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paperbanana

Generates academic paper diagrams and plots from method text via a multi-agent pipeline

Stars
2.5k
License
MIT
Last commit
Sep 2026
Last release
Jun 2026
Language
Python
Stars, last 29 weeks+1,162
21 Apr 2026: 1,314 stars28 Apr 2026: 1,356 stars5 May 2026: 1,372 stars12 May 2026: 1,388 stars19 May 2026: 1,648 stars26 May 2026: 1,745 stars2 Jun 2026: 1,767 stars9 Jun 2026: 1,943 stars16 Jun 2026: 1,979 stars23 Jun 2026: 1,999 stars30 Jun 2026: 2,023 stars7 Jul 2026: 2,045 stars14 Jul 2026: 2,096 stars21 Jul 2026: 2,155 stars28 Jul 2026: 2,181 stars4 Aug 2026: 2,203 stars11 Aug 2026: 2,220 stars18 Aug 2026: 2,235 stars25 Aug 2026: 2,270 stars1 Sept 2026: 2,285 stars8 Sept 2026: 2,333 stars15 Sept 2026: 2,351 stars22 Sept 2026: 2,368 stars29 Sept 2026: 2,378 stars6 Oct 2026: 2,385 stars13 Oct 2026: 2,399 stars20 Oct 2026: 2,413 stars27 Oct 2026: 2,426 stars3 Nov 2026: 2,460 stars10 Nov 2026: 2,476 stars

1,314 on 21 Apr 20262,476 on 10 Nov 2026

From GitHub's daily star counts.

Checked on GitHub.

Overview

Unofficial implementation of the PaperBanana paper. It turns methodology text or PDFs plus a caption into publication-style diagrams using a retriever, planner, stylist, visualizer and critic loop. Statistical plots are rendered from CSV/JSON via VLM-generated matplotlib code. It ships a CLI, Python API, MCP server, Claude Code skills and a local Gradio UI.

Who it is for: Researchers who need paper figures and plots generated from method text

Strengths

  • Supports OpenAI, Azure, Gemini, Atlas Cloud and OpenRouter providers, selectable per run
  • Batch manifests (YAML/JSON) with composite multi-panel figures and Markdown/HTML reports
  • Accepts PDF input with page selection and reference sketch images to guide the planner
  • Plots need only a VLM, no image-generation provider; venue style packs are customizable

Weaknesses

  • Requires a cloud API key; no local model path is described
  • README states it may differ from the original paper's system
  • Gemini image model is listed at $0.134 per 1K image, so runs cost money
  • Reference retrieval draws on a curated set of only 13 example diagrams

What it needs

  • no GPU
  • Docker
  • Needs OpenAI, Azure OpenAI, Google Gemini, Atlas Cloud or OpenRouter API key, PyMuPDF (optional, for PDF input), Gradio (optional, for Studio)
  • Models: gpt-5.2, gpt-image-1.5, gemini-2.5-flash, gemini-3-pro-image-preview, DeepSeek-V3-0324 (Atlas Cloud)
  • port 7860

Quick answers

Is paperbanana open source?

Yes. Its code is on GitHub under the MIT license.

Can you run paperbanana with Docker?

Yes. Its repository has a Docker setup: a Dockerfile, a Compose file or a published image.

Does paperbanana need a GPU?

No, it runs without one.

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