PaperBanana

Generate publication-quality academic diagrams and pipeline figures from a paper's methodology section and figure caption. PaperBanana orchestrates a multi-agent pipeline (Retriever, Planner, Stylist, Visualizer, Critic) to produce camera-ready figures suitable for venues like NeurIPS, ICML, and ACL.

Environment Setup

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Set your API key via environment variable or in configs/model_config.yaml.

Option 1 (Recommended): OpenRouter API key — one key for both text reasoning and image generation:
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Option 2: Google API key — direct access to Gemini API:
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If both keys are configured, OpenRouter is used by default.

Usage

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Parameters

*One of --content or --content-file is required.

When --num-candidates > 1, output files are named <stem>_0.png, <stem>_1.png, etc.

Output

The absolute path of each saved image is printed to stdout, one per line.

Examples

Diagram

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Important Notes

• - Runtime: A single candidate typically takes 3-10 minutes depending on model and network conditions. With the default 10 candidates running in parallel, expect ~10-30 minutes total. Plan accordingly.
• API calls: Each candidate involves multiple LLM calls (Retriever + Planner + Stylist + Visualizer + up to 3 Critic rounds). Candidates run in parallel for efficiency.
• Image generation: The Visualizer agent calls an image generation model (Gemini Image) to render diagrams.

About

PaperBanana is based on the PaperVizAgent framework, a reference-driven multi-agent system for automated academic illustration. It was developed as part of the research paper:

PaperBanana: Automating Academic Illustration for AI Scientists
Dawei Zhu, Rui Meng, Yale Song, Xiyu Wei, Sujian Li, Tomas Pfister, Jinsung Yoon
arXiv:2601.23265

The framework introduces a collaborative team of five specialized agents — Retriever, Planner, Stylist, Visualizer, and Critic — to transform raw scientific content into publication-quality diagrams. Evaluation is conducted on the PaperBananaBench benchmark.