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lexicalninja

paper-banana

by lexicalninja
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Python
Hybrid

Paper Banana — Proposal Image Generator

A multiagent AI pipeline for generating process diagrams and CONOPS visuals for government and defense proposals. Based on the Paper Banana framework (arXiv 2601.23265), adapted for proposal-domain aesthetics and distributed as an MCP server.

Architecture

User Input (proposal text + caption/intent)
        |
        v
[1] Classifier Agent      Determines: process diagram vs. CONOPS image
        |
        v
[2] Retriever Agent       Selects N most relevant examples from library
        |                 (library starts empty; grows via add_example)
        v
[3] Planner Agent         Synthesizes detailed visual description (few-shot)
        |
        v
[4] Stylist Agent         Applies proposal-specific aesthetic guidelines
        |
        v
[5] Visualizer Agent      Calls gemini-3-pro-image-preview -> generates PNG
        |  ^
        |  | (refined description, up to T=3 rounds)
        v  |
[6] Critic Agent          Evaluates image, produces refined description
        |
        v
    Final Image (PNG)

Pipeline Data Flow Diagram

Related MCP server: mmc-mcp

Models

Agent

Model

Role

Extractor

gemini-3.6-flash

Pulls the relevant section from a full document

Optimizer

gemini-3.6-flash

Enriches context + sharpens caption (2 concurrent calls)

Classifier

gemini-3.6-flash

Fast classification, no deep reasoning needed

Retriever

gemini-3.6-flash

Relevance scoring across examples

Planner

gemini-3.1-pro-preview

Best reasoning for synthesizing visual descriptions

Stylist

gemini-3.1-pro-preview

Creative + domain-aware aesthetic refinement

Visualizer

gemini-3-pro-image

Image generation

Critic

gemini-3.6-flash

Different model family from Planner/Stylist (anti-bias)

Prerequisites

Quick Start

Automatic (recommended)

git clone git@github.com:lexicalninja/paper-banana.git
cd paper-banana
./install.sh

The install script will:

  1. Install uv if needed

  2. Prompt for your GOOGLE_API_KEY

  3. Detect Claude Code and/or VS Code and configure them

  4. Optionally seed the example library

On Windows, use install.ps1 instead.

Manual — Claude Code

claude mcp add paper-banana -e GOOGLE_API_KEY=your-key -- \
  uvx --from "git+ssh://git@github.com/lexicalninja/paper-banana.git" paper-banana

Manual — VS Code

You can install at the user level (available in every workspace) or the workspace level (scoped to one project).

User-level — edit ~/Library/Application Support/Code/User/mcp.json (macOS) or ~/.config/Code/User/mcp.json (Linux) or %APPDATA%\Code\User\mcp.json (Windows). Add paper-banana inside the top-level servers object:

{
  "servers": {
    "paper-banana": {
      "command": "uvx",
      "args": ["--from", "git+ssh://git@github.com/lexicalninja/paper-banana.git", "paper-banana"],
      "env": { "GOOGLE_API_KEY": "your-key" }
    }
  }
}

Workspace-level — add the same block to .vscode/mcp.json in your project root. This is safe to commit so teammates get the server automatically.

Local Development

pip install -e .
export GOOGLE_API_KEY=your-key
paper-banana

After setup, the generate_proposal_image and add_example tools will appear in your MCP client.

MCP Tools

generate_proposal_image

Generate a proposal diagram image.

Parameter

Type

Default

Description

caption

string

required

Communicative intent for the diagram

source_context

string

""

Raw proposal text to diagram

source_file

string

""

Path to a document file; section specifies which section to extract

section

string

""

Section heading to extract from source_file

image_type

string

"auto"

"process", "conops", or "auto" (classifier decides)

iterations

integer

3

Max visualizer/critic refinement cycles (1–5)

output_path

string

"output.png"

Where to save the generated PNG

save_artifacts

boolean

true

Save per-iteration PNGs + critique JSON to {stem}_artifacts/

export_schema

boolean

true

Append structured diagram YAML/JSON to critique file

brand

string

"mlst"

Visual identity profile: "mlst" (purple/blue) or "default" (navy/gov)

aspect_ratio

string

""

Pin output dimensions: "16:9", "4:3", "1:1", "3:4", or "" (Planner decides)

resume_from

string

""

Path to a {stem}_run/ directory from a prior run to resume from

user_feedback

string

""

Freeform feedback about the prior output; overrides original intent in the critic

Returns the absolute path to the saved PNG.

Resuming a run

Each run saves state to {output_stem}_run/run_input.json. Pass that directory to resume_from to skip the full pipeline and iterate from where you left off:

generate_proposal_image(
  caption="",
  resume_from="output/my_diagram_run",
  user_feedback="Add a decision diamond after step 2 with yes/no branches",
  iterations=2
)

Use user_feedback to steer the next generation. The pipeline critiques the existing image first, bakes the feedback into a revised description, then generates.

Resume path data flow diagram

add_example

Add a reference image to the example library. The library starts empty; add examples over time to improve retrieval quality.

Parameter

Type

Description

description

string

Written description of what the image shows

image_path

string

Path to the image file

caption

string

Caption associated with the image

image_type

string

"process" or "conops"

Returns the unique ID assigned to the new example.

Cold Start

The library starts empty (examples/metadata.json contains []). On an empty library, the pipeline proceeds zero-shot (a warning is logged). Use add_example to build up a reference library over time. Retrieval quality improves noticeably after ~5 examples per diagram type.

Design Aesthetic

Two brand profiles ship with the server, selected via the brand parameter.

mlst (default) — MLST purple

Token

Hex

Usage

Brand purple

#73628A

Primary boxes, borders, headers, flow arrows

Muted purple

#9C8DAF

Secondary elements, highlights

Pale purple

#EAE8EE

Interior fill of content boxes

Blue

#3FA7D6

Data flows, supporting links

Coral

#FE5F55

Critical path, key decisions

Amber

#FAC05E

Alternate emphasis

Green

#59CD90

Confirmation states, approved paths

Off-white

#FAFAFA

Page background

Light gray

#EDEDED

Swimlanes, grouping zones

Near-black

#313131

Body text

default — government/defense

Token

Hex

Usage

Navy blue

#1B3A6B

Primary boxes, header bars, key actors

White

#FFFFFF

Text on dark backgrounds, box interiors

Gray

#6B7280

Connectors, borders, annotations

Light gray

#F3F4F6

Swimlane backgrounds, grouping zones

Deep orange

#C2410C

Decisions, critical path, highlights

Teal

#0D9488

Data flows, supporting systems, feedback paths

Running Directly

# Start the MCP server (used by VS Code / Claude Code)
python -m paper_banana.server

# Or via the installed entry point
paper-banana

Pipeline I/O

Pipeline I/O

Conncurrent Optimization

Concurrent Optimization Path

Run state persistence

State persistence diagram

Install Server
A
license - permissive license
A
quality
C
maintenance

How are these scores calculated?

Maintenance

–Maintainers
–Response time
–Release cycle
–Releases (12mo)
Commit activity

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