Diagrams your agent
must prove rendered.

DesignCore turns a coordinate-free graph spec into a verified draw.io, Mermaid, or Excalidraw diagram. The model never places a shape. Nothing counts as finished until it has actually rendered.

The two rules

Judgment for the model, geometry for the machine

LLMs are good at structure and bad at pixels. DesignCore splits the job along exactly that line.

1 · The model never writes coordinates

x, y, width, height, position are rejected at parse time. All geometry comes from Graphviz layout.

2 · No render, no diagram

A missing backend raises an error naming its install command. A spec never quietly degrades into an unverified picture.

Verified, honestly defined

Verified means: compiled cleanly, every backend rendered, deterministic structural/density/geometry checks passed. Not visual taste — that stays human.

Formats

One spec, three destinations

Write the graph once. Render it wherever the document needs it.

FormatBest for
Excalidraw (default)Editable sketches and architecture drawings your team keeps iterating on
MermaidDiagrams inside markdown — renders natively on GitHub, diffs as text
draw.ioConventional architecture artifacts for orgs already living in drawio
Example

A real output, not a mockup

This diagram of DesignCore's own pipeline was produced by DesignCore: a coordinate-free spec, Graphviz geometry, Excalidraw render, lint-clean.

DesignCore pipeline diagram: spec and Graphviz feed an emitter, renderer, lint, and manifest
Install

Spec in, verified diagram out

Requires Python 3.11+, uv, and the render backends you use (designcore doctor names each install command).

# Install the CLI
uv tool install designcore

# Check which backends you have
designcore doctor

# Describe the graph — no coordinates, ever
designcore new system-context --kind context

# Compile, lay out, render, lint — or fail loudly
designcore render system-context

# Prove the manifest matches disk
designcore check
Agent skills included

Judgment layer ships as skills

Three ready-made agent skills carry the conventions so any AI CLI can drive the pipeline well: architecture-diagram, flow-diagram, and concept-sketch.