agentsvc.io

services / paper-search

dataoperational · 1 ms

Scientific Paper Search

Search scientific papers across OpenAlex (250M works, all fields), arXiv (preprints) and PubMed (biomedicine) in one call. Returns title, up to 5 authors, year, venue, DOI, landing URL, open-access PDF URL, citation count and abstract (up to 600 chars), merged and deduplicated by DOI. Params: query (required), source ('all' default, 'openalex', 'arxiv', 'pubmed'), limit (1 to 25, default 10), year_from, open_access_only (default false).

Run free trial ↗1 free call per day with the example input. Paid: $0.004 USDC, no limit.

Call it

import { wrapFetchWithPayment, x402Client } from "@x402/fetch";
import { registerExactEvmScheme } from "@x402/evm/exact/client";
import { privateKeyToAccount } from "viem/accounts";

const client = new x402Client();
registerExactEvmScheme(client, { signer: privateKeyToAccount(process.env.EVM_PRIVATE_KEY) });
const payFetch = wrapFetchWithPayment(fetch, client);

const res = await payFetch("https://agentsvc.io/api/v1/proxy/paper-search", {
  method: "POST",
  headers: { "Content-Type": "application/json" },
  body: JSON.stringify({"query":"retrieval augmented generation","limit":5}),
});
const { data, payment } = await res.json();  // payment.transaction = on-chain receipt

Input

FieldTypeDescription
query *string
sourceall | openalex | arxiv | pubmed = "all"
limitinteger = 10
year_fromintegerOnly papers published in or after this year
open_access_onlyboolean = false

Output

FieldTypeDescription
querystring
countinteger
papersarray
sourcesarray
source_errorsobject
searched_atstring

Example response (data)

{
  "query": "retrieval augmented generation",
  "count": 2,
  "papers": [
    {
      "source": "openalex",
      "title": "Active Retrieval Augmented Generation",
      "authors": [
        "Zhengbao Jiang",
        "Frank F. Xu"
      ],
      "year": 2023,
      "venue": "Conference on Empirical Methods in Natural Language Processing (EMNLP)",
      "doi": "10.18653/v1/2023.emnlp-main.495",
      "url": "https://doi.org/10.18653/v1/2023.emnlp-main.495",
      "pdf_url": "https://aclanthology.org/2023.emnlp-main.495.pdf",
      "citations": 457,
      "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 E…"
    },
    {
      "source": "arxiv",
      "title": "AR-RAG: Autoregressive Retrieval Augmentation for Image Generation",
      "authors": [
        "Jingyuan Qi",
        "Zhiyang Xu"
      ],
      "year": 2025,
      "venue": "arXiv",
      "doi": null,
      "url": "http://arxiv.org/abs/2506.06962v3",
      "pdf_url": "https://arxiv.org/pdf/2506.06962v3",
      "citations": null,
      "abstract": "We introduce Autoregressive Retrieval Augmentation (AR-RAG), a novel paradigm that enhances image generation by autoregressively incorporating knearest neigh…"
    }
  ],
  "sources": [
    "openalex",
    "arxiv"
  ],
  "source_errors": {},
  "searched_at": "2026-10-06T14:30:54.495Z"
}