rfauto

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rfauto is an automation framework for RF/microwave design and simulation. Describe a device, get a first-cut geometry from physics formulas, simulate it with whichever solver you have, check the result for numerical artifacts, and let an optimizer tune the dimensions — with AI assistants allowed to drive the whole pipeline through MCP, under one hard rule:

Every physical number (frequency, loss, geometry) is produced by a deterministic kernel or solver — never by the LLM.

It drives 13 EM/EDA engines behind one interface, ships 43 parameterized device templates with built-in physics checks, and exposes 111 CLI commands and 80 MCP tools (+3 resources) — kept honest by 7400+ unit tests that run without any commercial license.

Contents · Why · What it does · Trust layer · Quick start · The Web UI · Engines · Docs · Roadmap · Contributing

Why

RF simulation work is full of manual repetition and quiet traps:

• Every iteration means redrawing geometry, re-running a solver that takes minutes to hours, and reading numbers out by hand.

• Each vendor tool has its own API and quirks; switching engines means rewriting your workflow.

• Solvers fail silently in confusing ways — a bad mesh or a wrong port can produce plausible-looking garbage.

• The good solvers need expensive licenses; the free ones deserve distrust until verified.

rfauto turns that loop into code: templates build the geometry, adapters talk to the engines, quality gates judge the results, optimizers close the loop, and every reported number carries its provenance.

Related MCP server: CST Studio Orchestrator MCP

What it does

• One interface, many engines — HFSS, ADS, openEMS, COMSOL, Elmer, NGSolve, Meep, Icepak, Q3D, Palace, KiCad, ngspice and FDTDX (JAX) behind a common adapter layer. Commercial engines stay opt-in extras; everything core runs against a built-in fake solver, so you can try the whole framework with zero licenses.

• Device template factory — 43 parameterized families (couplers, power dividers, filters, antennas, transitions…). Each template synthesizes starting dimensions from closed-form physics, and registers acceptance checks so you can tell "real result" from "mesh artifact".

• Optimization loops — TPE, CMA-ES and multi-objective NSGA-II, with a surrogate-model path: fit a cheap model from a batch of solves, then search the model instead of re-solving. Batch campaigns run unattended with budget admission, quotas and watchdogs.

• Quality gates everywhere — energy and passivity checks, grid-artifact diagnostics, cross-engine arbitration (compare the same geometry on a second solver), and physics-invariant tests. A result that fails a gate is reported as failed, never silently passed.

• AI that drives but doesn't invent — a full MCP server so Claude Desktop, Cursor or your own agent can operate the framework. Agent edits go through a sandbox draft and validation gates before they touch your workspace.

The trust layer

The part we care about most: how do you know a simulation result is believable? rfauto treats that as a first-class feature — health gates on every run, reference responses per template, deterministic kernels for every number, and a sandbox-plus-gates path for anything an AI agent wants to change.

Quick start

No commercial tools needed — the built-in fake solver covers the whole core.

git clone https://github.com/geer1895/rfauto && cd rfauto
pip install -e ".[dev]"          # or: uv sync --extra dev

# run the test suite (~7400 tests, no EDA required)
python -m pytest tests/unit -q

# check which solvers/licenses are visible on your machine
rfauto doctor

Synthesize a 50 Ω microstrip line at 2.4 GHz (pure math, instant):

$ rfauto syn mline 50.0 --freq 2.4 --stackup rogers4350b_h0.508
微带线综合结果 (rogers4350b_h0.508 @ 2.4 GHz)
  目标阻抗: 50.00 Ω
  线宽:     1.1133 mm
  εeff:     2.8530
  状态:     ok

Run a Wilkinson power-divider simulation without any solver installed (the fake adapter answers instantly; plug in openEMS or HFSS later for real physics):

$ rfauto run recipes/wilkinson_pd_v1.yaml --adapter fake
✓ 仿真完成  run_id: 20260921_001708_3fe788d3
  指标:
    s11_db_max_in_band: -12.21
    s21_db_mean_in_band: -3.67
    iso_s23_db_min_in_band: 28.07

From there, the usual loop:

rfauto sweep recipes/wilkinson_pd_v1.yaml --adapter fake   # parameter sweep
rfauto tune  recipes/wilkinson_pd_v1.yaml --max-trials 60  # optimization loop
rfauto replay <run_id>                                     # reproduce a past run

Connect an AI assistant (optional)

pip install -e ".[mcp]"
python -m rfauto.mcp_server        # stdio transport; 80 tools

Then register it in your MCP client (Claude Desktop example):

{
  "mcpServers": {
    "rfauto": {
      "command": "python",
      "args": ["-m", "rfauto.mcp_server"],
      "cwd": "/path/to/rfauto"
    }
  }
}

The Web UI

rfauto ui opens a local review workbench — no data leaves your machine. Inspect every run's metrics and curves, compare adapters, run the built-in microwave calculators, and review AI-agent proposals before promoting them:

rfauto ui          # http://127.0.0.1:8642 — local only

Every page is deep-linkable (#runs, #sparams, #tools, …), so you can bookmark the view you care about.

Engines

Commercial tools need your own valid license; the framework neither includes nor circumvents any license, and no vendor-proprietary content is distributed in this repository (see THIRD_PARTY_NOTICES.md).

Documentation

• Templates reference — per-template acceptance values and modeling rules

• openEMS build guide

• COMSOL notes · Migration guide

• Template metadata — one meta.yaml per device family

• Changelog · Contributing

Status & roadmap

rfauto is a working tool, not a demo: the core chain (template → synthesis → solve → quality gates → optimization → report) runs on real HFSS, ADS, openEMS, COMSOL and KiCad installs, backed by the test suite above. It is Windows-first today, single-maintainer, and moving toward Linux/Docker friendliness.

Planned next, in the open:

• Datasets & benchmarks — the simulation datasets collected by the built-in data-factory pipeline and the agent evaluation sets are not part of this repository yet; we plan to release them progressively, and would love collaborators to help shape and curate them.

• Methodology paper — a write-up of the quality-gate / deterministic- kernel methodology is planned; contributions and co-authoring welcome.

• More device families, more engines, better onboarding — all good first issues.

If any of this sounds interesting to you, open an issue — we'd like this to become a community project, not a solo archive.

Contributing

Issues and pull requests are welcome — see CONTRIBUTING.md for the quick start, project rules and the meaning of the #NNN markers in code comments.

Citation

If rfauto helps your research, please cite it — see CITATION.cff.

License

rfauto is licensed under GPL-3.0-only (see LICENSE). Third-party package licenses are listed in THIRD_PARTY_NOTICES.md. Note that the optional openEMS adapter drives GPL-licensed openEMS through a separate subprocess; the openEMS bindings themselves are not included in this repository and are built from the official openEMS source by the user.