({
    keyword: 'CRISPR gene editing',
    max_results: 25,
    sources: ['openalex', 'crossref', 'pubmed', 'arxiv'],
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items.map((p) => `${p.source} — ${p.doi} — ${p.title}`));

Make.com

1. Add the Apify → Run an Actor module to your scenario.
2. Connect your Apify account with an API token from the Apify Console.
3. Paste CxbecA1Ig5sUnEiDo into the Actor field.
4. Set the input JSON to {"keyword":"CRISPR gene editing","max_results":25}.
5. Chain Apify → Get Dataset Items to pull the 20-field rows into the next module.

The Make.com integration is documented in the Apify Make integration guide.

n8n

1. Add the Apify node and choose the Run Actor operation.
2. Set the Actor to CxbecA1Ig5sUnEiDo.
3. Paste the same input JSON: {"keyword":"CRISPR gene editing","max_results":25}.
4. Add a second Apify node with Get Dataset Items to receive one item per paper.

The node reference lives at n8n.io/integrations/apify.

MCP

Apify exposes every Actor as a Model Context Protocol tool, so an assistant can call this search directly. Point your MCP client at https://mcp.apify.com with your Apify token, then ask it to call agentx/all-academic-papers-search with {"keyword":"CRISPR gene editing","max_results":25}. Setup is documented in the Apify MCP server guide.

Pricing

A paper costs $0.002 on the FREE tier and the Actor Start costs $0.01, so the smallest run — one paper from one index — totals $0.012, and a twelve-index sweep at max_results: 25 returns roughly 270 rows for about $0.55.

Event	FREE	BRONZE	SILVER	GOLD	PLATINUM	DIAMOND
Actor Start (per run)	$0.01	$0.01	$0.01	$0.01	$0.01	$0.01
Result (per paper)	$0.00200	$0.00180	$0.00160	$0.00150	$0.00150	$0.00150

max_results is counted per index, and every row that reaches the dataset is charged. Twelve indexes at 25 is up to 300 rows before folding, so the number you type is multiplied by the number of indexes you selected. Narrow sources to the indexes your field actually uses when you want the count you typed: ["pubmed"] at 25 is 25 rows and $0.06. Measured on 2026-09-07, all twelve selected: max_results: 10 returned 109 rows and max_results: 200 returned 2,116.

The Actor Start event is charged once per gigabyte of run memory with a minimum of one event, and this Actor is sized well under a gigabyte, so a run is charged exactly one Actor Start whatever it returns. Only rows that reach the dataset are charged; an index that fails to answer costs nothing, and a run that returns no papers is charged the Actor Start alone.

Batching is where the price works for you: the Actor Start is a per-run cost, so one run asking for 500 papers from a single index costs $1.01 while 500 single-paper runs cost $6.00 for the same data. Prices can change; the current figures are always on the Actor pricing page.

Use Cases

• Systematic literature screening. Run the same keyword across all twelve indexes with max_results set high, export to CSV, and use is_open, published_year and venue to triage what to read first. The cross-index fold means your screening sheet has one row per work to reconcile rather than twelve.
• Building a RAG corpus of citable sources. Filter rows on is_open: true and pull pdf_url, so every document in the index is one a reader can open and verify. license tells you which files you may redistribute, and arXiv and OpenReview supply a PDF link on every row they return.
• Citation-count monitoring. Schedule a weekly run with a fixed keyword and compare citations between runs to see which papers in a topic are accelerating. source keeps the comparison honest, because each index counts citations over its own reference corpus.
• Reaching the Chinese-language record. Select cqvip beside the English-language indexes and the same query returns Chinese journal articles with their own titles, venues, subject terms and author affiliations, in the same 20 columns as everything else.
• Filling gaps in a reference list. Pass a DOI as the keyword and the indexes that carry the record return it, so a half-remembered reference resolves into a full title, author list and venue.
• Feeding an agent that must cite. The MCP path lets an assistant search, then hand doi, work_url and pdf_url straight into an answer, so the citation is a real record rather than a generated string.

Alternatives

Search the indexes by hand. OpenAlex, Crossref, PubMed, arXiv and the rest all have public web interfaces and they are genuinely good for one-off questions. They stop being practical the moment you need the same query run across twelve of them, folded to one row per work, and delivered as a table you can filter — that is twelve browser tabs and a spreadsheet merge every time.

Call the indexes' own APIs. Most of these indexes publish a free, documented, key-free API and they are the authority on their own data — OpenAlex documents its works endpoint at docs.openalex.org and NCBI documents the PubMed E-utilities at ncbi.nlm.nih.gov. Going direct is better when you want one index's own facets beside the search, such as institution filters or citation graphs. It is more work when you want all twelve together, because you then own twelve paging schemes, twelve field vocabularies and the folding logic.

A general scholarly search suite. Reference managers and discovery platforms cover full-text search and PDF management alongside the metadata. They also cost a subscription, hold your library inside their product, and rarely give you a clean per-row export keyed on DOI.

Limits and Troubleshooting

• A run returns fewer papers than max_results × the index count → the indexes ran out of matches, or a work was carried by several. Each index is paged until your count is met or it stops returning new records, and works carried by more than one index fold into a single row. A narrow query simply has fewer papers than you asked for; broaden the keyword rather than raising the count.
• A CQVIP run stops at 20 rows → that is the route's whole page. The CQVIP search page carries exactly twenty records and serves the same twenty for every page parameter, so twenty is what one run of that index returns however high max_results goes. The other eleven indexes page normally.
• A CORE run returns fewer rows than the others → CORE's anonymous tier limits by request count. Measured on 2026-09-07, CORE returned 24 rows in a run where the others returned 40 each, and the same request succeeded minutes later. Re-run to fill the gap, or leave core out of sources when you need a predictable count.
• citations is empty on some rows → that index publishes no count. arXiv, DOAJ and OpenReview publish none; the field is left empty rather than filled, so an absent value is a fact about the source. Compare counts within one source, because each index counts over its own corpus.
• A run reports that an index did not answer → the index was briefly unavailable. What the other indexes returned is still delivered and charged; the missing one is named in the log and costs nothing. Re-run to fill the gap.

If something looks wrong, open an issue on the Actor's Issues tab with the run ID, the exact input JSON, and one example row that shows the problem.

Trust and Reliability

Runs execute on Apify's managed platform, with run history, logs, dataset storage and scheduling handled by the platform rather than by this Actor. Billing is pay-per-event: one Actor Start per run plus one Result event per row that reaches the dataset, so a failed lookup never appears on the invoice.

The Dataset contract is fixed at 20 fields and every one of them is documented on this page. Values are published exactly as the index returned them — the transformations are the cross-index fold, the reconstruction of OpenAlex abstracts from the word-position map that index publishes instead of a text field, and the resolution of PubMed identifiers into the full records they point at. Nothing is inferred, scored or filled in when a source is silent.

Legal and Compliance

Data scope. This Actor reads publicly available bibliographic metadata from twelve open scholarly indexes. It returns records about published works — titles, identifiers, venues, counts and links — and the pdf_url field is the link the index itself publishes, so the full text stays with the publisher or repository that hosts it.

Privacy. Author names and affiliations appear in these records because the indexes publish them as part of the scholarly record. If you process rows about identifiable people, you remain the controller of that data and are responsible for the lawful basis, retention and rights handling in your jurisdiction.

Platform terms. Use of this Actor is subject to the Apify Terms of Service and to each index's own terms. Do not use the Actor for harassment, surveillance, doxxing, impersonation, or bulk redistribution of a source's database in a way its licence forbids; several of these indexes publish under open licences that require attribution, and the license field is there so you can honour it.

Frequently Asked Questions

Can I search OpenAlex, Crossref, PubMed and DOAJ in one query?

Yes, along with eight more. One keyword string is sent to every index you select — OpenAlex, Crossref, PubMed, DOAJ, arXiv, Europe PMC, OpenAIRE, CORE, DataCite, OpenReview, Semantic Scholar and CQVIP — each is paged independently until your max_results is satisfied, and the results are folded to one row per work before they reach the dataset. The source field on each row records which index supplied it.

Is there an academic paper search API without an API key?

Yes. Every index this Actor reads answers a public endpoint, so the input carries no key, no OAuth step and no credential. You need only an Apify token to start the run itself.

Is there a free academic paper API?

The indexes themselves are free to query and are linked in the Alternatives section of this page. What you pay for here is the twelve-index fan-out, the cross-index fold, the paging, and the delivery of a single flat table — $0.002 per paper on the FREE tier, with a $0.01 Actor Start per run.

Can I schedule runs to monitor a research topic over time?

Yes. Use Apify's scheduler to run the same input on a cron expression — weekly is a common choice for a moving topic. Each run writes its own dataset, so comparing citations and doi sets between runs shows which papers are new and which are gaining attention.

How many rows will a run return, and what will it cost?

max_results is a per-index budget, so the row count is roughly your number times the number of indexes you selected, less the works that fold together. Measured across all twelve on 2026-09-07: max_results: 10 returned 109 rows and max_results: 200 returned 2,116, at $0.23 and $4.24 on the FREE tier. Select fewer indexes in sources when you want the count you typed.

How do duplicates get folded?

The DOI is the primary key, lowercased and stripped of any https://doi.org/ prefix, so the same paper from three indexes collapses into one row. Where an index publishes no DOI, the key is the normalized title plus the publication year — case, punctuation and accents removed — which is what lets an arXiv preprint and its OpenReview submission meet.

Which index should I select for Chinese-language research?

Select cqvip. It returns the Chinese journal record with its own title, venue, subject terms and, on 84% of the rows measured on 2026-09-07, the author affiliations — in the same 20 columns as every other index, so it merges into the same table.

Can I search by author or by DOI instead of by keyword?

Yes. The keyword field is free text and is passed to each index's own search endpoint, so an author name works as a query and so does a bare DOI. Precision then depends on each index's own matching rules rather than on anything this Actor adds.

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• All Property Scraper — Property listings scraper for 38 marketplaces across 23 countries
• Homes Property Scraper — Homes.com property scraper for United States sale and rental searches
• MagicBricks Property Scraper — MagicBricks property scraper for Indian sale and rental searches
• NoBroker Property Scraper — NoBroker property scraper for Indian sale and rental searches
• Property24 Property Scraper — Property24 property scraper for sale and rental searches across nine African markets
• Realtor Property Scraper — Realtor.com property scraper for United States sale, rental, and sold searches
• Zillow Property Scraper — Zillow property scraper for United States and Canada sale, rental, and sold searches

Developer and Data Tools

• All Image Scraper — image search downloader for Google, Bing, Yahoo, Brave, and Yandex, one engine or several per run: a keyword…
• Weather Forecast API — weather forecast api that resolves one place into 54-field daily forecast or historical rows with hourly…

Support and Community

Ask about index coverage, cross-index folding, open-access flags or the per-index result budget in the AgentX community on Telegram; for a reproducible bug, open an Issue with the run ID and the exact input.

AgentX is an Arcyton brand — arcyton.com.

Last Updated: September 7, 2026