How to Build a Literature Review Tool with Google Scholar API

Why Automate Literature Reviews?

Systematic literature reviews can require screening hundreds or thousands of papers. Manual Google Scholar searches don't scale. A literature review tool powered by a Google Scholar API lets researchers define queries, collect results programmatically, deduplicate, and export — cutting weeks of work to hours.

Architecture Overview

Query builder — user defines keywords, authors, date ranges

API fetcher — paginate through Scholar results via BitLore

Deduplication — merge by DOI, title similarity, or URL

Storage — SQLite, PostgreSQL, or JSON files

Export — CSV, BibTeX, or RIS for reference managers

Core Fetch Logic

Python

import requests
from urllib.parse import urlparse

API_KEY = "YOUR_API_KEY"

def fetch_review_papers(queries: list[str], pages_per_query: int = 3) -> list[dict]:
    seen_urls = set()
    collected = []

    for query in queries:
        for page in range(1, pages_per_query + 1):
            resp = requests.get("https://api.bitlore.in/search", params={
                "search_engine": "google_scholar",
                "q": query,
                "page": page,
                "api_key": API_KEY,
            })
            resp.raise_for_status()
            results = resp.json().get("data", {}).get("results", [])

            for paper in results:
                url = paper.get("link", "")
                if url and url not in seen_urls:
                    seen_urls.add(url)
                    collected.append({
                        "query": query,
                        "title": paper.get("title"),
                        "link": url,
                        "snippet": paper.get("snippet"),
                    })
            if not results:
                break
    return collected

Writing Effective Search Queries

For systematic reviews, combine broad and narrow queries:

•

("machine learning" OR "deep learning") AND healthcare

•

author:"Geoffrey Hinton" transformer — author-focused sweep

•

Run multiple query variants and deduplicate — catches papers a single query misses

Deduplication Strategies

Method	Accuracy	Complexity
Exact URL match	High for same source	Low
Normalized title (lowercase, strip punctuation)	Medium-high	Low
Fuzzy title match (Levenshtein > 0.9)	High	Medium
DOI matching (if available in metadata)	Very high	Medium

Combine with PubMed for Medical Reviews

Biomedical systematic reviews should query both Google Scholar and PubMed. BitLore's unified endpoint supports both with one API key:

Python

def fetch_multi_source(query: str, api_key: str) -> dict:
    base = "https://api.bitlore.in/search"
    scholar = requests.get(base, params={
        "search_engine": "google_scholar", "q": query, "api_key": api_key
    }).json()
    pubmed = requests.get(base, params={
        "search_engine": "pubmed", "q": query, "api_key": api_key
    }).json()
    return {"scholar": scholar, "pubmed": pubmed}

See our PubMed integration guide for medical-specific tips.

Production Considerations

→

Rate limits: Batch queries with delays; use BitLore's rate limit docs

→

Caching: Cache results by query hash to avoid duplicate API calls

→

Audit trail: Log query, timestamp, and result count for PRISMA compliance

→

Human screening: API finds candidates; researchers still screen titles/abstracts

Tech Stack Suggestions

•

Backend: Python (FastAPI) or Node.js (Express)

•

Frontend: React or Next.js with a searchable paper table

•

Database: PostgreSQL with full-text search on titles

Get Started

Get your free API key (50 calls) and follow the Python tutorial to build your fetcher today.