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<!-- mcp-name: io.github.llmsresearch/paperbanana -->
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<h1>PaperBanana</h1>
<p><strong>Automated Academic Illustration for AI Scientists</strong></p>
<p>
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---
> **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](https://arxiv.org/abs/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`
<p align="center">
<img src="assets/img/hero_image.png" alt="PaperBanana takes paper as input and provide diagram as output" style="max-width: 960px; width: 100%; height: auto;"/>
</p>
**Learning LLM concepts?** We also publish [211 free visual cards](https://github.com/llmsresearch/llm-flashcards)
on attention, RAG, agents, and inference.
[Read them online](https://llmsresearch.com/cards?utm_source=github&utm_medium=referral&utm_campaign=visual_library&utm_content=paperbanana-readme).
## Atlas Cloud
<p align="center">
<img src="assets/sponsors/atlas_cloud_logo.png" alt="Atlas Cloud Logo" width="180"/>
</p>
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](https://www.atlascloud.ai/console/coding-plan?utm_source=github&utm_medium=link&utm_campaign=paperbanana)
---
## Quick Start
> **Try it in your browser:** the
> [Colab quickstart notebook](https://colab.research.google.com/github/llmsresearch/paperbanana/blob/main/notebooks/PaperBanana_Colab_Quickstart.ipynb)
> 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](https://platform.openai.com/api-keys)) or Azure OpenAI / Foundry endpoint
- Or a Google Gemini API key (free, [Google AI Studio](https://makersuite.google.com/app/apikey))
### Step 1: Install
```bash
pip install paperbanana
```
Or install from source for development:
```bash
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:
```bash
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`:
```bash
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
```bash
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://<resource>.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:
```bash
paperbanana setup
```
### Step 3: Generate a Diagram
```bash
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:
```bash
paperbanana generate \
--input my_method.txt \
--caption "Overview of our encoder-decoder framework" \
--optimize --auto
```
Output is saved to `outputs/run_<timestamp>/final_output.png` along with all intermediate iterations and metadata.
### PaperBanana Studio (local web UI)
Install the optional Gradio dependency, then start the app:
```bash
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`):**
0. **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:**
1. **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
2. **Planner** generates a detailed textual description of the target diagram via in-context learning from the retrieved examples
3. **Stylist** refines the description for visual aesthetics using NeurIPS-style guidelines (color palette, layout, typography)
**Phase 2 -- Iterative Refinement:**
4. **Visualizer** renders the description into an image
5. **Critic** evaluates the generated image against the source context and provides a revised description addressing any issues
6. 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](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](https://www.atlascloud.ai/models?utm_source=github&utm_medium=link&utm_campaign=paperbanana) — 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
### `paperbanana generate` -- Methodology Diagrams
```bash
# Basic generation
paperbanana generate \
--input method.txt \
--caption "Overview of our framework"
# With input optimization and auto-refine
paperbanana generate \
--input method.txt \
--caption "Overview of our framework" \
--optimize --auto
# Continue the latest run with user feedback
paperbanana generate --continue \
--feedback "Make arrows thicker and colors more distinct"
# Continue a specific run
paperbanana generate --continue-run run_20260218_125448_e7b876 \
--iterations 3
# PDF as input (install PyMuPDF: pip install 'paperbanana[pdf]')
paperbanana generate \
--input paper.pdf \
--caption "Overview of our method" \
--pdf-pages "3-8"
# Guide generation with a reference/sketch image (repeatable)
paperbanana generate \
--input method.txt \
--caption "Overview of our framework" \
--image sketch.png --image prior_figure.png
```
| Flag | Short | Description |
|------|-------|-------------|
| `--input` | `-i` | Path to methodology text file or PDF (required for new runs) |
| `--caption` | `-c` | Figure caption / communicative intent (required for new runs) |
| `--image` | | Reference/sketch image (hand-drawn sketch, whiteboard photo, prior figure) that guides the Planner. Repeatable for multiple images |
| `--output` | `-o` | Output image path (default: auto-generated in `outputs/`) |
| `--iterations` | `-n` | Number of Visualizer-Critic refinement rounds (default: 3) |
| `--num-candidates` | `-k` | Generate N candidate images in parallel, 1-8 (default: 1). Planning runs once; refinement fans out per candidate with seed offsets. Outputs land in `candidates/cand_<i>/`; the run-root `final_output` is candidate 1. Cost estimates and `--budget` account for the fan-out |
| `--auto` | | Loop until critic is satisfied (with `--max-iterations` safety cap) |
| `--max-iterations` | | Safety cap for `--auto` mode (default: 30) |
| `--optimize` | | Preprocess inputs with parallel context enrichment and caption sharpening |
| `--continue` | | Continue from the latest run in `outputs/` |
| `--continue-run` | | Continue from a specific run ID |
| `--feedback` | | User feedback for the critic when continuing a run |
| `--pdf-pages` | | PDF input only: 1-based pages (e.g. `1-5`, `2,4,6-8`; default: all) |
| `--vlm-provider` | | VLM provider name (default: `openai`) |
| `--vlm-model` | | VLM model name (default: `gpt-5.2`) |
| `--image-provider` | | Image gen provider (default: `openai_imagen`) |
| `--image-model` | | Image gen model (default: `gpt-image-1.5`) |
| `--format` | `-f` | Output format: `png`, `jpeg`, or `webp` (default: `png`) |
| `--config` | | Path to YAML config file (see `configs/config.yaml`) |
| `--verbose` | `-v` | Show detailed agent progress and timing |
| `--progress-json` | | Emit JSON progress events to stdout during generation |
### `paperbanana plot` -- Statistical Plots
```bash
paperbanana plot \
--data results.csv \
--intent "Bar chart comparing model accuracy across benchmarks"
```
| Flag | Short | Description |
|------|-------|-------------|
| `--data` | `-d` | Path to data file, CSV or JSON (required) |
| `--intent` | | Communicative intent for the plot (required) |
| `--output` | `-o` | Output image path |
| `--iterations` | `-n` | Refinement iterations (default: 3) |
| `--vlm-provider` | | VLM provider name |
| `--vlm-model` | | VLM model name |
Plots are rendered via VLM-generated matplotlib code — no image-generation provider or credentials are required.
### `paperbanana venues` -- Custom Venue Style Packs
`--venue` selects a *venue style pack*: a directory with `methodology_style_guide.md`, `plot_style_guide.md`, and an optional `venue.yaml`. Built-in packs (`neurips`, `icml`, `acl`, `ieee`) ship with PaperBanana; you can add your own under `~/.config/paperbanana/venues/` (override with `--venue-dir` or `PAPERBANANA_VENUE_DIR`) without touching the repo:
```bash
# 1. Scaffold a pack (seeds both guides from the NeurIPS templates)
paperbanana venues init mylab
# 2. Edit the style guides — or generate them from a corpus of example figures:
# paperbanana guidelines synthesize --reference-set ./examples \
# --output ~/.config/paperbanana/venues/mylab/methodology_style_guide.md
# 3. Use it anywhere --venue is accepted
paperbanana generate --input method.txt --caption "Overview" --venue mylab
# See everything that's available (built-in + user, with source)
paperbanana venues list
```
`venue.yaml` (all fields optional):
```yaml
display_name: "My Lab Style" # shown by `paperbanana venues list`
aspect_ratio: "16:9" # default --aspect-ratio for this venue's runs
fonts: # preferred fonts, appended to the style guides
- "Helvetica"
```
On a name clash, built-in packs win — user packs cannot shadow built-in venues. Unknown venue names fail fast with the list of available packs from both sources.
### `paperbanana batch` -- Batch Generation
Generate multiple methodology diagrams from a single manifest file (YAML or JSON). Each item runs the full pipeline; outputs are written under `outputs/batch_<id>/run_<id>/` and a `batch_report.json` summarizes all runs.
```bash
paperbanana batch --manifest examples/batch_manifest.yaml --optimize
```
Manifest format (YAML or JSON with an `items` list):
```yaml
items:
- input: path/to/method1.txt
caption: "Overview of our encoder-decoder"
id: fig1
- input: method2.txt
caption: "Training pipeline"
id: fig2
- input: paper.pdf
caption: "System overview"
id: fig3
pdf_pages: "4-9" # optional; PDF inputs only
```
Paths in the manifest are resolved relative to the manifest file's directory.
**Composite figures:** Add an optional `composite` section to automatically stitch all generated panels into a single labeled figure after the batch completes:
```yaml
composite:
layout: "1x3" # rows x cols, or "auto"
labels: auto # (a), (b), (c)... or explicit list, or null
spacing: 20 # pixels between panels
label_position: bottom # top or bottom
output: "composite.png"
items:
- input: method_encoder.txt
caption: "Encoder architecture"
id: panel_a
# ...
```
The composite image is saved alongside the individual panels in the batch output directory. See `examples/composite_batch_manifest.yaml` for a complete example.
**Generate a human-readable report** from an existing batch run (Markdown or HTML):
```bash
paperbanana batch-report --batch-dir outputs/batch_20250109_123456_abc --format markdown
# or by batch ID (under default output dir)
paperbanana batch-report --batch-id batch_20250109_123456_abc --format html --output report.html
```
Diagram batch reports include `batch_kind: methodology`; plot batches use `batch_kind: statistical_plot`. Human-readable reports (`paperbanana batch-report`) show the batch kind when present.
**Sweep manifests** let you store the full sweep plan as YAML/JSON instead of eight comma-separated CLI flags. Mutually exclusive with the axis flags; see `examples/sweep_manifest.yaml`.
```bash
paperbanana sweep --manifest examples/sweep_manifest.yaml
```
**Sweep reports** produced by `paperbanana sweep` can be rendered the same way:
```bash
paperbanana sweep-report --sweep-dir outputs/sweep_20250109_123456_abc --format html
# or by sweep ID
paperbanana sweep-report --sweep-id sweep_20250109_123456_abc --format markdown
```
Rendered sweep reports include a summary, a top-5 ranked table, the full variants table (with per-variant provider/model, iterations, critic-suggestion count, proxy score, and output path), and the `quality_proxy_score` note. Dry-run reports render a simplified "Planned Variants" section.
| Flag | Short | Description |
|------|-------|-------------|
| `--manifest` | `-m` | Path to manifest file (required) |
| `--output-dir` | `-o` | Parent directory for batch run (default: outputs) |
| `--config` | | Path to config YAML |
| `--iterations` | `-n` | Refinement iterations per item |
| `--optimize` | | Preprocess inputs for each item |
| `--auto` | | Loop until critic satisfied per item |
| `--format` | `-f` | Output image format (png, jpeg, webp) |
| `--auto-download-data` | | Auto-download the PaperBananaBench reference set (~254 MB) if not cached |
### `paperbanana plot-batch` -- Batch Statistical Plots
Generate multiple plots from a manifest (YAML or JSON). Each item specifies a **data** file (CSV or JSON) and an **intent** string, mirroring `paperbanana plot`. Outputs live under `outputs/batch_<id>/run_<id>/` with the same `batch_report.json` and `paperbanana batch-report` workflow as diagram batches.
```bash
paperbanana plot-batch --manifest examples/plot_batch_manifest.yaml --optimize
```
Manifest format (`items` list):
```yaml
items:
- data: path/to/results.csv
intent: "Bar chart comparing accuracy across models"
id: fig_acc
- data: other.json
intent: "Scatter plot with trend line"
aspect_ratio: "16:9" # optional per item; CLI --aspect-ratio is the default when omitted
```
Paths are resolved relative to the manifest file’s directory.
| Flag | Short | Description |
|------|-------|-------------|
| `--manifest` | `-m` | Path to manifest (required) |
| `--output-dir` | `-o` | Parent directory for `batch_*` (default: outputs) |
| `--config` | | Path to config YAML |
| `--vlm-provider` | | VLM provider (default: gemini) |
| `--vlm-model` | | VLM model override |
| `--image-provider` | | Image gen provider |
| `--image-model` | | Image gen model |
| `--iterations` | `-n` | Refinement iterations per item |
| `--auto` | | Loop until critic satisfied per item |
| `--max-iterations` | | Safety cap for `--auto` |
| `--optimize` | | Input optimization per item |
| `--format` | `-f` | png, jpeg, or webp |
| `--save-prompts` / `--no-save-prompts` | | Persist prompts (default: on, same as `plot`) |
| `--venue` | | Venue style pack: built-in (neurips, icml, acl, ieee), a user pack, or `custom` |
| `--aspect-ratio` | `-ar` | Default aspect ratio when not set in the manifest |
| `--verbose` | `-v` | Verbose logging |
### `paperbanana orchestrate` -- Full-Paper Figure Package
Generate a publication-focused figure bundle from a full paper source, with optional data-driven plots. The command:
- parses the paper (`.txt`, `.md`, or `.pdf`)
- plans multiple methodology figures from section structure
- optionally discovers CSV/JSON files to plan statistical plots
- runs generation for all planned items
- writes a package folder containing `figure_package.json`, `figures/`, `figures.tex`, and `captions.md`
```bash
paperbanana orchestrate \
--paper paper.pdf \
--data-dir ./results \
--max-method-figures 4 \
--max-plot-figures 3 \
--optimize
```
Use `--dry-run` to only plan and inspect `orchestration_plan.json` without API calls.
Use `--resume-orchestrate <id-or-path>` to continue an interrupted orchestration from checkpoint state.
| Flag | Description |
|------|-------------|
| `--paper` / `-p` | Paper source path (`.txt`, `.md`, or `.pdf`) |
| `--resume-orchestrate` | Resume an existing orchestration by ID or directory |
| `--retry-failed` | When resuming, include previously failed tasks |
| `--max-retries` | Extra retries per task after first failure |
| `--data-dir` | Optional directory containing CSV/JSON files for plot planning |
| `--output-dir` / `-o` | Parent output directory (creates `orchestrate_*`) |
| `--max-method-figures` | Max methodology figures to plan/generate |
| `--max-plot-figures` | Max plot figures to plan/generate |
| `--pdf-pages` | PDF-only page selection (e.g. `1-5`, `2,4,6-8`) |
| `--optimize` | Enable input optimization for generated items |
| `--iterations` / `-n` | Refinement iterations per generated item |
| `--auto` + `--max-iterations` | Critic-driven auto-refine mode with safety cap |
| `--concurrency` | Parallel figure generation workers |
| `--format` / `-f` | Output format (`png`, `jpeg`, `webp`) |
| `--dry-run` | Plan package only; no generation calls |
### `paperbanana composite` -- Compose Multi-Panel Figures
Stitch multiple images into a single labeled figure with `(a)`, `(b)`, `(c)` sub-panel labels:
```bash
paperbanana composite \
panel_a.png panel_b.png panel_c.png \
--layout 1x3 \
--output figure2.png
```
| Flag | Short | Description |
|------|-------|-------------|
| `IMAGES` | | Positional: paths to images to compose |
| `--layout` | `-l` | Grid layout: `RxC` (e.g. `1x3`, `2x2`) or `auto` (default: auto) |
| `--labels` | | Comma-separated labels, or `none` to disable (default: auto `(a),(b),...`) |
| `--spacing` | `-s` | Pixel spacing between panels (default: 20) |
| `--label-position` | | `top` or `bottom` (default: bottom) |
| `--label-font-size` | | Font size for labels (default: 32) |
| `--output` | `-o` | Output path (default: composite_output.png) |
This command works on any existing images — no API calls needed. It is also triggered automatically when a batch manifest includes a `composite` section (see `paperbanana batch` above).
### `paperbanana evaluate` -- Quality Assessment
Comparative evaluation of a generated diagram against a human reference using VLM-as-a-Judge:
```bash
paperbanana evaluate \
--generated diagram.png \
--reference human_diagram.png \
--context method.txt \
--caption "Overview of our framework"
```
| Flag | Short | Description |
|------|-------|-------------|
| `--generated` | `-g` | Path to generated image (required) |
| `--reference` | `-r` | Path to human reference image (required) |
| `--context` | | Path to source context text file or PDF (required) |
| `--caption` | `-c` | Figure caption (required) |
| `--pdf-pages` | | PDF context only: 1-based page selection (default: all) |
Scores on 4 dimensions (hierarchical aggregation per the paper):
- **Primary**: Faithfulness, Readability
- **Secondary**: Conciseness, Aesthetics
### `paperbanana polish` -- Refine an Existing Figure
Bring your own figure: a VLM audits it against the venue style guide and proposes up to 10 concrete, actionable improvements, then an image-edit capable provider applies them to the original figure (guided edit). Suggestions are printed to the console so you can see exactly what changed.
```bash
paperbanana polish --input figure.png
paperbanana polish --input figure.png --venue icml --iterations 2 --output polished.png
```
| Flag | Short | Description |
|------|-------|-------------|
| `--input` | `-i` | Path to the existing figure image (required) |
| `--output` | `-o` | Output path (default: `outputs/polish_<timestamp>/final_output.png`) |
| `--venue` | | Venue style guide: `neurips` (default), `icml`, `acl`, `ieee`, `custom` |
| `--iterations` | `-n` | Polish rounds; each round suggests and applies improvements on the previous result (default: 1) |
| `--aspect-ratio` | `-ar` | Target aspect ratio (default: preserve the input figure's ratio) |
| `--num-candidates` | `-k` | Apply each round's suggestions N times in parallel (1-8) |
| `--budget` | | Budget cap in USD; polishing stops gracefully when exceeded |
| `--seed` | | Random seed for reproducible edits |
Requires an image provider that supports guided image edits (Google Gemini image models). If the figure already conforms to the style guide, polish exits without making changes.
### `paperbanana studio` -- Local web UI
Requires `pip install 'paperbanana[studio]'` (Gradio).
```bash
paperbanana studio
paperbanana studio --port 8080 --output-dir ./my_outputs
```
| Flag | Description |
|------|-------------|
| `--host` | Bind address (default `127.0.0.1`) |
| `--port` | Port (default `7860`) |
| `--share` | Create a temporary public Gradio link (do not use with sensitive data) |
| `--config` | Path to YAML config |
| `--output-dir` / `-o` | Default output directory for runs |
| `--root-path` | URL subpath when behind a reverse proxy |
### `paperbanana setup` -- First-Time Configuration
```bash
paperbanana setup
```
Interactive wizard that first asks whether to use the official Gemini API.
If you choose official API, it follows the default AI Studio key flow; if not, it asks for a custom Gemini-compatible URL and API key.
### `paperbanana data` -- Reference Dataset
```bash
# Download the PaperBananaBench reference set (~254 MB, one command)
paperbanana data download
# Import plot references too (or both)
paperbanana data download --task plot
paperbanana data download --task both
# Inspect / clear the cache
paperbanana data info
paperbanana data clear
```
The dataset is served from a project-hosted GitHub release mirror
([`bench-data-v1`](https://github.com/llmsresearch/paperbanana/releases/tag/bench-data-v1))
and its SHA256 checksum is verified before extraction. Credit to the
[PaperBananaBench](https://huggingface.co/datasets/dwzhu/PaperBananaBench) authors —
the mirror tracks their 2026-03-22 revision. The set is cached under
`~/.cache/paperbanana/` (override with `PAPERBANANA_CACHE_DIR`); generation
commands can also fetch it on first use via `--auto-download-data`.
---
## Python API
```python
import asyncio
from paperbanana import PaperBananaPipeline, GenerationInput, DiagramType
from paperbanana.core.config import Settings
settings = Settings(
vlm_provider="openai",
vlm_model="gpt-5.2",
image_provider="openai_imagen",
image_model="gpt-image-1.5",
optimize_inputs=True, # Enable input optimization
auto_refine=True, # Loop until critic is satisfied
)
pipeline = PaperBananaPipeline(settings=settings)
result = asyncio.run(pipeline.generate(
GenerationInput(
source_context="Our framework consists of...",
communicative_intent="Overview of the proposed method.",
diagram_type=DiagramType.METHODOLOGY,
)
))
print(f"Output: {result.image_path}")
```
**Progress callbacks:** `generate()` and `continue_run()` accept an optional `progress_callback` argument. The pipeline invokes it with `PipelineProgressEvent` objects (stage, message, seconds, iteration, extra) at each step (optimizer, retriever, planner, stylist, visualizer, critic), so you can show progress in UIs or log timing without patching agents.
To continue a previous run:
```python
from paperbanana.core.resume import load_resume_state
state = load_resume_state("outputs", "run_20260218_125448_e7b876")
result = asyncio.run(pipeline.continue_run(
resume_state=state,
additional_iterations=3,
user_feedback="Make the encoder block more prominent",
))
```
See `examples/generate_diagram.py` and `examples/generate_plot.py` for complete working examples.
---
## MCP Server
PaperBanana includes an MCP server for use with Claude Code, Cursor, or any MCP-compatible client. Add the following config to use it via `uvx` without a local clone:
```json
{
"mcpServers": {
"paperbanana": {
"command": "uvx",
"args": ["--from", "paperbanana[mcp]", "paperbanana-mcp"],
"env": { "GOOGLE_API_KEY": "your-google-api-key" }
}
}
}
```
Eleven MCP tools are exposed: `generate_diagram`, `generate_plot`, `continue_run` (resume a prior `run_*` with optional feedback), `continue_diagram`, `continue_plot`, `evaluate_diagram`, `evaluate_plot`, `orchestrate_figures` (full-paper figure packages), `batch_diagrams`, `batch_plots`, and `download_references`.
The repo also ships with 3 Claude Code skills:
- `/generate-diagram <file> [caption]` - generate a methodology diagram from a text file
- `/generate-plot <data-file> [intent]` - generate a statistical plot from CSV/JSON data
- `/evaluate-diagram <generated> <reference>` - evaluate a diagram against a human reference
See [`mcp_server/README.md`](mcp_server/README.md) for full setup details (Claude Code, Cursor, local development).
---
## Overleaf Integration (GitHub Action)
Keep your paper's methodology figure in sync with the text — automatically. PaperBanana ships a GitHub Action that pairs with Overleaf's built-in GitHub sync: push your `.tex` changes, the action extracts the methodology section, generates the figure, and commits back the image plus a ready-to-`\input` LaTeX snippet. Pull in Overleaf and it's in your file tree.
```yaml
- uses: actions/checkout@v4
- uses: llmsresearch/paperbanana/integrations/github-action@main
with:
tex-file: sections/method.tex
caption: "Overview of our proposed framework"
env:
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
```
See [`integrations/github-action/README.md`](integrations/github-action/README.md) for the full workflow, all inputs, and cost-control options.
---
## Configuration
Default settings are in `configs/config.yaml`. Override via CLI flags or a custom YAML:
```bash
paperbanana generate \
--input method.txt \
--caption "Overview" \
--config my_config.yaml
```
Key settings:
```yaml
vlm:
provider: openai # openai, atlas, gemini, or openrouter
model: gpt-5.2
image:
provider: openai_imagen # openai_imagen, atlas_imagen, google_imagen, or openrouter_imagen
model: gpt-image-1.5
pipeline:
num_retrieval_examples: 10
refinement_iterations: 3
# auto_refine: true # Loop until critic is satisfied
# max_iterations: 30 # Safety cap for auto_refine mode
# optimize_inputs: true # Preprocess inputs for better generation
output_resolution: "2k"
reference:
path: data/reference_sets
output:
dir: outputs
save_iterations: true
save_metadata: true
```
Environment variables (`.env`):
```bash
# OpenAI (default)
OPENAI_API_KEY=your-key
OPENAI_BASE_URL=https://api.openai.com/v1 # or Azure endpoint
OPENAI_VLM_MODEL=gpt-5.2 # override model
OPENAI_IMAGE_MODEL=gpt-image-1.5 # override model
# Atlas Cloud
ATLASCLOUD_API_KEY=your-key
ATLASCLOUD_BASE_URL=https://api.atlascloud.ai/v1
ATLASCLOUD_VLM_MODEL=deepseek-ai/DeepSeek-V3-0324
ATLASCLOUD_IMAGE_BASE_URL=https://api.atlascloud.ai/api/v1
ATLASCLOUD_IMAGE_MODEL=openai/gpt-image-2/text-to-image
# Google Gemini (alternative, free)
GOOGLE_API_KEY=your-key
GOOGLE_BASE_URL= # optional custom Gemini-compatible endpoint
GOOGLE_VLM_MODEL=gemini-2.5-flash # override Gemini VLM model
GOOGLE_IMAGE_MODEL=gemini-3-pro-image-preview # override Gemini image model
```
---
## Project Structure
```
paperbanana/
├── paperbanana/
│ ├── core/ # Pipeline orchestration, types, config, resume, utilities
│ ├── agents/ # Optimizer, Retriever, Planner, Stylist, Visualizer, Critic
│ ├── providers/ # VLM and image gen provider implementations
│ │ ├── vlm/ # OpenAI, Atlas Cloud, Gemini, OpenRouter VLM providers
│ │ └── image_gen/ # OpenAI, Atlas Cloud, Gemini, OpenRouter image gen providers
│ ├── reference/ # Reference set management (13 curated examples)
│ ├── guidelines/ # Style guidelines loader
│ └── evaluation/ # VLM-as-Judge evaluation system
├── configs/ # YAML configuration files
├── prompts/ # Prompt templates for all agents + evaluation
│ ├── diagram/ # context_enricher, caption_sharpener, retriever, planner, stylist, visualizer, critic
│ ├── plot/ # plot-specific prompt variants
│ └── evaluation/ # faithfulness, conciseness, readability, aesthetics
├── data/
│ ├── reference_sets/ # 13 verified methodology diagrams
│ └── guidelines/ # NeurIPS-style aesthetic guidelines
├── examples/ # Working example scripts + sample inputs
├── scripts/ # Data curation and build scripts
├── tests/ # Test suite
├── mcp_server/ # MCP server for IDE integration
└── .claude/skills/ # Claude Code skills (generate-diagram, generate-plot, evaluate-diagram)
```
## Development
```bash
# Install with dev dependencies
pip install -e ".[dev,openai,google]"
# Run tests
pytest tests/ -v
# Lint
ruff check paperbanana/ mcp_server/ tests/ scripts/
# Format
ruff format paperbanana/ mcp_server/ tests/ scripts/
```
## Citation
This is an **unofficial** implementation. If you use this work, please cite the **original paper**:
```bibtex
@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}
}
```
**Original paper**: [https://arxiv.org/abs/2601.23265](https://arxiv.org/abs/2601.23265)
## Disclaimer
This project is an independent open-source reimplementation based on the publicly available paper.
It is not affiliated with, endorsed by, or connected to the original authors, Google Research, or
Peking University in any way. The implementation may differ from the original system described in the paper.
Use at your own discretion.
## License
MIT
