Awesome AI AgentsMulti-Agent Frameworks

Pan-Chera/Multi-Agent-CAD

⭐ 941 Python repository created 2026-07-30

MAC, short for Multi-Agent CAD, is a research framework from Tsinghua University IEI Lab for text-to-CAD generation. Instead of asking one large model to write an entire CAD program in a single long context, it decouples the job across four cooperating agents that hand structured intermediate results to each other, and it constrains how much test-time compute each stage may spend. The generated artefact is Python code for build123d, a parametric modelling library, so the output is an editable feature history that exports to STEP rather than a mesh, and the aider coding tool is used inside the loop to iterate on that code. The stated motivation is efficiency: the README compares MAC against a single-agent CAD skill baseline on the same ten benchmark prompts and reports 896 thousand tokens versus 103.9 million, roughly a thirteen-fold lower inference cost, and a feature pass rate of 99.3 percent against 97.9 percent. The benchmark parts exercise arrayed features, boolean operations, rotational patterns, helical sweeps and multi-body assemblies, and a separate gallery of ten original prompts covers ornaments and mechanisms such as a Geneva mechanism, an impeller and a multi-link chain. A harder scenario is print-in-place articulable models, where several independent solid bodies must coexist in one STEP file with clearances of roughly 0.4 to 1 millimetre so that the kinematic pairs move freely straight off the printer without assembly; examples include a ball-in-cage fidget toy and a gyroscope with pivot pins. A web UI drives a full pipeline run, and the documentation records per-prompt token and cost data. Installation uses a conda environment plus a pinned aider install, with a documented numpy conflict workaround. Python 3.11, MIT licensed.

https://github.com/Pan-Chera/Multi-Agent-CAD

multi-agenttext-to-cad3d-modelingbuild123dcode-generation3d-printingresearch

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