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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 generation.

2k stars2k/wkupdated 15d agogithub ↗
88good
▣ Overview

What it does

PaperBanana is an agentic framework for generating publication-quality academic diagrams and statistical plots from text descriptions. It implements a multi-agent pipeline with iterative refinement, powered by vision language models and image generation from OpenAI, Azure OpenAI, or Google Gemini. The system optimizes input descriptions, generates candidate diagrams, evaluates them, and refines iteratively until quality criteria are met. Supports single diagram generation, batch processing from YAML/JSON manifests, PDF-based methodology context, and continuation of prior runs with user feedback. Access is provided via CLI (paperbanana generate, paperbanana plot-batch), Python API, or an MCP server with Claude Code integration.

Who it's for

Researchers and AI scientists authoring papers who need to convert methodology descriptions and experimental results into publication-ready visualizations without manual design. Teams using Claude or other IDEs who want to integrate automated diagram generation into their research workflow.

Common use cases

  • Generate architecture and methodology diagrams from natural-language descriptions in paper drafts.
  • Create statistical plots and comparative charts from data specifications or CSV files.
  • Batch-generate multiple diagrams in one run from a manifest, then refine iteratively.
  • Integrate diagram generation into Claude Code workflows via the MCP server and /generate-diagram skill.
  • Use PaperBanana Studio (local Gradio web UI) for interactive iteration and evaluation before publication.

Setup pitfalls

  • Requires an API key from OpenAI, Azure OpenAI, or Google Gemini (.env configuration). Gemini offers a free tier via paperbanana setup wizard.
  • Requires Python 3.10+. Install via pip install paperbanana or from source with pip install -e ".[dev,openai,google]".
  • Azure OpenAI and Foundry require OPENAI_BASE_URL environment variable; standard OpenAI uses OPENAI_API_KEY.
  • Reads and writes files to disk extensively (all outputs, intermediate iterations, metadata); ensure write permissions in your working and output directories.
▣ Score BreakdownMCPScore = Σ(raw × weight)
DimensionRawWeighted
Security
35%
100
35.0
Freshness
25%
100
25.0
Adoption
20%
72
14.4
Quality
10%
90
9.0
Trust
10%
50
5.0
Total
88.4
⚿ Capabilities & Risk Explainer
fs readfs writenetworkexecsecrets
◆ Risk level: high· 5 tools · auth: API key
fs read + fs write + network + exec + secrets active — can execute code, access credentials, and make external network calls.
Tool nameDescriptionDestructive?
generate_diagramGenerate a methodology diagram from text context and caption✓ no
continue_runContinue refinement for an existing run directory (optional critic feedback)⚠ yes
generate_plotGenerate a statistical plot from JSON data and intent description✓ no
evaluate_diagramCompare a generated diagram against a human reference on 4 dimensions✓ no
evaluate_plotCompare a generated statistical plot against a human reference on 4 dimensions✓ no
⚙ Install config
Claude Desktop · Cursor · Windsurf · VS Code (Copilot) · Claude Code
add to your MCP client config:
{
  "mcpServers": {
    "paperbanana-1": {
      "command": "uvx",
      "args": [
        "paperbanana"
      ]
    }
  }
}
⚙ Maintenance health
65/ 100 · is this project alive?
contributors (1y)31
top contributor share27%
releases (1y)3
last release113d ago
ci✓ passing
⛁ Raw data
weekly downloads2k
github stars2k
forks263
open issues35
license✓ present
readme length13012 chars
last publish0d ago
last commit15d ago
last updated10h ago
install verified✓ pass · 18d ago
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