Note: This framework, introduced in the paper "PaperBanana", has been renamed to PaperVizAgent.

This repository is the official implementation for PaperVizAgent (widely known as PaperBanana), a reference-driven multi-agent framework for automated academic illustration generation. Acting like a creative team of specialized agents, it transforms raw scientific content into publication-quality diagrams and plots through an orchestrated pipeline of Retriever, Planner, Stylist, Visualizer, and Critic agents. The framework leverages in-context learning from reference examples and iterative refinement to produce aesthetically pleasing and semantically accurate scientific illustrations.

Here are some example diagrams and plots generated by PaperVizAgent (PaperBanana):

Originally published as PaperBanana, PaperVizAgent achieves high-quality academic illustration generation by orchestrating five specialized agents in a structured pipeline:

1. Retriever Agent: Identifies the most relevant reference diagrams from a curated collection to guide downstream agents
2. Planner Agent: Translates method content and communicative intent into comprehensive textual descriptions using in-context learning
3. Stylist Agent: Refines descriptions to adhere to academic aesthetic standards using automatically synthesized style guidelines
4. Visualizer Agent: Transforms textual descriptions into visual outputs using state-of-the-art image generation models
5. Critic Agent: Forms a closed-loop refinement mechanism with the Visualizer through multi-round iterative improvements

git clone [your-repo-url]
cd PaperVizAgent

PaperVizAgent supports configuring API keys and Google Cloud settings via environment variables OR a YAML configuration file. You can duplicate the configs/model_config.template.yaml file into configs/model_config.yaml to externalize all user configurations. This file is ignored by git to keep your api keys and configurations secret.

PaperBananaBench dataset will be released shortly. Once available, you will place it under the data directory (e.g., data/PaperBananaBench/). The framework is designed to function gracefully without the dataset by bypassing the Retriever Agent's few-shot learning capability.

1. We use uv to manage Python packages. Please install uv following the instructions here.

2. Create and activate a virtual environment

   uv venv # This will create a virtual environment in the current directory, under .venv/
   source .venv/bin/activate  # or .venv\Scripts\activate on Windows

3. Install python 3.12

   uv python install 3.12

4. Install required packages

   uv pip install -r requirements.txt

5. Set up API Keys

   export GOOGLE_API_KEY="your_google_api_key" # 
   export ANTHROPIC_API_KEY="your_anthropic_api_key"
   export OPENAI_API_KEY="your_openai_api_key"

The easiest way to launch PaperVizAgent is via the interactive Streamlit demo:

streamlit run demo.py

The web interface provides two main workflows:

1. Generate Candidates Tab:

• Paste your method section content (Markdown recommended) and provide the figure caption.
• Configure settings (pipeline mode, retrieval setting, number of candidates, aspect ratio, critic rounds).
• Click "Generate Candidates" and wait for parallel processing.
• View results in a grid with evolution timelines and download individual images or batch ZIP.

2. Refine Image Tab:

• Upload a generated candidate or any diagram.
• Describe desired changes or request upscaling.
• Select resolution (2K/4K) and aspect ratio.
• Download the refined high-resolution output.

You can also run PaperVizAgent from the command line:

# Basic usage with default settings
python main.py

# Advanced usage with custom settings
python main.py \
  --dataset_name "PaperBananaBench" \
  --task_name "diagram" \
  --split_name "test" \
  --exp_mode "dev_full" \
  --retrieval_setting "auto"

Available Options:

• --dataset_name: Dataset to use (default: PaperBananaBench)
• --task_name: Task type - diagram or plot (default: diagram)
• --split_name: Dataset split (default: test)
• --exp_mode: Experiment mode (see section below)
• --retrieval_setting: Retrieval strategy - auto, manual, random, or none (default: auto)

Experiment Modes:

• vanilla: Direct generation without planning or refinement
• dev_planner: Planner → Visualizer only
• dev_planner_stylist: Planner → Stylist → Visualizer
• dev_planner_critic: Planner → Visualizer → Critic (multi-round)
• dev_full: Full pipeline with all agents
• demo_planner_critic: Demo mode (Planner → Visualizer → Critic) without evaluation
• demo_full: Demo mode (full pipeline) without evaluation

View pipeline evolution and intermediate results:

streamlit run visualize/show_pipeline_evolution.py

View evaluation results:

streamlit run visualize/show_referenced_eval.py

├── .venv/
│   └── ...
├── data/
│   └── PaperBananaBench/
│       ├── diagram/
│       │   ├── images/
│       │   ├── pdfs/
│       │   ├── test.json
│       │   └── ref.json
│       └── plot/
├── agents/
│   ├── __init__.py
│   ├── base_agent.py
│   ├── retriever_agent.py
│   ├── planner_agent.py
│   ├── stylist_agent.py
│   ├── visualizer_agent.py
│   ├── critic_agent.py
│   ├── vanilla_agent.py
│   └── polish_agent.py
├── prompts/
│   ├── __init__.py
│   ├── diagram_eval_prompts.py
│   └── plot_eval_prompts.py
├── style_guides/
│   ├── generate_category_style_guide.py
│   └── ...
├── utils/
│   ├── __init__.py
│   ├── config.py
│   ├── paperviz_processor.py
│   ├── eval_toolkits.py
│   ├── generation_utils.py
│   └── image_utils.py
├── visualize/
│   ├── show_pipeline_evolution.py
│   └── show_referenced_eval.py
├── scripts/
│   ├── run_main.sh
│   ├── run_demo.sh
├── configs/
│   └── model_config.template.yaml
├── results/
│   ├── PaperBananaBench_diagram/
│   └── parallel_demo/
├── main.py
├── demo.py
└── README.md

• Reference-Driven: Learns from curated examples through generative retrieval
• Iterative Refinement: Critic-Visualizer loop for progressive quality improvement
• Style-Aware: Automatically synthesized aesthetic guidelines ensure academic quality
• Flexible Modes: Multiple experiment modes for different use cases

• Parallel Generation: Generate up to 20 candidate diagrams simultaneously
• Pipeline Visualization: Track the evolution through Planner → Stylist → Critic stages
• High-Resolution Refinement: Upscale to 2K/4K using Image Generation APIs
• Batch Export: Download all candidates as PNG or ZIP

• Modular Agents: Each agent is independently configurable
• Task Support: Handles both conceptual diagrams and data plots
• Evaluation Framework: Built-in evaluation against ground truth with multiple metrics
• Async Processing: Efficient batch processing with configurable concurrency

If you find this repo helpful, please cite our paper as follows:

@article{zhu2026paperbanana,
  title={PaperBanana: Automating Academic Illustration for AI Scientists},
  author={Zhu, Dawei and Meng, Rui and Song, Yale and Wei, Xiyu and Li, Sujian and Pfister, Tomas and Yoon, Jinsung},
  journal={arXiv preprint arXiv:2601.23265},
  year={2026}
}

This is not an officially supported Google product. This project is not eligible for the Google Open Source Software Vulnerability Rewards Program.

This project is intended for demonstration purposes only. It is not intended for use in a production environment.