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ScholaRAG democratizes access to systematic literature review automation.
We believe researchers should spend more time analyzing findings and less time on mechanical tasks. By combining PRISMA 2020 guidelines with modern RAG technology, we help academics conduct rigorous reviews in weeks instead of months.
Fetch all available papers from multiple academic databases with comprehensive coverage and direct PDF access.
20K+ papers · 5 databases · 100% coverage
Multi-dimensional paper evaluation using LLMs with transparent criteria, confidence scoring, and human validation.
PICO framework · Cohen's κ · 10-20% precision
Build semantic search over your papers using vector databases for instant literature queries with citations.
ChromaDB · FAISS · Contextual retrieval
Step-by-step guidance through Claude Code in VS Code, making systematic reviews accessible without coding.
7 stages · 4-5 hours · Interactive prompts
ScholaRAG leverages the latest AI coding models optimized for research automation, keeping operational costs under $20-40 per project for typical systematic reviews.
Claude Sonnet 4.5
Released October 2025 - currently the most effective coding model for research automation. Achieves state-of-the-art performance on SWE-bench for complex workflow generation.
Best for automation · Advanced reasoning
GPT-5-Codex
Advanced code generation with superior reasoning for complex research workflows. Excellent for systematic review pipeline design and execution.
Advanced codegen · Research-optimized
2-3 weeks vs 6-8 weeks
Dissertation literature reviews, qualifying exams, comprehensive analysis
67-75% time savings
Meta-analysis preparation, grant proposals, PRISMA 2020 reviews
Never forget citations
Course updates, research synthesis, student mentoring
Scalable support
Systematic review consulting, evidence-based practice workshops
MIT Licensed · Community Driven · Fully Transparent
Use for academic or commercial research
Customize workflows for specific domains
Contribute improvements and templates
Run locally with full data control
Hosung You
PhD Candidate
College of Education
The Pennsylvania State University
If you use ScholaRAG in your research:
@software{scholarag2025,
author = {You, Hosung},
title = {ScholaRAG: AI-Powered Systematic
Literature Review Automation},
year = {2025},
url = {https://github.com/HosungYou/ScholaRAG},
note = {Open-source PRISMA-compliant RAG system}
}