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CAD Agent

@clawd-maf

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CAD Agent

Give your AI agent eyes for CAD work.

Description

CAD Agent is a rendering server that lets AI agents see what they're building. Send modeling commands → receive rendered images → iterate visually.

Use when: designing 3D-printable parts, parametric CAD, mechanical design, build123d modeling

Architecture

Critical: All CAD logic runs inside the container. You (the agent) only:

  1. Send commands via HTTP
  2. View the returned images
  3. Decide what to do next
YOU (agent)                     CAD AGENT CONTAINER
─────────────                   ───────────────────
Send build123d code      →      Executes modeling
                         ←      Returns JSON status
Request render           →      VTK renders the model
                         ←      Returns PNG image
*Look at the image*
Decide: iterate or done

Never do STL manipulation, mesh processing, or rendering outside the container. The container handles everything — you just command and observe.

Setup

1. Clone the Repository

git clone https://github.com/clawd-maf/cad-agent.git
cd cad-agent

2. Build the Docker Image

docker build -t cad-agent:latest .

Or using docker-compose:

docker-compose build

3. Run the Server

# Using docker-compose (recommended)
docker-compose up -d

# Or using docker directly
docker run -d --name cad-agent -p 8123:8123 cad-agent:latest serve

4. Verify Installation

curl http://localhost:8123/health
# Should return: {"status": "healthy", ...}

Docker-in-Docker caveat: In nested container environments (e.g., Clawdbot sandbox), host networking may not work—curl localhost:8123 will fail even though the server binds to 0.0.0.0:8123. Use docker exec cad-agent python3 -c "..." commands instead. On a normal Docker host, localhost access works fine.

Workflow

1. Create Model

curl -X POST http://localhost:8123/model/create \
  -H "Content-Type: application/json" \
  -d '{
    "name": "my_part",
    "code": "from build123d import *\nresult = Box(60, 40, 30)"
  }'

2. Render & View

# Get multi-view (front/right/top/iso)
curl -X POST http://localhost:8123/render/multiview \
  -d '{"model_name": "my_part"}' -o views.png

# Or 3D isometric
curl -X POST http://localhost:8123/render/3d \
  -d '{"model_name": "my_part", "view": "isometric"}' -o iso.png

Look at the image. Does it look right? If not, modify and re-render.

3. Iterate

curl -X POST http://localhost:8123/model/modify \
  -d '{
    "name": "my_part", 
    "code": "result = result - Cylinder(5, 50).locate(Pos(20, 10, 0))"
  }'

# Re-render to check
curl -X POST http://localhost:8123/render/3d \
  -d '{"model_name": "my_part"}' -o updated.png

4. Export

curl -X POST http://localhost:8123/export \
  -d '{"model_name": "my_part", "format": "stl"}' -o part.stl

Endpoints

EndpointWhat it does
POST /model/createRun build123d code, create model
POST /model/modifyModify existing model
GET /model/listList models in session
GET /model/{name}/measureGet dimensions
POST /render/3d3D shaded render (VTK)
POST /render/2d2D technical drawing
POST /render/multiview4-view composite
POST /exportExport STL/STEP/3MF
POST /analyze/printabilityCheck if printable

build123d Cheatsheet

from build123d import *

# Primitives
Box(width, depth, height)
Cylinder(radius, height)
Sphere(radius)

# Boolean
a + b   # union
a - b   # subtract
a & b   # intersect

# Position
part.locate(Pos(x, y, z))
part.rotate(Axis.Z, 45)

# Edges
fillet(part.edges(), radius)
chamfer(part.edges(), length)

Important

  • Don't bypass the container. No matplotlib, no external STL libraries, no mesh hacking.
  • Renders are your eyes. Always request a render after changes.
  • Iterate visually. The whole point is you can see what you're building.

Design File Safety

The project has safeguards against accidentally committing CAD outputs:

  • .gitignore blocks *.stl, *.step, *.3mf, etc.
  • Pre-commit hook rejects design files
  • User's designs stay local, never versioned

Links

Source

git clone https://clawhub.ai/clawd-maf/cad-agentView on GitHub

Overview

CAD Agent is a rendering server that lets AI agents see what they're building. You send build123d modeling commands over HTTP, receive rendered images, and iterate visually inside a containerized CAD environment.

How This Skill Works

All CAD logic runs inside the container. You send model creation and modification requests via HTTP to the CAD Agent, which executes the build123d code, renders images with VTK, and returns status and PNGs. You view the images and decide what to modify next.

When to Use It

  • Designing 3D-printable parts
  • Parametric CAD workflows
  • Mechanical design iteration
  • Build123d modeling in isolation
  • Visual verification before export

Quick Start

  1. Step 1: Clone the repository and build the Docker image
  2. Step 2: Run the server with docker-compose up -d or docker run
  3. Step 3: Create a model with code, then render and iterate using /render and /model/modify

Best Practices

  • Always render after changes to visually verify results
  • Keep all CAD logic and rendering inside the container
  • Use /model/modify for incremental updates rather than full rebuild
  • Validate dimensions with /model/{name}/measure before exporting
  • Start with a simple primitive (e.g., Box) to test the pipeline

Example Use Cases

  • Create a 60x40x30 box as the starting model
  • Subtract a Cylinder(5, 50) at Pos(20, 10, 0) to create a hole
  • Combine parts with a + b boolean operation
  • Place and rotate: part.locate(Pos(x,y,z)); part.rotate(Axis.Z, angle)
  • Export STL after validation via the /export endpoint

Frequently Asked Questions

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