MAC — Multi-Agent CAD for Parts and Assemblies

Generate editable CAD parts and multi-part assemblies from natural language.

简体中文

MAC turns an ordinary design brief into editable engineering geometry. It can generate a single printable part or decompose a mechanism into separately generated components, assemble them, validate the result, and export the artifacts needed for downstream inspection and simulation workflows.

MAC was originally released as a single-part Text-to-CAD workflow and has since received nearly 1,000 GitHub stars. This release preserves that workflow while adding visual verification, an independent Judge, and experimental multi-part assembly generation.

Assembly Gallery (Technology Preview)

Building on the original single-part workflow, MAC v2 introduces experimental natural-language assembly generation. The examples below are curated outputs, not a measured prompt-success rate, and the assembly workflow is not presented as outperforming CAD-agent skills. Validate generated geometry and joints before manufacturing or simulation use.

Complex assemblies

| Three-Axis Gantry Metrology Cell | Telescopic Cinema Robot Crane | |---|---| | A bridge-style inspection platform with three orthogonal motion stages. | A multi-stage crane with an articulated support and telescopic payload arm. | | | |

| Heavy-Duty Mobile Manipulator | Advanced Vision Inspection Robot Arm | |---|---| | A mobile base carrying a visually detailed articulated manipulator. | A multi-joint inspection arm with a framed vision payload. | | | |

Complex-gallery generation usage

| Assembly | Planning & mating | Part generation & repair | Judge & other workflow | Total tokens | Estimated cost | |---|---:|---:|---:|---:|---:| | Three-Axis Gantry Metrology Cell | about 178,000 | 461,811 | 260,157 | about 900,000 | about $2 | | Telescopic Cinema Robot Crane | 535,407 | 663,427 | about 451,000 | about 1,650,000 | about $3 | | Heavy-Duty Mobile Manipulator | about 520,000 | 2,678,005 | 2,602,142 | about 5,800,000 | about $13 | | Advanced Vision Inspection Robot Arm | 85,016 | 160,367 | about 55,000 | about 300,000 | about $1 |

Compact functional assemblies

| Hinged Twin-Claw Gripper | Guided Linear Plunger | Rotary Fork-Key Tool | Reusable Five-Digit Hand | |---|---|---|---| | | | | |

These curated compact examples exercise natural-language decomposition, joint interfaces, repeated-part reuse, and mirrored geometry. The complex gallery shows how the same workflow scales to larger visual assemblies. See the assembly workflow documentation for implementation details and current limitations.

URDF Simulation

The assembly workflow can export generated mechanisms as URDF files for downstream robotics and physics simulation. The demonstrations below use an AI-generated hand assembly to execute object rotation and pick-and-place tasks in simulation.

| Object Rotation | Pick and Place | |---|---| | | |

Part Gallery

MAC also generates standalone mechanical and creative parts. The examples below are editable CAD results, not image-only generations.

| Honeycomb Organizer | Gyroscope Ornament | Lighthouse | Smartphone Stand | Ball-in-Cage | |---|---|---|---|---| | | | | | |

| Articulable Gyroscope | Multi-Link Chain | Geneva Mechanism | Plasma Reactor | Brake Disc | |---|---|---|---|---| | | | | | |

The single-part workflow documentation covers configuration, execution, caching, and QA. Benchmark prompts and per-model data are linked from its benchmark-details section.

Real-World Results

The printed collection above includes benchmark parts and original showcase models generated by the single-part workflow. MAC can also produce print-in-place mechanisms containing separate bodies and functional clearances:

<p align="center"> <img src="assets/parts/articulable.gif" width="520" alt="Printed articulable MAC models"> </p>

Single-Part Benchmark

The following numbers apply specifically to the documented ten-prompt, 141-feature single-part-workflow benchmark. They are not assembly success-rate or assembly-cost claims.

| Workflow | Model | In-loop verification | Tokens | Estimated cost | Feature pass rate | |---|---|---|---:|---:|---:| | CAD Skill reproduction | Qwen 3.7 | — | 103.95M | $18.59 | 138/141 (97.9%) | | CAD Skill reproduction | Qwen 3.8 | Visual | 87.46M | $29.45 | 132/141 (93.6%) | | MAC v1 | Qwen 3.7 | Geometric | 0.90M | $1.43 | 140/141 (99.3%) | | MAC v2 | Qwen 3.8 | Visual + geometric | 1.40M | $4.85 | 140/141 (99.3%) |

Under the Qwen 3.7 setup, MAC v1 used 116× fewer recorded tokens and had a 13× lower estimated cost than the reproduced Skill baseline. Under Qwen 3.8 with visual verification, MAC v2 used 62.7× fewer recorded tokens and had a 6.1× lower estimated cost.

MAC v1 refers to the original single-part workflow and its published benchmark run. MAC v2 refers to the updated single-part workflow with visual verification and the independent Judge. Assembly generation is presented separately as a technology preview and is not included in these benchmark figures.

Across the two documented model configurations, MAC retained a 140/141 feature pass rate while using substantially fewer recorded tokens and lower estimated cost than the corresponding reproduced Skill runs.

Methodology and raw breakdowns: single-part README, English evaluation, and Chinese evaluation.

Why MAC?

• Natural-language input — describe geometry, dimensions, interfaces, and motion without writing CAD code or an internal schema.
• Parts and assemblies — use one project for standalone printable parts and articulated, multi-component mechanisms.
• Editable engineering outputs — export STEP, STL, and GLB rather than a render-only result; assemblies also provide a URDF handoff.
• Auditable generation — inspect structured briefs, geometry plans, generated Python, measurements, QA reports, and repair history.
• Automatic execution and repair — generated CAD is executed and checked, with bounded feedback loops for recoverable failures.
• Reuse instead of regeneration — repeated and mirrored components can be derived from one source geometry.
• Model-flexible stages — configure different OpenAI-compatible models for planning, geometry, coding, and repair.
• Visual workflow — the single-part pipeline includes a browser UI with 3D preview and downloadable results.
• Built on a proven workflow — MAC v2 extends the original nearly 1,000-star single-part pipeline with visual verification and assembly generation while retaining the existing part-generation workflow.

Quick Start

Install

git clone https://github.com/Pan-Chera/Multi-Agent-CAD.git
cd Multi-Agent-CAD
conda env create -f environment.yml
conda activate multi_agent_cad
pip install --no-deps "aider-chat==0.82.3"
export DASHSCOPE_API_KEY="your-key"

The final pip install is needed because aider-chat pins NumPy 1.x, which conflicts with build123d's NumPy 2.x requirement. It is therefore omitted from environment.yml; --no-deps avoids replacing the working NumPy version.

The default configuration targets an OpenAI-compatible DashScope endpoint. Other providers are covered in the single-part configuration guide.

pip users (no conda): aider-chat pins numpy==1.26.4, but build123d>=0.8 requires numpy>=2,<3 — these conflict in pure pip. Use this workaround (verified on macOS arm64 + Python 3.11):

python3.11 -m venv .venv
source .venv/bin/activate          # Windows PowerShell: .venv\Scripts\activate
pip install --upgrade pip
# Install aider first (pulls numpy 1.26.4 + transitive deps), then force-upgrade numpy.
# Verified: aider 0.82.3 imports cleanly on numpy 2.x — the pin is over-cautious upstream.
pip install "aider-chat==0.82.3"
pip install --no-deps --force-reinstall "numpy>=2,<2.3"
pip install "build123d>=0.8" "langgraph>=0.2,<0.3" "langgraph-checkpoint>=2.0,<3.0" \
            "pydantic>=2.5" "openai>=1.20.0" "anthropic>=0.30" \
            "trimesh>=4.0" "rtree>=1.1" "scipy>=1.10" "scikit-learn>=1.3" \
            "fastapi>=0.110" "uvicorn[standard]>=0.27" "ipython>=8.15" "pytest>=7.4"
# --no-deps skips re-checking the numpy pin in pyproject.toml; fastapi+uvicorn
# are already installed by the previous step, so the [web] extras resolve.
pip install --no-deps -e .

The last step registers the mac-config-reset console script and lets you run python -m multi_agent_cad.graph from any directory. See requirements.txt / pyproject.toml for the canonical dependency list.

Windows: the same conda env create + pip install --no-deps aider-chat==0.82.3 flow works — trimesh and rtree come from conda-forge prebuilt; OCP is pulled in transitively by build123d (via its PyPI dep cadquery-ocp-novtk). Don't use the pure-pip workaround below on Windows — native wheels for trimesh/rtree can be unreliable. Set the API key in PowerShell as $env:DASHSCOPE_API_KEY = "sk-..." (or set DASHSCOPE_API_KEY=sk-... in cmd.exe). For the Web UI under conda, pip install -e ".[web]" inside the activated env works — uvloop auto-skips on Windows. Windows isn't in CI, but the code avoids Unix-only APIs and uses UTF-8 throughout; issues welcome.

Generate one part

Set USER_REQUEST in multi_agent_cad/config.py, then run:

python -m multi_agent_cad.graph

For the single-part browser UI:

pip install -e ".[web]"
python -m multi_agent_cad.web

The UI listens on 127.0.0.1 by default. It executes generated Python with your user account's permissions, so do not expose it directly to an untrusted network. Copyi