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AI-powered analysis of scientific research papers using 8 specialized agents.
Upload any research paper PDF and receive a comprehensive, multi-dimensional analysis in minutes — covering methodology, writing quality, data visualizations, citations, plagiarism risk, journal recommendations, and funding opportunities.
Try the Live Demo — 3 pre-analyzed papers available instantly, no account needed.
Most paper review tools focus on a single dimension. This analyzer deploys 8 independent AI agents, each specialized in a different aspect of academic paper evaluation. They run in parallel and return structured, actionable feedback.
| Agent | What It Evaluates | Key Output |
|---|---|---|
| Results Synthesizer | Key findings, effect sizes, statistical significance | Evidence strength rating, p-values, confidence intervals |
| Writing Coach | Academic writing quality per section | Score (1-5), passive voice %, sentence length, style guide references |
| Methodology Critic | Study design, sample size, bias, reproducibility | Quality score (1-5), identified biases, critical issues |
| DataViz Critic | Figures against Tufte/Cleveland/Few best practices | Per-figure scores, colorblind accessibility, chart junk detection |
| Citation Hunter | Related literature via Semantic Scholar | Supporting & conflicting papers, research gaps |
| Plagiarism Detector | Missing citations, suspicious paraphrasing | Risk score (0-100), flagged sections with severity |
| Journal Recommender | Target journals via OpenAlex metrics | 5+5 ranked journals with impact factor, h-index, APC, acceptance likelihood |
| Funding Advisor | Funding sources from similar funded research | Ranked funders with programs, typical amounts, eligibility, application tips |
- Original Research
- Review Articles / Literature Reviews
- Meta-Analyses
- Case Studies
PDF Upload
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Text Extraction (pypdf) ──> Section Detection (LLM) ──> Paper Type Classification
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[PARALLEL EXECUTION - ThreadPoolExecutor]
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v v v v v v v
Results Writing Methods DataViz Citations Plagiarism Funding
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v v
(6 LLM calls, (PyMuPDF extracts figures,
Python regex GPT-4o Vision analyzes
+ LLM hybrid) up to 5 charts)
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v
Journal Recommender
(runs after Methods + Results
for better recommendations)
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v
Aggregate ──> Dashboard (9 tabs)
──> JSON Export
──> Markdown Report
Key design decisions:
- Hybrid Python + LLM approach — Python computes objective metrics (passive voice ratio, sentence length, hedge word count), LLM provides qualitative interpretation
- LLM-based section detection — Not regex-based, because academic papers vary widely across disciplines
- Vision-based figure analysis — GPT-4o Vision evaluates actual chart visuals, not just text descriptions
- Paper-type aware — Review papers get different plagiarism criteria (paraphrasing is expected), methodology is scored differently
- Cross-section context — Methodology agent reads Methods + Abstract + Results together for deeper analysis
Three pre-analyzed papers are included for instant exploration without Azure credentials:
| Paper | Type | Domain |
|---|---|---|
| Hospital Variation in THR Outcomes (n=583) | Original Research | Healthcare / Orthopedics |
| Pharmacological Advances in HIV Treatment | Review Article | Pharmacology / Infectious Disease |
| ML Framework for Stock Market Prediction | Original Research | Quantitative Finance / AI |
| Component | Technology |
|---|---|
| Frontend | Streamlit |
| LLM | Azure OpenAI (GPT-4o) |
| PDF Text | pypdf |
| PDF Images | PyMuPDF (fitz) |
| Citation Search | Semantic Scholar API (free) |
| Journal & Funding Data | OpenAlex API (free) |
| Image Processing | Pillow |
| Parallelization | concurrent.futures.ThreadPoolExecutor |
Running all 8 agents on a typical 12-page paper:
| Agent | Cost | Time |
|---|---|---|
| Results Synthesizer | ~$0.01 | ~5s |
| Writing Coach | ~$0.03 | ~30s |
| Methodology Critic | ~$0.02 | ~5s |
| DataViz Critic | ~$0.05-0.15 | ~60-120s |
| Citation Hunter | ~$0.02 | ~10s |
| Plagiarism Detector | ~$0.03 | ~5s |
| Journal Recommender | ~$0.04 | ~60-120s |
| Funding Advisor | ~$0.04 | ~60-120s |
| Total | ~$0.24-0.34 | ~3-5 min |
Semantic Scholar and OpenAlex APIs are free. With parallel execution, total wall-clock time is determined by the slowest agent (~2-3 minutes).
- Python 3.11+
- Azure OpenAI resource with a GPT-4o deployment
git clone https://github.com/leelesemann-sys/research-paper-analyzer.git
cd research-paper-analyzer
python -m venv .venv
.venv\Scripts\activate # Windows
# source .venv/bin/activate # macOS/Linux
pip install -r requirements.txtCopy .env.example to .env and fill in your Azure OpenAI credentials:
AZURE_OPENAI_ENDPOINT=https://your-resource.openai.azure.com/
AZURE_OPENAI_API_KEY=your-key-here
AZURE_OPENAI_DEPLOYMENT_NAME=gpt-4o
AZURE_OPENAI_API_VERSION=2024-12-01-preview
streamlit run Paper_Analyzer.pyThe demo mode works without Azure credentials — you can explore 3 pre-analyzed papers immediately.
research-paper-analyzer/
|-- Paper_Analyzer.py # Streamlit frontend (main app)
|-- workflow.py # Orchestrator: PDF processing, section detection, agent coordination
|-- agents/
| |-- results.py # Agent 1: Results Synthesizer
| |-- writing.py # Agent 2: Writing Quality Coach
| |-- methodology.py # Agent 3: Methodology Critic
| |-- visualization.py # Agent 4: DataViz Critic (Vision API)
| |-- citations.py # Agent 5: Citation Hunter (Semantic Scholar)
| |-- plagiarism.py # Agent 6: Plagiarism Detector
| |-- journals.py # Agent 7: Journal Recommender (OpenAlex)
| |-- funding.py # Agent 8: Funding Advisor (OpenAlex)
|-- pages/
| |-- 1_How_It_Works.py # Architecture & agent documentation page
|-- demo_data/ # Pre-computed demo analyses (3 papers)
|-- requirements.txt
|-- .env.example
This project is for portfolio and educational purposes.
Built with Azure OpenAI, Streamlit, Semantic Scholar, and OpenAlex.