> Import references. Deduplicate. Screen abstracts.
> Process PDFs. Extract structured data.
> 9 AI providers. Free local models included.
> Desktop GUI & browser Web App included.
> One tool. Zero hassle.█
SYS: Windows // macOS // Linux • REQ: Python 3.10+ • LIC: MIT
// Full PRISMA workflow coverage — from import to extraction table
Import RIS, BibTeX, or CSV exports from PubMed, Scopus, Web of Science. Auto field normalisation.
Exact DOI matching + fuzzy title comparison (≥90% Levenshtein). No duplicates survive.
AI screens titles & abstracts before any PDF download — hours of triage eliminated.
3-engine cascade: pymupdf4llm → pdfplumber → PyPDF2. Handles virtually any PDF.
Pydantic + instructor + Quote-Then-Answer. The AI cites real text, not fabrications.
Stage 1: fast include/exclude. Stage 2: detailed criteria. Mirrors manual PRISMA.
Multiple PDFs simultaneously. Configurable threads. Smart caching avoids reprocessing.
Colour-coded Excel, CSV exports, summary reports — ready for your results chapter.
Your criteria. Your fields. Or pick from 5 built-in domain templates. Total control.
Full browser-based UI. 4-stage pipeline. AI prompt enhancer. PDF manager. In-app guide with disclaimer. No desktop install needed.
One-click AI improvement of your screening criteria, prompts & extraction fields — powered by your own API key.
// Cloud API or local model — your call, commander
| PROVIDER | COST | REC_MODEL | BEST_FOR |
|---|---|---|---|
| Ollama | [FREE] | llama3 / mistral | Large projects, privacy, offline |
| Google Gemini | [FREE] | gemini-2.5-flash | Fast cloud, generous limits |
| DeepSeek | [FREE] | deepseek-chat | Budget-friendly quality |
| Kimi (Moonshot) | [FREE] | moonshot-v1-auto | Long-context cloud AI |
| Mistral | [FREE] | mistral-large-latest | EU data residency |
| OpenAI | [TRIAL] | gpt-4o-mini | Best cost/quality ratio |
| Anthropic | [PAID] | claude-sonnet-4 | Highest reasoning quality |
| Grok (xAI) | [PAID] | grok-3-mini-fast | Real-time knowledge |
| Custom API | [VARY] | Any OpenAI-compat | LM Studio, vLLM, LocalAI |
> NO API KEY? Start with Ollama — 100% free, runs on your machine. Get it at ollama.ai
// 4 steps from raw references to finished data
Load RIS/BibTeX/CSV exports. Auto-normalise fields. Remove dupes via DOI + fuzzy title matching.
AI evaluates each title & abstract against your criteria → Include / Exclude / Maybe. No PDFs needed yet.
Drop PDFs into a folder. 3-engine text extraction + two-stage screening + parallel processing + caching.
AI extracts your specified data fields into colour-coded Excel. Copy-paste into your SLR chapter. Done.
// Pre-configured criteria for common research domains
Any research field
RCTs, PICO, cohorts
Pedagogy, outcomes
Climate, ecology
Algorithms, benchmarks
// Two interfaces — Desktop GUI & browser Web App. Same setup, your choice.
REM 1. Download or clone the repository
git clone https://github.com/sadeghanisi/SLR.git
cd SLR
REM 2. One-click setup (creates venv + installs all deps)
setup.bat
REM 3a. Launch desktop GUI
launch_gui.bat
REM 3b. OR launch Web App (opens in your browser)
cd WebApp
run.bat
REM → then open http://127.0.0.1:5000# 1. Download or clone the repository
git clone https://github.com/sadeghanisi/SLR.git
cd SLR
# 2. Create venv & install
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
# 3a. Launch desktop GUI
python slr_gui.py
# 3b. OR launch Web App (opens in your browser)
cd WebApp
python app.py
# → then open http://127.0.0.1:5000# Requires Python 3.10+
python -m venv .venv
# Activate
.venv\Scripts\activate # Windows
source .venv/bin/activate # macOS / Linux
# Install all dependencies
pip install -r requirements.txt
# Option A — Desktop GUI
python slr_gui.py
# Option B — Web App (runs locally in your browser)
cd WebApp
python app.py
# → open http://127.0.0.1:5000# Step 1 — Clone and set up (same as desktop GUI)
git clone https://github.com/sadeghanisi/SLR.git
cd SLR
python -m venv .venv
# Activate venv
.venv\Scripts\activate # Windows
source .venv/bin/activate # macOS / Linux
# Step 2 — Install all dependencies (Flask is included)
pip install -r requirements.txt
pip install flask flask-cors
# Step 3 — Launch the Web App server
cd WebApp
python app.py
# Step 4 — Open in browser
# → http://127.0.0.1:5000
# Windows shortcut: double-click WebApp\run.bat
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║ BUILT FOR REAL RESEARCH ║
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║ ║
║ PhD students processing 500 papers for a dissertation ║
║ Professors supervising multiple systematic reviews ║
║ Research teams running large-scale evidence synthesis ║
║ ║
║ >> Weeks of manual screening → Hours of automation << ║
║ ║
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YES. The tool = 100% free, open source (MIT License). Cloud AI providers may charge for API usage — but Ollama runs locally at $0.
NO. Graphical interface — click buttons, select options, browse files. Zero command-line knowledge needed after initial setup.
LLM = Large Language Model (the AI that reads papers, like ChatGPT). API key = password letting the tool talk to the AI service. Sign up with any provider to get one. See the COMPLETE_USER_GUIDE.md.
FREE + PRIVATE → Ollama. FREE CLOUD → DeepSeek / Gemini. BEST QUALITY → GPT-4o-mini or Claude Sonnet.
85–95% on abstract screening, higher on full-text. Quote-Then-Answer forces AI to cite actual paper text → minimal hallucination.
AFFIRMATIVE. Follows PRISMA framework. Do a final manual check of edge cases, mention the tool in your methods section.
INPUT: RIS, BibTeX (.bib), CSV, PDF • OUTPUT: Excel (.xlsx) colour-coded, CSV, plain-text summary reports.
Cloud provider → paper text sent to their API. Ollama (local) → everything stays on your machine. We receive ZERO data. Ever.
> "As Is" Provision: This software is provided "as is," without warranty of any kind, express or implied. The authors and distributors accept no responsibility for decisions made based on AI-generated screening or extraction results.
> AI Limitations: This tool assists with — but does not replace — human judgment. AI models can and do make errors, including incorrect inclusion/exclusion decisions and inaccurate data extraction. All AI outputs must be independently verified by qualified researchers before use in any publication, thesis, clinical decision, or policy document.
> No Academic Guarantee: Use of this tool does not ensure compliance with PRISMA, CONSORT, or any other reporting standard. Researchers remain solely responsible for the methodological integrity, transparency, and accuracy of their systematic reviews.
> Data Privacy: When using cloud-based AI providers (OpenAI, Anthropic, Google, DeepSeek, or others), your paper content is transmitted to third-party servers. The authors of this tool make no representations regarding how those providers store, process, or use your data. Consult each provider’s privacy policy before processing sensitive or unpublished material. For confidential data, use the local Ollama option.
> Cost and Billing: API usage fees are charged directly by third-party AI providers. The authors of this tool have no visibility into, or responsibility for, charges incurred through your API account. Monitor your usage and set billing limits with your provider before running large processing jobs.
> Institutional Compliance: It is your responsibility to verify that AI-assisted research methods comply with your institution’s policies, your funding body’s requirements, and the ethical standards of your field. Disclose AI tool usage in all relevant sections of your research output.
> No Liability: To the fullest extent permitted by law, the authors, contributors, and distributors of this tool shall not be liable for any direct, indirect, incidental, or consequential damages arising from its use, including but not limited to data loss, incorrect research conclusions, academic penalties, or financial charges.
By using this tool, you acknowledge that you have read, understood, and accepted these terms.
┌─────────────────────────────────────────────┐
│ READY TO AUTOMATE YOUR LIT REVIEW? │
│ Download → Setup (2 min) → Process. │
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v3.3.0 • MIT License • Python 3.10+ • WIN / MAC / LINUX