QuantaQuill is an AI-driven system that automates the creation of structured scientific research papers. It combines neural language models with symbolic reasoning to generate academic content that is coherent, logically validated, and well-cited.
✔ Automated literature review from trusted sources ✔ Generation of all major sections:
- Abstract
- Introduction
- Methodology
- Experiments
- Results
- Conclusion ✔ IEEE-style citations and reference list ✔ Knowledge Graph-based logical validation ✔ Export to PDF in one click
Traditional paper writing is time-consuming and manual. QuantaQuill simplifies the process by leveraging:
- Multi-Agent Architecture for research, writing, and validation
- Neurosymbolic AI for structured and explainable outputs
- Automation without compromising academic integrity
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Enter your research topic
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(Optional) Add Methodology and Experiments details
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The system:
- Fetches and summarizes related literature
- Writes structured sections
- Adds citations and validates claims
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Download your paper in PDF format
# Clone the repository
git clone https://github.com/yourusername/quantaquill.git
cd quantaquill
# Create virtual environment and install dependencies
uv venv venv
source venv/bin/activate # or venv\Scripts\activate on Windows
uv pip install -r requirements.txtRun the application:
# Start the FastAPI backend
uvicorn backend.main:app --reload
# Start the Streamlit frontend
streamlit run frontend/app.pyAccess the interface: ➡ http://localhost:8501
- Abstract
- Introduction
- Methodology
- Experiments
- Results
- Conclusion
- References (IEEE format)
QuantaQuill isn’t just another text generator — it integrates:
- Symbolic reasoning for claim validation
- Knowledge Graph for consistency checks
- Automated citations to ensure academic reliability
QuantaQuill showcases the future of AI-assisted research workflows by combining automation, accuracy, and academic integrity.