CADVERT

Turn CAD files into something an AI can actually read.

A STEP file is a boundary representation — dense surface equations and topology built for CAD kernels, not for language models. Paste one into an LLM and it chokes; show it a screenshot and the model can see the shape but can't tell you the wall is exactly 11.000 mm thick. In engineering, the number is the point.

CADVERT reads the CAD file directly and extracts the exact geometry the engineer authored — dimensions, holes, fillets, wall thicknesses, tolerances — as clean, structured text (and other representations) any model can reason over. Geometry is analytical, straight from the B-REP — never tessellated or approximated.

pip install cadvert

Quick start

import cadvert

result = cadvert.analyze("bracket.step")

print(result.to_text())     # LLM-ready structured document (the flagship)
result.to_dict()            # JSON-safe analysis for quoting / CI / DBs
result.to_json()
result.to_graph()           # networkx face-adjacency graph  (GNN / UV-Net)
result.to_points(2048)      # (N,3) surface point cloud       (PointNet / 3D-CNN)

Example to_text() output:

PART: bracket
[GLOBAL PROPERTIES]
Bodies: 1  |  Faces: 8  |  Edges: 18
Bbox: X[0,60]  Y[0,40]  Z[0,20]
[FEATURES]
  hole_1: THROUGH HOLE  d=8.000mm  depth=20.000mm  [5/16"]
  pattern_1: PATTERN  2× through hole  d=8.000mm
[SPATIAL RELATIONSHIPS]
  Wall thickness: hole_1 bore → outer surface = 11.000 mm

Supported formats

Representations

Optional extras

pip install cadvert[graph]   # networkx  → .to_graph()
pip install cadvert[mesh]    # trimesh   → area-weighted .to_points(), OBJ
pip install cadvert[dxf]     # ezdxf     → .dxf drawings
pip install cadvert[server]  # FastAPI REST server + web UI
pip install cadvert[llm]     # OpenAI semantic enrichment
pip install cadvert[full]    # everything

CLI

cadvert part.step -o out.hsd.txt      # convert to an HSD document
cadvert-server                        # REST API on http://localhost:8000

Building an AI CAD tool

Register analyze as a tool in your LLM framework and the model can answer questions about real parts using real dimensions:

def analyze_cad(file_path: str) -> str:
    return cadvert.analyze(file_path).to_text()

Requires Python 3.10+. Core install ships cadquery-ocp (OCCT bindings) and numpy — no other heavy dependencies.