开源MCP工具：CAD/CAE Copilot

Option 1: GitHub Codespaces (fastest, zero install)

Click "Open in GitHub Codespaces" above. The environment sets itself up; when it finishes loading, run make dev (or python3 scripts/dev.py if make is unavailable). Then connect an agent and paste the motor mounting fixture prompt, or the shorter bracket prompt:

Create a 120 × 80 × 12 mm machined bearing support bracket with a centered
Ø42 mm horizontal bearing bore, four Ø10 mm base mounting holes, and two
mirrored gussets. Preserve the exact dimensions, expose editable parameters,
verify the final geometry, and run the deterministic engineering critique.

Inspect the generated model, named parts, verification results, and stable @face:* references in the workbench.

Option 2: Docker all-in-one (recommended local package)

Packages the backend, built viewer, MCP HTTP server, build123d / OpenCASCADE dependencies, and CalculiX into one container.

Quick start with Docker Compose (recommended):

docker compose up -d

Or build and run manually:

docker build -t aieng/workbench:local .
docker run --rm -it -p 8000:8000 -p 8765:8765 -v aieng-data:/data aieng/workbench:local

Open the viewer at http://localhost:8000/app/ and point an MCP-over-HTTP client at http://localhost:8765/sse. Projects and .aieng packages persist in the aieng-data volume. The container enables AIENG_MCP_MANAGED_APPROVAL=1 by default, so approval-gated CAD/CAE tools surface through the workbench UI.

Option 3: Local developer install

Best when you intend to modify the code. Prerequisite: a conda env named exactly aieng311 (Python ≥ 3.11) with build123d — the MCP configs and run scripts assume this name. The build123d / OpenCASCADE (OCP) install can be slow or fail on some platforms; if it does, prefer the Docker path above.

conda create -n aieng311 python=3.11 -y
conda activate aieng311
pip install build123d
cd aieng-ui/backend && pip install -e .

Then start both services in one terminal (Ctrl+C stops both):

make dev                  # macOS / Linux / WSL
.\dev.ps1                 # Windows PowerShell
python scripts/dev.py     # cross-platform fallback

Backend → FastAPI on http://127.0.0.1:8000; frontend → Vite on http://localhost:5173. Custom ports: BACKEND_PORT=8080 FRONTEND_PORT=3000 make dev.

<details> <summary>Start services individually / run tests</summary>

make backend     # or: cd aieng-ui/backend && uvicorn app.main:app --host 127.0.0.1 --port 8000 --reload
make frontend    # or: cd aieng-ui/frontend && npm install && npm run dev
cd aieng-ui/backend && python -m pytest    # backend test suite

</details>

2. Rebuild CAD from Mesh → Export STEP

Reconstructs analytic CAD from a mesh and exports STEP when the shell validates.

<img src="docs/assets/showcase/mesh_to_cad_flow.svg" width="880" alt="Mesh to Region Segmentation to Surface Fitting to Face Generation to Sew Shell to Export STEP"/>

pytest aieng/tests/test_mesh_brep_solidification.py -q

Key artifacts: geometry/reconstructed.step (when valid), graph/mesh_brep_stitching_plan.json Boundary: Mesh-derived/lossy; plane/cylinder dominant; freeform/NURBS future work; partial shells do not produce STEP. Details →

Quick start

Three ways in — pick one and you're modeling in minutes.

Before you start: you need your own MCP client (Claude Code, OpenAI Codex, GitHub Copilot, Cursor, …) with its own model access. The aieng backend itself needs no API key — your agent connects to it over MCP and drives the workbench through its own harness.

For a first try, Docker (Option 2) is the most reliable — it pins the build123d / OpenCASCADE / CalculiX stack so nothing has to compile on your machine. The local dev install is best once you intend to hack on the code.

Industrial CAD examples

Each example starts from explicit dimensions, feature locations, and modeling constraints — the agent executes and verifies the specification rather than inventing the requirements.

Machined Bearing Bracket Datums, bore, mounting pattern, gussets, fillets, and critique

Six-Port Pneumatic Manifold Exact envelope, port spacing, counterbores, and editable dimensions

Industrial Junction Box Named assembly parts, exported artifacts, and stable face pointers

<details> <summary><strong>What these examples verify</strong></summary>

- Machined bearing support bracket — one manufacturable solid with a specified base envelope, horizontal bearing bore, symmetric mounting pattern, mirrored gussets, fillets, and chamfers. The workbench caught and corrected construction errors, then verified the final datums, topology, editable parameters, and engineering critique. - Six-port pneumatic manifold — a specification-driven manifold with an exact 160 × 50 × 40 mm envelope, six equally spaced outlets, axial inlet ports, counterbored mounting holes, edge fillets, opening chamfers, and editable dimensions. - Industrial junction-box assembly — a two-part enclosure assembly with named base and lid solids, internal mounting bosses, cable-gland openings, separated lid placement, generated STEP/STL/GLB artifacts, and a selectable stable face pointer for precise follow-up work.

</details>

Showcase demos

Canonical backend demos, each runnable as a single test:

Visual inspection in VS Code

The VS Code extension is the most visual way to experience aieng — a front-end for the .aieng package format, MCP tools, and CAD/CAE backend that brings the AI-CAD design loop directly into your editor. It can:

• open a local .aieng package as a read-only custom editor,
• connect to a live backend project preview,
• visualize generated GLB/STL CAD outputs,
• and copy stable @face:id pointers back into your chat with an agent.

The extension is one layer of the system, not the whole thing — the core is the package format and engineering backend that let agents and humans share reproducible CAD/CAE project state. Setup and development notes live in aieng-vscode-extension/README.md.

4. Design Study: Adjustable Dimensions → Compare → Adopt

Validates, executes, compares, and optionally adopts parameterized design candidates without overwriting the baseline.

<img src="docs/assets/showcase/design_study_flow.svg" width="960" alt="Setup Problem to Propose Candidate to Safety Checks to Build Design Copy to Compare Options to Adopt Best"/>

pytest aieng-ui/backend/tests/test_design_study_demo.py -q

Key artifacts: analysis/design_study_candidate_ranking.json, analysis/design_study_acceptance.json, accepted/candidate_good/geometry/shape_ir.json Boundary: Static metrics in demo; no autonomous optimization; no baseline overwrite; ranking is advisory. Details →