PaperBanana

Description

PaperBanana is an agentic AI framework that automates academic illustration, transforming raw scientific content such as methodology, data, or concepts into publication-quality diagrams and plots. It is aimed at lifting the illustration bottleneck in the research workflow so researchers can produce figures without labor-intensive manual design.

Under the hood, PaperBanana orchestrates five specialized agents that collaborate on each illustration: a Retriever that finds relevant reference examples to guide style and content, a Planner that translates content into detailed descriptions, a Stylist that enforces academic aesthetic standards, a Visualizer that renders the images, and a Critic that inspects results against the source and provides feedback for iterative refinement until output is publication-ready. For statistical charts, it generates executable Python Matplotlib code so every bar height, axis tick, and data point reflects the actual numbers rather than AI approximations.

The tool supports several illustration types, including methodology diagrams (neural network architectures, algorithm flowcharts, and system pipelines), statistical plots, aesthetic enhancement of rough sketches, educational infographics, and aesthetic refinement of existing diagrams. Outputs are high-resolution and ready to insert into LaTeX or Word documents, and users can download the figure or the underlying code. A free illustration is available on sign-in, with paid plans starting at a monthly rate.

PaperBanana's Core Features

• Multi-agent workflow (Retriever, Planner, Stylist, Visualizer, Critic)

• Reference-driven generation matching academic standards

• Iterative self-critique for automatic refinement

• Code-based statistical plots via executable Python Matplotlib

• Methodology diagrams for architectures, flowcharts, and pipelines

• Aesthetic enhancement and refinement of sketches and existing figures

• Educational infographics for teaching and communication

• High-resolution output ready for LaTeX or Word

How to use PaperBanana?

1. 1

   Sign in: Sign in with Google to generate your first illustration.

2. 2

   Enter your content: Provide a methodology description, dataset, or concept to visualize.

3. 3

   Generate: Let the multi-agent workflow plan, render, and critique the illustration.

4. 4

   Refine: The Critic agent iterates until the result is publication-ready.

5. 5

   Download: Save the high-resolution figure or the underlying code for further customization.

PaperBanana's Use Cases

• Methodology diagrams
• Accurate statistical plots
• Figure polishing
• Educational visuals

FAQ from PaperBanana

• What is PaperBanana?

  PaperBanana is an agentic framework that orchestrates specialized agents to transform raw scientific content into publication-quality diagrams and plots automatically.

• What illustration types can it generate?

  It supports methodology diagrams, statistical plots, aesthetic enhancement of rough sketches, educational infographics, and aesthetic refinement of existing diagrams.

• How does it ensure quality?

  It uses a multi-agent workflow where the Retriever finds references, the Planner writes descriptions, the Stylist enforces academic aesthetics, the Visualizer renders, and the Critic inspects and refines the results.

• How does it keep statistical plots accurate?

  It generates executable Python Matplotlib code so charts reflect your actual numbers with correct scales and legends, eliminating AI approximation errors.

• What input does it need?

  It works with text descriptions of your research content, such as methodology descriptions, data for plots, or concept descriptions, and you can also upload references.

• Where can I use the output?

  Outputs are high-resolution and ready to insert directly into research papers, presentations, and academic posters, in LaTeX or Word.