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Paperbanana
Open source implementation and extension of Google Research’s PaperBanana for automated academic figures, diagrams, and research visuals, expanded to new domains like slide…
Install
$
pip install paperbananaInstall command taken from the project's README or registry entry. Always review code before running it.
README excerpt
Read full README on GitHub →</td>
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PaperBanana
<strong>Automated Academic Illustration for AI Scientists</strong>
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Disclaimer: This is an unofficial, community-driven open-source implementation of the paper "PaperBanana: Automating Academic Illustration for AI Scientists" by Dawei Zhu, Rui Meng, Yale Song, Xiyu Wei, Sujian Li, Tomas Pfister, and Jinsung Yoon (arXiv:2601.23265). This project is not affiliated with or endorsed by the original authors or Google Research. The implementation is based on the publicly available paper and may differ from the original system.
An agentic framework for generating publication-quality academic diagrams and statistical plots from text descriptions. Supports OpenAI (GPT-5.2 + GPT-Image-1.5), Azure OpenAI / Foundry, Google Gemini, and Atlas Cloud providers.
- Two-phase multi-agent pipeline with iterative refinement
- Multiple VLM and image generation providers (OpenAI, Azure, Gemini, Atlas Cloud)
- Input optimization layer for better generation quality
- Auto-refine mode and run continuation with user feedback
- CLI, Python API, and MCP server for IDE integration
- Batch generation from a manifest file (YAML/JSON) for multiple diagrams in one run
- Batch plots —
paperbanana plot-batchruns many statistical plots from one manifest (CSV/JSON per item) - PDF inputs for methodology context (optional
paperbanana[pdf]/ PyMuPDF), with per-page selection - PaperBanana Studio — local Gradio web UI (
paperbanana studio) for diagrams, plots, evaluation, batch, and run browser - Claude Code skills for
/generate-diagram,/generate-plot, and/evaluate-diagram
Learning LLM concepts? We also publish 211 free visual cards on attention, RAG, agents, and inference. Read them online.
Atlas Cloud
Atlas Cloud is a full-modal AI inference platform that gives developers a single AI API to access video generation, image generation, and LLM APIs. Instead of managing multiple vendor integrations, you connect once and get unified access to 300+ curated models across all modalities.
Check out Atlas Cloud's new coding plan promotion for more budget-friendly API access: https://www.atlascloud.ai/console/coding-plan
Quick Start
Try it in your browser: the Colab quickstart notebook walks through install → API key → diagram generation end-to-end, no local setup required.
Prerequisites
- Python 3.10+
- An OpenAI API key (platform.openai.com) or Azure OpenAI / Foundry endpoint
- Or a Google Gemini API key (free, Google AI Studio)
Step 1: Install
pip install paperbanana
Or install from source for development:
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