New · AI optional · runs on your hardware
Three ways to drive Loom — AI is optional
A model reasons; Vyges executes. Reach the same engines three ways — from the CLI & CI, from your AI IDE, or by registering a model and letting vyges drive it. Bring any AI model — general-purpose (Claude, GPT, Grok), open-weight (Qwen, Llama, DeepSeek), or a semiconductor-specific model such as SemiKong. If it can call tools, it can drive Loom through vyges mcp. It all runs on your hardware — no cloud, no upload, no auth.
1 · CLI & CI
No AI at all
vyges loom drc run … — scriptable, CI-native, reproducible. The deterministic core stands on its own.
2 · AI IDE — bring your model
Claude Code · Cursor · VS Code
Point any MCP client at vyges mcp; your assistant calls the engines as tools. You bring the model, Vyges brings the tools.
3 · vyges model run — autonomous
Register a model; Vyges drives it
Register a local model with vyges model; vyges model run runs the reason → act → observe loop itself — for CI, air-gapped, and autonomous flows.
Every engineer already knows how to use Claude or Cursor. Now those same assistants can drive real silicon implementation tools.
See it actually happen
A published conformance run — on a clean runner, not our laptop
We publish the run. An AI agent is handed a set of plain-English requests and nothing else — no fine-tuning, no hard-coded command lines — and has to work out which engine to reach for from the engines' own --describe contracts, then run them on a fresh GitHub Actions machine. The report shows what it picked, what it ran, and what came back. It is a demo bundle of the suite, sized to run in minutes on a free runner — not every engine on this page, and the fixtures are small.
Read the latest run report →
Runs on demand rather than on a schedule, so the report is dated — the timestamp on it is the run you are reading. Source and fixtures: vyges-loom-testbench.
Already using SemiKong?
Keep your model. Register it with vyges model, point it at vyges mcp, and it can drive DRC, LVS, timing, extraction, and optimization against your own PDK and private IP — entirely on local hardware.
$ vyges install loom
$ vyges mcp setup # register Loom with the AI IDEs on your machine
# then, in your IDE: "run DRC on this GDS against the sky130 deck installed locally"
All three routes are the same binary. vyges loom, vyges mcp and vyges model ship in the Vyges CLI — install once and you have all three, whether or not you ever point a model at them.
Routes 2 and 3 both run through vyges mcp, the local MCP server — how the engines describe themselves, and how you control what an agent is allowed to change. How it works →
Deterministic execution
The AI is a driver, never inside the engines. Pull it out and the exact same flow runs headless in CI — reproducible, the way a tapeout demands.
Your design never leaves your box
A plain local subprocess over stdio — no server, no port, no auth. The right trust model for RTL, layout, and PDKs under NDA.
loom.feedback — the agent's eyes
One call returns a rendered layout, categorized DRC verdicts, and a score — so an agent can debug a layout, not just pass/fail it.
Walkthrough: Point your AI IDE at your own silicon tools →
See it live: an off-the-shelf model drives every Loom engine, from a fresh install → — no fine-tuning, no orchestration code, running in a clean cloud environment.