Scientific Papers API

API ID 14867

Search millions of research papers by keyword, author or DOI. Returns title, authors, year, venue, abstract, citation count, DOI and an open-access PDF link when one exists, as clean JSON.

API Documentation

Endpoints

Request
Scientific Papers API - Research Paper Search, Citations. Returns up to 100 results per request as JSON.
Endpoint ID: 38700
GET https://zylalabs.com/api/14867/scientific+papers+api/38700/scientific+papers+api
INPUT PARAMETERS

Scientific Papers API — Endpoint Features

Object Description
queries Optional Keywords or phrases to search in paper titles and abstracts, one per entry, e.g. 'retrieval augmented generation'. Each query is searched separately and results are de-duplicated. Several values: comma separated.
author Optional Only papers by this author, e.g. 'Geoffrey Hinton'. Can be combined with queries, or used alone to list an author's papers.
dois Optional Look up specific papers by DOI, e.g. 10.1038/nature14539 (a link also works). Returns metadata, abstract and citation count for each.
fromYear Optional Only papers published in this year or later, e.g. 2022. Leave empty for no limit.
toYear Optional Only papers published in this year or earlier, e.g. 2025. Leave empty for no limit.
openAccessOnly Optional Return only papers that have a free full-text version. Works with OpenAlex and arXiv results (Crossref does not report open access).
sort Optional Relevance = best match first, citations = most cited first, newest = most recently published first.
source Optional Auto = OpenAlex first, falling back to Crossref and then arXiv if a source is unavailable. Or force one source.
maxItems Optional Maximum number of papers in total (you pay per paper returned).

Free test requests remaining: 3 of 3.


INPUT PARAMETERS

queries
author
dois
fromYear
toYear
openAccessOnly
sort
source
maxItems
API EXAMPLE RESPONSE
JSON
{
    "count": 5,
    "items": [
        {
            "title": "Corrective Retrieval Augmented Generation",
            "authors": [
                "Shi-Qi Yan",
                "Jia-Chen Gu",
                "Yun Zhu",
                "Zhen-Hua Ling"
            ],
            "year": 2026,
            "date": "2026-01-01",
            "type": "conference-paper",
            "doi": "10.18653/2026.findings-aacl.151",
            "doiUrl": "https://doi.org/10.18653/2026.findings-aacl.151",
            "citedBy": 717,
            "venue": null,
            "isOpenAccess": true,
            "pdfUrl": "https://aclanthology.org/2026.findings-aacl.151.pdf",
            "landingUrl": "https://doi.org/10.18653/2026.findings-aacl.151",
            "abstract": "Large language models (LLMs) inevitably exhibit hallucinations since the accuracy of generated texts cannot be secured solely by the parametric knowledge they encapsulate.Although",
            "openalexId": "https://openalex.org/W4410705984",
            "source": "openalex",
            "query": "retrieval augmented generation",
            "scrapedAt": "2026-11-12T22:57:23Z"
        },
        {
            "title": "Graph Retrieval-Augmented Generation: A Survey",
            "authors": [
                "Boci Peng",
                "Yun Zhu",
                "Yongchao Liu",
                "Xiaohe Bo",
                "Haizhou Shi",
                "Chuntao Hong",
                "Yan Zhang",
                "Siliang Tang"
            ],
            "year": 2025,
            "date": "2025-11-19",
            "type": "article",
            "doi": "10.1145/3777378",
            "doiUrl": "https://doi.org/10.1145/3777378",
            "citedBy": 715,
            "venue": "ACM Transactions on Information Systems",
            "isOpenAccess": true,
            "pdfUrl": "https://arxiv.org/pdf/2408.08921",
            "landingUrl": "https://doi.org/10.1145/3777378",
            "abstract": "Recently, Retrieval-Augmented Generation (RAG) has achieved remarkable success in addressing the challenges of Large Language Models (LLMs) without necessitating retraining. By ref",
            "openalexId": "https://openalex.org/W4416379250",
            "source": "openalex",
            "query": "retrieval augmented generation",
            "scrapedAt": "2026-11-12T22:57:24Z"
        },
        {
            "title": "Active Retrieval Augmented Generation",
            "authors": [
                "Zhengbao Jiang",
                "Frank F. Xu",
                "Luyu Gao",
                "Zhiqing Sun",
                "Qian Liu",
                "Jane Dwivedi-Yu",
                "Yiming Yang",
                "Jamie Callan",
                "Graham Neubig"
            ],
            "year": 2023,
            "date": "2023-01-01",
            "type": "conference-paper",
            "doi": "10.18653/v1/2023.emnlp-main.495",
            "doiUrl": "https://doi.org/10.18653/v1/2023.emnlp-main.495",
            "citedBy": 1043,
            "venue": "Conference on Empirical Methods in Natural Language Processing (EMNLP)",
            "isOpenAccess": true,
            "pdfUrl": "https://aclanthology.org/2023.emnlp-main.495.pdf",
            "landingUrl": "https://doi.org/10.18653/v1/2023.emnlp-main.495",
            "abstract": "Zhengbao Jiang, Frank Xu, Luyu Gao, Zhiqing Sun, Qian Liu, Jane Dwivedi-Yu, Yiming Yang, Jamie Callan, Graham Neubig. Proceedings of the 2023 Conference on Empirical Methods in Nat",
            "openalexId": "https://openalex.org/W4389519118",
            "source": "openalex",
            "query": "retrieval augmented generation",
            "scrapedAt": "2026-11-12T22:57:24Z"
        },
        {
            "title": "Evaluation of Retrieval-Augmented Generation: A Survey",
            "authors": [
                "Hao Yu",
                "Aoran Gan",
                "Kai Zhang",
                "Shiwei Tong",
                "Qi Liu",
                "Zhaofeng Liu"
            ],
            "year": 2025,
            "date": "2025-01-01",
            "type": "conference-paper",
            "doi": "10.1007/978-981-96-1024-2_8",
            "doiUrl": "https://doi.org/10.1007/978-981-96-1024-2_8",
            "citedBy": 375,
            "venue": "Communications in computer and information science",
            "isOpenAccess": true,
            "pdfUrl": "https://arxiv.org/pdf/2405.07437",
            "landingUrl": "https://doi.org/10.1007/978-981-96-1024-2_8",
            "abstract": null,
            "openalexId": "https://openalex.org/W4406771321",
            "source": "openalex",
            "query": "retrieval augmented generation",
            "scrapedAt": "2026-11-12T22:57:24Z"
        },
        {
            "title": "Benchmarking Retrieval-Augmented Generation for Medicine",
            "authors": [
                "Guangzhi Xiong",
                "Qiao Jin",
                "Zhiyong Lu",
                "Aidong Zhang"
            ],
            "year": 2024,
            "date": "2024-01-01",
            "type": "conference-paper",
            "doi": "10.18653/v1/2024.findings-acl.372",
            "doiUrl": "https://doi.org/10.18653/v1/2024.findings-acl.372",
            "citedBy": 539,
            "venue": "Findings of the Association for Computational Linguistics: ACL (Findings of ACL)",
            "isOpenAccess": true,
            "pdfUrl": "https://aclanthology.org/2024.findings-acl.372.pdf",
            "landingUrl": "https://doi.org/10.18653/v1/2024.findings-acl.372",
            "abstract": "While large language models (LLMs) have achieved state-of-the-art performance on a wide range of medical question answering (QA) tasks, they still face challenges with hallucinatio",
            "openalexId": "https://openalex.org/W4402670290",
            "source": "openalex",
            "query": "retrieval augmented generation",
            "scrapedAt": "2026-11-12T22:57:24Z"
        }
    ]
}
Scientific Papers API — CODE SNIPPETS

curl --location --request GET 'https://zylalabs.com/api/14867/scientific+papers+api/38700/scientific+papers+api?queries=retrieval augmented generation&openAccessOnly=false&sort=relevance&source=auto&maxItems=5' --header 'Authorization: Bearer YOUR_API_KEY' 


    

API Access Key & Authentication

After signing up, every developer is assigned a personal API access key, a unique combination of letters and digits provided to access to our API endpoint. To authenticate with the Scientific Papers API simply include your bearer token in the Authorization header.

Headers
Header Description
Authorization Required Should be Bearer access_key. See "Your API Access Key" above when you are subscribed.

No long-term commitment. Upgrade, downgrade, or cancel anytime.

Starts at
$10,000/Year


  • Custom Volume
  • Custom Rate Limit
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  • Real-Time API Monitoring

Overview

Search millions of research papers by keyword, author or DOI. Returns title, authors, year, venue, abstract, citation count, DOI and an open-access PDF link when one exists, as clean JSON.

Scientific Papers API FAQs

Search millions of research papers by keyword, author or DOI. Returns title, authors, year, venue, abstract, citation count, DOI and an open-access PDF link when one exists, as clean JSON.

Up to 100 results per request. Set it with the maxItems parameter.

Every request collects live data from the public source. Identical requests may be served from a cache for up to 6 hours.

List parameters accept several values separated by commas, for example: london,paris.

General FAQs

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