Multi-Agent-CAD
MAC (Multi-Agent CAD): A decoupled multi-agent framework for text-to-CAD generation via constrained test-time compute
Multi-Agent-CAD (MAC) is a decoupled multi-agent framework that converts natural language descriptions into printable 3D CAD models using four specialized AI agents. Developed by Tsinghua University's IEI Lab, it reduces token consumption by 116× and inference costs by 13× compared to single-agent approaches while achieving a 99.3% feature pass rate on benchmarks. The system coordinates a Spec Planner, Geometric Architect, Python Coder, and Autonomous Skill Loop that communicate via structured JSON state rather than bloated conversation context. MAC generates build123d-compatible Python code and exports production-ready STEP and STL files, including complex print-in-place articulated models.
Key Features
Use Cases
- 01Generating printable 3D mechanical parts from text descriptions (flanges, brackets, gears, shafts)
- 02Creating print-in-place articulated models like gyroscopes, ball-in-cage toys, and kinematic mechanisms
- 03Rapid prototyping of CAD designs for 3D printing without manual modeling
- 04Generating benchmark mechanical components with arrayed features, boolean operations, and helical sweeps
- 05Cost-optimized CAD generation workflows for educational or research environments
- 06Building custom text-to-CAD pipelines with specialized models for each generation stage
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Multi-Agent-CAD — FAQ
What is Multi-Agent-CAD?+
Multi-Agent-CAD (MAC) is a four-agent AI framework that generates printable 3D CAD models from natural language prompts. It splits the generation process across specialized agents (Spec Planner, Geometric Architect, Python Coder, Autonomous Skill Loop) that communicate via structured JSON, reducing token usage by 116× compared to single-agent approaches.
How do I install Multi-Agent-CAD?+
Clone the repository, create the conda environment from environment.yml, activate it, then install aider-chat with --no-deps to resolve numpy version conflicts. Set your LLM API key in the DASHSCOPE_API_KEY environment variable and edit config.py to configure your model provider and default CAD request.
What LLM providers does MAC support?+
MAC works with any OpenAI-compatible API endpoint. The default configuration uses Alibaba Cloud DashScope with qwen3.7-max, but you can point it to OpenAI, DeepSeek, Google Gemini, local Ollama models, or Anthropic Claude via a gateway by editing DS_BASE_URL and model names in config.py.
Do I need API keys or paid accounts?+
Yes, you need an API key for your chosen LLM provider (DashScope, OpenAI, DeepSeek, etc.) unless you use a local model via Ollama. The framework itself is MIT-licensed and free, but LLM API calls incur the provider's standard costs.
Can I customize which models each agent uses?+
Yes, MAC supports per-stage model selection. Each of the four agents (Spec Planner, Geometric Architect, Python Coder, Aider Repair) has independent MODEL, TEMPERATURE, MAX_TOKENS, and KWARGS settings in config.py, allowing you to assign cheaper models to simple tasks and stronger models to complex reasoning.
What output formats does MAC generate?+
MAC outputs build123d Python source code (temp_design_*.py), STEP files for CAD software import, STL files for 3D printing, and JSON files containing feature measurements and QA diagnostics. The Web UI also provides GLB previews for in-browser 3D visualization.
How do I install Multi-Agent-CAD?+
Open the source repository on GitHub and follow its README. Multi-Agent-CAD is a agent — MCP Agents Market links you directly to the official repo.
Is Multi-Agent-CAD free?+
Multi-Agent-CAD is an open-source project hosted on GitHub. Check the repository for its license and any usage requirements.