agentbrush 0.3.1

Image editing toolkit for AI agents. Background removal, compositing, text rendering, resizing, and validation.

AgentBrush

Image editing toolkit for AI agents. Background removal, compositing, text rendering, resizing, format conversion, and spec validation.

Install

pip install agentbrush

With AI image generation support:

pip install agentbrush[generate]

Quick Start

Python API

from agentbrush import (
    remove_background, resize_image, validate_design,
    smart_crop, extract_palette, diff_images, batch_process,
)

# Remove background (edge-based flood fill, safe for artwork)
result = remove_background("photo.png", "cutout.png", color="white")

# Resize for social media
result = resize_image("cutout.png", "og_image.png", width=1200, height=630, pad=True)

# Validate against a preset
result = validate_design("og_image.png", preset="social-og")
print(result.summary())

# Smart crop to content bounds
result = smart_crop("padded.png", "tight.png", padding=20)

# Extract dominant colors
result = extract_palette("photo.png", count=6)
print(result.metadata["colors"])  # [{r, g, b, hex, pct}, ...]

# Diff two images
result = diff_images("before.png", "after.png", "diff.png")
print(f"{result.metadata['changed_pct']}% changed")

# Batch process a directory
result = batch_process("./input/", "./output/", operation="crop", padding=10)

Every function returns a Result object:

result.success         # True if no errors
result.width           # Output width in px
result.height          # Output height in px
result.transparent_pct # Percentage of transparent pixels
result.warnings        # Non-fatal issues
result.errors          # Fatal issues
result.metadata        # Operation-specific data
result.summary()       # Human-readable string

CLI

# Background removal
agentbrush remove-bg input.png output.png --color white --smooth

# Green screen removal
agentbrush greenscreen input.png output.png --upscale 3 --halo-passes 20

# Border artifact cleanup
agentbrush border-cleanup input.png output.png --passes 15 --green-halo-passes 20

# Text rendering
agentbrush text input.png output.png "HELLO" --font mono --bold --size 72
agentbrush text new:1200x630 output.png "Title Text" --bold --center

# Compositing
agentbrush composite base.png art.png output.png --position 100,200
agentbrush composite paste-centered output.png --overlay art.png --canvas 1200x630 --fit

# Resize
agentbrush resize input.png output.png --width 1200 --height 630
agentbrush resize input.png output.png --scale 3.0
agentbrush resize input.png output.png --width 1080 --height 1080 --fit --pad

# Validate against presets
agentbrush validate check image.png --preset social-og
agentbrush validate check image.png --preset favicon
agentbrush validate check image.png --width 800 --height 600 --transparent
agentbrush validate compare source.png processed.png --max-loss 10

# Format conversion
agentbrush convert input.png output.jpg --quality 95
agentbrush convert input.png output.webp --quality 90

# AI image generation (requires openai package)
agentbrush generate --provider openai --prompt "cat coding" --output cat.png

# Smart crop (auto-detect content, crop tight)
agentbrush crop input.png output.png --padding 20
agentbrush crop input.png output.png --bg-color 255,255,255

# Color palette extraction
agentbrush palette input.png --format json --count 6
agentbrush palette input.png --format hex
agentbrush palette input.png --format text

# Image diff (before/after comparison)
agentbrush diff before.png after.png --output diff.png
agentbrush diff before.png after.png --output diff.png --threshold 20

# Batch processing
agentbrush batch ./input/ ./output/ --operation crop --padding 10
agentbrush batch ./input/ ./output/ --operation validate --preset sticker
agentbrush batch ./input/ ./output/ --operation resize --width 400 --height 400
agentbrush batch ./input/ ./output/ --operation remove-bg --color white

Exit codes: 0 = success, 1 = validation failure, 2 = input error.

Examples

Background Removal

Edge-based flood fill removes the background while preserving artwork that threshold-based tools destroy. The colorful owl below keeps every feather edge, outline, and sticker border intact — only the solid white background is removed:

agentbrush remove-bg owl.png cutout.png --color white --smooth

Green Screen Removal

Multi-pass pipeline handles fine fur, hair, and complex outlines. The arctic fox below has thousands of wispy fur strands at the boundary — flood fill + trapped patch sweep + 3× upscale + halo cleanup preserves them all:

agentbrush greenscreen fox.png cutout.png --upscale 3 --halo-passes 25
agentbrush border-cleanup cutout.png clean.png --green-halo-passes 25 --alpha-smooth

Text Rendering

Accurate Pillow-based text rendering — layer multiple text elements with different sizes, colors, and positions. No AI text mangling, every character pixel-perfect:

agentbrush text badge_bg.png step1.png "AGENTCON" --font mono --bold --size 96 --center
agentbrush text step1.png step2.png "2026" --font mono --bold --size 48 --color "120,80,255,255" --center
agentbrush text step2.png step3.png "SPEAKER" --font mono --bold --size 36 --color "255,200,50,255" --center
agentbrush text step3.png final.png "Dr. Agent Smith" --font mono --size 64 --center

Compositing

Combine cutouts, backgrounds, and text into finished assets. The owl cutout from step 1 is placed on a gradient background with text overlay — a complete workflow in three commands:

agentbrush composite gradient.png owl_cutout.png composed.png --position center --resize 500x500
agentbrush text composed.png final.png "AgentBrush" --font mono --bold --size 48 --color "0,220,255,255"

Resize & Validate

Resize to exact dimensions with letterbox padding, then validate against platform presets. Square badge → OG image in one command:

agentbrush resize badge.png og_image.png --width 1200 --height 630 --fit --pad --pad-color "20,15,60,255"
agentbrush validate check og_image.png --preset social-og
# [OK] Size: 1200x630px — preset: social-og ✓

Agent Skills

AgentBrush ships as an Agent Skills package. Copy skill/agent-brush/ into your project's .claude/skills/ directory:

cp -r skill/agent-brush/ .claude/skills/agent-brush/

Claude Code (and other compatible tools) will automatically discover the skill and use it when processing images.

Usage Without Install

The standalone scripts work directly from a git clone — no pip install needed:

git clone https://github.com/ultrathink-art/agentbrush.git
cd agentbrush

python skill/agent-brush/scripts/remove_bg.py input.png output.png --color black
python skill/agent-brush/scripts/validate.py check image.png --preset social-og
python skill/agent-brush/scripts/resize.py input.png output.png --width 1200 --height 630

Requirements: Python >= 3.10 and Pillow >= 12.1 (pip install 'Pillow>=12.1').

Modules

Module	Description	Key function
background	Edge-based flood fill bg removal	remove_background()
greenscreen	Multi-pass green screen pipeline	remove_greenscreen()
border	Border artifact erosion + halo cleanup	cleanup_border()
text	Pillow text rendering (accurate)	add_text(), render_text()
composite	Image layering + centering	composite(), paste_centered()
resize	Resize with fit/pad/scale modes	resize_image()
validate	Spec validation against presets	validate_design(), compare_images()
convert	Format conversion (PNG/JPEG/WEBP)	convert_image()
generate	AI image generation (optional)	generate_image()
crop	Smart crop to content bounds	smart_crop()
palette	Dominant color extraction	extract_palette()
diff	Visual image comparison	diff_images()
batch	Directory batch processing	batch_process()

Core Primitives

Low-level functions available for custom pipelines:

from agentbrush.core import (
    flood_fill_from_edges,   # BFS flood fill (4-conn or 8-conn)
    is_near_color,           # Color distance matching
    parse_color,             # Parse "black", "white", "R,G,B" strings
    smooth_edges,            # 1px edge feathering
    smooth_alpha_edges,      # Gaussian alpha blur (edges only)
    find_artwork_bounds,     # Opaque pixel bounding box
    crop_to_content,         # Crop to content with padding
    find_opaque_centroid,    # Center of mass for opaque region
    ensure_single_shape,     # Remove floating elements (8-connected BFS)
    count_components,        # Connected component count
    find_font,               # Cross-platform font discovery
)

Presets

General Purpose

Preset	Width	Height	Transparent	Use Case
social-og	1200	630	No	Open Graph / link previews
social-square	1080	1080	No	Instagram, social posts
social-story	1080	1920	No	Stories, reels, vertical
favicon	32	32	Yes	Browser favicon
icon-ios	1024	1024	No	iOS app icon
icon-android	512	512	Yes	Android app icon
thumbnail	400	400	-	Thumbnails, previews
banner	1920	480	-	Website/profile banners
avatar	256	256	No	Profile avatars

Custom & Domain-Specific

Define custom specs inline or use domain-specific presets (e.g., print-on-demand). See docs/presets/ for additional preset packs.

Why Edge-Based Flood Fill?

Threshold-based removal (magick -fuzz -transparent black) scans every pixel and removes anything "close enough" to the target color — including internal outlines, dark shadows, and fine details inside the artwork.

AgentBrush starts flood fill from image edges only. Interior pixels that happen to match the background color are never touched because flood fill can't reach them without crossing through the artwork.

Threshold-based:              Edge-based flood fill:
removes ALL dark pixels       removes ONLY edge-connected dark pixels
+-----------------+           +-----------------+
|                 |           |                 |
|    #########    |           |    #########    |
|   #         #   | <- loses  |   #*********#   | <- preserved!
|   #         #   |   detail  |   #*********#   |
|    #########    |           |    #########    |
|                 |           |                 |
+-----------------+           +-----------------+

Guides

Step-by-step pipeline walkthroughs:

• Social Media Images — OG images, thumbnails, avatars
• Background Removal — black bg, white bg, green screen techniques

Testing

pip install -e ".[dev]"
pytest tests/ -v

All tests use synthetic Pillow-generated fixtures (no production images).

Dependencies

• Required: Pillow >= 12.1
• Optional: openai >= 1.0 (for generate command)
• Dev: pytest >= 7.0, pytest-cov

Related Tools

Part of the Ultrathink Agent Suite:

• Agent Architect Kit — Multi-agent starter kit for Claude Code with role definitions and process docs
• Agent Cerebro — Long-term memory with semantic search for persistent agent knowledge
• Agent Orchestra — Task queue + orchestration CLI for spawning and managing agents

Built by an AI-run dev shop. Read how →

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License

MIT