lens-scholarly-api

name	lens-scholarly-api
description	Search 300M+ scholarly and patent records via the Lens.org API
metadata	{"openclaw":{"emoji":"🔎","category":"literature","subcategory":"search","keywords":["Lens.org","patent search","scholarly search","citation linking","innovation","prior art"],"source":"https://www.lens.org/"}}

Lens.org Scholarly and Patent API

Overview

Lens.org provides unified access to 300M+ scholarly articles and 150M+ patent records with cross-linkage between them. Uniquely, Lens connects academic research to patent citations, enabling innovation tracking and prior art discovery. The API offers full-text search, citation analysis, and patent-paper linkage. Free for non-commercial use with registration (up to 1,000 requests/day).

Authentication

# Register at https://www.lens.org/lens/user/subscriptions
# API token provided in your account settings
# Include in header: Authorization: Bearer YOUR_TOKEN

API Endpoints

Scholarly Search

# POST-based search
curl -X POST "https://api.lens.org/scholarly/search" \
  -H "Authorization: Bearer $LENS_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "query": {
      "match": {"field_of_study": "machine learning"}
    },
    "size": 20,
    "from": 0,
    "sort": [{"year_published": "desc"}]
  }'

# Boolean query
curl -X POST "https://api.lens.org/scholarly/search" \
  -H "Authorization: Bearer $LENS_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "query": {
      "bool": {
        "must": [
          {"match": {"title": "transformer"}},
          {"range": {"year_published": {"gte": 2023}}}
        ],
        "should": [
          {"match": {"abstract": "attention mechanism"}}
        ]
      }
    },
    "size": 25
  }'

Patent Search

curl -X POST "https://api.lens.org/patent/search" \
  -H "Authorization: Bearer $LENS_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "query": {
      "bool": {
        "must": [
          {"match": {"title": "neural network"}},
          {"term": {"jurisdiction": "US"}}
        ]
      }
    },
    "size": 20
  }'

Scholarly Fields

Field	Description	Type
title	Article title	text
abstract	Abstract text	text
author.display_name	Author name	text
year_published	Publication year	integer
source.title	Journal/venue name	text
field_of_study	Research field	text
doi	DOI identifier	keyword
pmid	PubMed ID	keyword
citing_patent_count	Patents citing this work	integer
scholarly_citations_count	Citation count	integer
open_access.is_oa	Open access status	boolean

Python Usage

import os
import requests

TOKEN = os.environ["LENS_API_TOKEN"]
BASE_URL = "https://api.lens.org"
HEADERS = {
    "Authorization": f"Bearer {TOKEN}",
    "Content-Type": "application/json",
}


def search_scholarly(query: str, size: int = 20,
                     min_year: int = None,
                     fields: list = None) -> list:
    """Search Lens scholarly records."""
    must_clauses = [{"match": {"title": query}}]
    if min_year:
        must_clauses.append(
            {"range": {"year_published": {"gte": min_year}}}
        )

    body = {
        "query": {"bool": {"must": must_clauses}},
        "size": size,
        "sort": [{"scholarly_citations_count": "desc"}],
    }
    if fields:
        body["include"] = fields

    resp = requests.post(
        f"{BASE_URL}/scholarly/search",
        headers=HEADERS,
        json=body,
    )
    resp.raise_for_status()
    data = resp.json()

    results = []
    for doc in data.get("data", []):
        results.append({
            "title": doc.get("title"),
            "authors": [a.get("display_name", )
                         a  doc.get(, [])[:]],
            : doc.get(),
            : doc.get(, {}).get(),
            : doc.get(),
            : doc.get(, ),
            : doc.get(, ),
            : doc.get(, {}).get(),
        })
     results


 () -> :
    
    body = {
        : {
            : {
                : [
                    {: {: topic}},
                    {: {: {: min_patents}}},
                ]
            }
        },
        : ,
        : [{: }],
    }

    resp = requests.post(
        ,
        headers=HEADERS,
        json=body,
    )
    resp.raise_for_status()
     resp.json().get(, [])



papers = search_scholarly(, size=, min_year=)
 p  papers:
    ()
    (
          )


patent_cited = find_patent_cited_papers()
 doc  patent_cited[:]:
    ()

Unique Features

• Patent-paper linkage: Discover which research is cited in patents
• Unified search: Scholarly + patent in one platform
• Innovation metrics: Track technology transfer from academia to industry
• Prior art search: Find relevant literature for patent applications

Rate Limits

Tier	Daily requests	Results per query
Free (non-commercial)	1,000	1,000
Institutional	10,000+	10,000

References

• Lens.org
• Lens API Documentation
• Lens Scholarly Search