PaperBanana 🍌

Hi everyone! The original version of PaperBanana is already open-sourced under Google-Research as PaperVizAgent. This repository forked the content of that repo and aims to keep evolving toward better support for academic paper illustration—though we have made solid progress, there is still a long way to go for more reliable generation and for more diverse, complex scenarios. PaperBanana is intended to be a fully open-source project dedicated to facilitating academic illustration for all researchers. Our goal is simply to benefit the community, so we currently have no plans to use it for commercial purposes.

Latest News

• 2026-09-23: 🎉 Congratulations to Xueqing Wu on releasing PaperBanana-Interact, a follow-up to PaperBanana that makes figure generation a multi-turn conversation, so you can iteratively refine figures with feedback. Check out the paper, code, data, demo and project page.
• 2026-04-30: PaperBanana was accepted to ICML 2026 as a Spotlight.
• 2026-03-24: PaperBanana is now hosted on Hugging Face Spaces. Many thanks to the Hugging Face team for their support.
• 2026-03-11: Published PaperBanana as a ClawHub skill — install with clawhub install paperbanana.
• 2026-03-11: Added model selection to Streamlit UI — now supports choosing both Main Model (VLM) and Image Generation Model, with preset options and custom input.
• 2026-03-11: Added OpenRouter support — use models from OpenAI, Anthropic, and other providers via a unified API.
• 2026-03-11: Added Contributors section with all-contributors bot support.

TODO List

• Add support for using manually selected examples. Provide a user-friendly interface.
• Upload code for generating statistical plots.
• Upload code for improving existing diagrams based on style guideline.
• Expand the reference set to support more areas beyond computer science.

PaperBanana is 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 PaperBanana:

Overview of PaperBanana

PaperBanana 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

Quick Start

Step1: Clone the Repo

git clone https://github.com/dwzhu-pku/PaperBanana.git
cd PaperBanana

Step2: Configuration

PaperBanana supports configuring API keys from a YAML configuration file or via environment variables.

We recommend 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. In model_config.yaml, remember to fill in the two model names (defaults.main_model_name and defaults.image_gen_model_name) and set at least one API key under api_keys—for example only google_api_key (Gemini), or only openrouter_api_key (OpenRouter). You do not need both; either one is enough. If both are configured, OpenRouter is preferred for routing when available.

Note that if you need to generate many candidates simultaneously, you will require an API key that supports high concurrency.

Step3: Installing the Environment

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
   

以上为 README 的开头部分。其余内容请阅读原文。 查看完整 README ↗