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Chat with your data — natural-language BI with SQL generation
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View guide →PaperBanana — agentic generation of publication-quality academic figures, diagrams and plots from text
paperbanana is an open-source design project by llmsresearch (llmsresearch/paperbanana on GitHub). It has 2,388 GitHub stars and 341 forks, is released under the MIT license, and was last updated on GitHub on 17 September 2026.
Prepare your methodology text (or a paper PDF) and a precise caption describing the figure you want; for plots, prepare the CSV/JSON data and the chart's intent.
pip install paperbanana, or clone and install from source with the provider extras you need.
Copy .env.example to .env and add an OpenAI, Azure or Gemini key (Gemini's free tier works), or run paperbanana setup for the Gemini wizard.
Run paperbanana generate with your input and caption; add --optimize and --auto for input optimization and critic-driven auto-refinement, then review the iterations in outputs/.
Up to 7 specialized agents: input optimizer (context enricher + caption sharpener), retriever, planner, stylist, visualizer and critic, in a two-phase plan-then-refine flow.
generate makes methodology diagrams; plot renders statistical plots via VLM-generated matplotlib code — no image-generation credentials needed for plots.
Refinement rounds (default 3), --auto to loop until the critic is satisfied, and --continue to resume a run with user feedback.
Batch diagrams from a YAML/JSON manifest, batch plots per-item from CSV/JSON, and PDF inputs with per-page selection (optional PyMuPDF extra).
A local Gradio Studio (paperbanana studio), an MCP server for IDE integration, and Claude Code skills for /generate-diagram, /generate-plot and /evaluate-diagram.
Built-in NeurIPS, ICML, ACL and IEEE style packs, with scaffolding to create your own from example figures.
Clone PaperBanana if installing from source; PyPI installation is step 2's alternative.
git clone https://github.com/llmsresearch/paperbanana.git
cd paperbananaInstall from PyPI, or from source with the provider extras shown in the README.
pip install paperbanana
# source: pip install -e ".[dev,openai,google]"Set your key in .env first (OPENAI_API_KEY or GOOGLE_API_KEY), then generate from the sample input.
paperbanana generate --input examples/sample_inputs/transformer_method.txt --caption "Overview of our encoder-decoder architecture with sparse routing"llmsresearch maintains this unofficial, community-driven implementation of the PaperBanana paper (Zhu et al.); it is not affiliated with or endorsed by Google Research or the original authors.
The code is MIT-licensed. Generation calls external VLM/image APIs — Gemini has a free tier, while OpenAI/Azure/Atlas usage is billed by those providers.
It needs an API key for a VLM/image provider, but a free Google Gemini key from AI Studio is enough to start; statistical plots need no image provider.
A single prompt gives one unreviewed image. PaperBanana plans from curated reference diagrams, styles to venue guidelines, and critiques/revises its own output over several rounds — and can continue from your feedback.
pip install paperbanana, put a Gemini or OpenAI key in .env, and run paperbanana generate on the sample input — or try the Colab quickstart with no local setup.
Chat with your data — natural-language BI with SQL generation
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