- AI Agents Frameworks
PaperBanana is an agentic framework for AI researchers, creating publication-ready diagrams and charts.
- Free
- Open Source
- Horizontal
PaperBanana
empowers professionals like
- AI Engineers AI Researchers Clinical Researchers Data Engineers Data Scientists Machine Learning Engineers Research Scientists Researchers
PaperBanana
can assist with
- Academic Illustrations Academic Research
PaperBanana
Introduction
PaperBanana is an agent-based automation framework built for AI researchers, enabling the creation of publication-ready academic visuals. It transforms text prompts or rough references into structured methodology diagrams, statistical charts, and research illustrations with minimal manual effort.
PaperBanana
Features
✨ AI-Powered Diagram Creation
PaperBanana turns simple text prompts into structured visuals, automatically building methodology diagrams, system overviews, complex flows, and research layouts.
✨ Sketch-to-Professional Polishing
Rough hand-drawn ideas can be uploaded and refined into clean digital diagrams while preserving layout intent, improving style consistency, and enhancing visual quality.
✨ Academic-Grade Statistical Visuals
The framework also generates publication-ready charts and plots, delivering clear data visualization, accurate representation, and high-quality vector outputs suitable for research use.
PaperBanana
Use Cases
✓ Accelerating Research Workflows
PaperBanana removes the manual effort of designing academic visuals, helping researchers save time by automating publication-ready diagrams, charts, and scientific illustrations.
✓ Self-Improving Visual Outputs
With an iterative critique system, the framework evaluates and refines visuals to maintain clarity, faithfulness, and strong academic presentation standards.
✓ Multi-Agent Research Automation
Specialized agents handle context retrieval, layout planning, rendering, and evaluation, enabling a structured workflow for generating high-fidelity research graphics.
✓ Consistent Conference-Ready Design
Figures maintain standardized aesthetics and precision, ensuring visuals align with strict academic guidelines used in leading AI conferences.
✓ Multimodal Research Creation
Powered by advanced vision-language and image models, PaperBanana supports both text-to-image generation and continuous refinement through intelligent feedback loops.
✓ Open Research Collaboration
Through resources like PaperBananaBench, researchers can explore curated benchmarks and contribute to improving automated scientific illustration across domains.
✓ High-Accuracy Scientific Visualization
Designed for academic rigor, the framework produces methodology diagrams and statistical plots that prioritize precision, clarity, and research-grade quality.
PaperBanana
Integration Method
- API | Web Application
