PaperBanana

Name: PaperBanana
Author: llmsresearch

by llmsresearchversion 0.1.1i★ –iRuns on your computer

Registry nameio.github.llmsresearch/paperbanana

Install

Overview

Description from the maker (original English)

Generate academic diagrams and statistical plots from text using multi-agent AI.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiEarly↓ 2.1K/wk · ★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Design, images & audio

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
The registry lists no key to fill in.

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "paperbanana": {
      "command": "uvx",
      "args": [
        "paperbanana"
      ]
    }
  }
}

Run this in your terminal

claude mcp add paperbanana -- uvx paperbanana

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "paperbanana": {
      "command": "uvx",
      "args": [
        "paperbanana"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "paperbanana": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "paperbanana"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 8, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account llmsresearch, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Design, images & audioi

More stars than 0% of the other 2782 servers in this category
Published to the registry more recently than 1% of the other 2782 servers in this category
Basic checks equal to or better than 100% of the other 2782 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is PaperBanana safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.1.1) reached the registry 9 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Figma Context MCP, Claude Real Video, Figma MCP Server

Alternatives

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Reading a card:↓ Downloads per weeki★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

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Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

PaperBanana Automated Academic Illustration for AI Scientists

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-batch runs 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:

git clone https://github.com/llmsresearch/paperbanana.git

cd paperbanana pip install -e ".[dev,openai,google]"

Docker

Build the image from a clone of the repo and pass your API key at runtime:

docker build -t paperbanana .

docker run --rm -e GOOGLE_API_KEY paperbanana generate --help

To generate a diagram, mount your input and an outputs folder into /work:

docker run --rm -e GOOGLE_API_KEY \

-v "$(pwd)/method.txt:/work/method.txt:ro" \ -v "$(pwd)/outputs:/work/outputs" \ paperbanana generate --input method.txt --caption "Overview of our framework"

Step 2: Get Your API Key

cp .env.example .env

Edit .env and add your API key:

OPENAI_API_KEY=your-key-here

GOOGLE_API_KEY=your-key-here

For Azure OpenAI / Foundry:

OPENAI_BASE_URL=https:// .openai.azure.com/openai/v1

Optional Gemini overrides:

GOOGLE_BASE_URL=https://your-gemini-proxy.example.com

GOOGLE_VLM_MODEL=gemini-2.5-flash

GOOGLE_IMAGE_MODEL=gemini-3-pro-image-preview

Or use the setup wizard for Gemini:

paperbanana setup

Step 3: Generate a Diagram

paperbanana generate \

--input examples/sample_inputs/transformer_method.txt \ --caption "Overview of our encoder-decoder architecture with sparse routing"

With input optimization and auto-refine:

paperbanana generate \

--input my_method.txt \ --caption "Overview of our encoder-decoder framework" \ --optimize --auto

Output is saved to outputs/run_ /final_output.png along with all intermediate iterations and metadata.

PaperBanana Studio (local web UI)

Install the optional Gradio dependency, then start the app:

pip install 'paperbanana[studio]'

paperbanana studio

Open the URL shown in the terminal (default http://127.0.0.1:7860/). The Studio exposes the same workflows as the CLI: methodology diagrams, statistical plots, comparative evaluation, continuing a prior run, batch manifests (methodology or plot batch via the Batch tab), and a simple browser for run_* / batch_* output folders. Use --host, --port, --config, and --output-dir as needed.

How It Works

PaperBanana implements a multi-agent pipeline with up to 7 specialized agents:

Phase 0 -- Input Optimization (optional, `--optimize`):

Input Optimizer runs two parallel VLM calls:

Context Enricher structures raw methodology text into diagram-ready format (components, flows, groupings, I/O)
Caption Sharpener transforms vague captions into precise visual specifications

Phase 1 -- Linear Planning:

Retriever selects the most relevant reference examples from a curated set of 13 methodology diagrams spanning agent/reasoning, vision/perception, generative/learning, and science/applications domains
Planner generates a detailed textual description of the target diagram via in-context learning from the retrieved examples
Stylist refines the description for visual aesthetics using NeurIPS-style guidelines (color palette, layout, typography)

Phase 2 -- Iterative Refinement:

Visualizer renders the description into an image
Critic evaluates the generated image against the source context and provides a revised description addressing any issues
Steps 4-5 repeat for a fixed number of iterations (default 3), or until the critic is satisfied (--auto)

Providers

PaperBanana supports multiple VLM and image generation providers:

Component	Provider	Model	Notes
VLM (planning, critique)	OpenAI	`gpt-5.2`	Default
Image Generation	OpenAI	`gpt-image-1.5`	Default
VLM	Atlas Cloud	`deepseek-ai/DeepSeek-V3-0324`	OpenAI-compatible chat endpoint
Image Generation	Atlas Cloud	`openai/gpt-image-2/text-to-image`	Async prediction API
VLM	Google Gemini	`gemini-2.5-flash`	Low cost
Image Generation	Google Gemini	`gemini-3-pro-image-preview`	$0.134/image (1K)
VLM / Image	OpenRouter	Any supported model	Flexible routing

Azure OpenAI / Foundry endpoints are auto-detected — set OPENAI_BASE_URL to your endpoint. Gemini-compatible gateways are also supported — set GOOGLE_BASE_URL when needed. Atlas Cloud uses ATLASCLOUD_BASE_URL=https://api.atlascloud.ai/v1 for chat and ATLASCLOUD_IMAGE_BASE_URL=https://api.atlascloud.ai/api/v1 for image generation.

Atlas Cloud official site: https://www.atlascloud.ai/?utm_source=github&utm_medium=link&utm_campaign=paperbanana

Recommended Atlas LLM models for ATLASCLOUD_VLM_MODEL:

deepseek-ai/DeepSeek-V3-0324 (default)
openai/gpt-4o
openai/gpt-4.1
google/gemini-2.5-flash
anthropic/claude-sonnet-4.5-20250929

These are stable, generally available models verified against the Atlas Cloud API. The full, always-current model pool (300+ models) is documented on Atlas Cloud's own docs — see https://www.atlascloud.ai/models — and any model id listed there can be passed via ATLASCLOUD_VLM_MODEL.

Recommended Atlas image models for ATLASCLOUD_IMAGE_MODEL:

openai/gpt-image-2/text-to-image
openai/gpt-image-2/edit
baidu/ERNIE-Image-Turbo/text-to-image
black-forest-labs/flux-dev
black-forest-labs/flux-schnell
qwen/qwen-image

CLI Reference

`paperbanan

Only the beginning is shown; read the rest in the repo

PaperBanana

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Design, images & audioi

Common questions

Alternatives

Figma Context MCP

Claude Real Video

Figma MCP Server

README from the maker

Atlas Cloud

Quick Start

Prerequisites

Step 1: Install

Docker

Step 2: Get Your API Key

Edit .env and add your API key:

OPENAI_API_KEY=your-key-here

GOOGLE_API_KEY=your-key-here

For Azure OpenAI / Foundry:

OPENAI_BASE_URL=https:// .openai.azure.com/openai/v1

Optional Gemini overrides:

GOOGLE_BASE_URL=https://your-gemini-proxy.example.com

GOOGLE_VLM_MODEL=gemini-2.5-flash

GOOGLE_IMAGE_MODEL=gemini-3-pro-image-preview

Step 3: Generate a Diagram

PaperBanana Studio (local web UI)

How It Works

Providers

CLI Reference

`paperbanan