PaperBanana is an AI-based figure-generation tool tailored for researchers. It accepts natural-language descriptions or uploaded sketches and produces methodological diagrams, system architectures, statistical plots, and educational infographics. A multi-agent architecture translates text into structured layouts and renders visuals with a dedicated model, while statistical plots are produced as executable Matplotlib code to guarantee numeric accuracy. Advanced mode can ingest articles to recover visual styles and refine aesthetics to publication standards.
Paper Banana Core Features
Multi-agent pipeline for converting text to structured diagrams
Generation of executable Matplotlib code for accurate statistical plots
Styles and aesthetic refinement to publication standards
Support for uploaded sketches and article-based style recovery
Multiple models and advanced generation modes (Nano-Banana variants)
Paper Banana Pro & Cons
The Cons
Web-only interface may limit offline workflows
Generation may require credits or account sign-in
Focused on academic visuals—less suitable for general artistic images
Some advanced features may need technical familiarity
The Pros
Produces scientifically accurate plots via code export
Tailored to academic standards with aesthetic refinement
Supports multiple figure types and uploaded inputs
Backed by a research paper and an associated GitHub project