omagent

LLM-assisted modeling for OpenModelica — an open-source, headless Python agent that turns a natural-language task into a verified Modelica model:

natural language ──> generate ──> compile (omc) ──> simulate ──> verify physics
                        ▲                                            │
                        └──── structured error / verifier feedback ──┘

Unlike a plain code assistant, omagent closes the loop on physics, not just compilation: quantitative verifiers check trajectories against expected behavior (final values, settling windows, bounds, overshoot), and every failure — compiler diagnostics or physics complaints — is parsed into structured feedback for the next fix attempt.

Key capabilities:

• Structured omc diagnostics — parses getErrorString(), simulation logs, and OMPython's exception format into records with severity, source location, and failure kind (syntax / lookup / type / balance / connect / initialization / runtime)
• Environment-grounded fix hints — on Class X not found, omagent asks omc what the parent package actually contains (getClassNames) and puts near-miss suggestions into the fix prompt. This resolves the dominant observed failure mode: stale library knowledge (e.g. MSL 3.2 names such as Basic.EMF vs. MSL 4.x Basic.RotationalEMF)
• Quantitative verification — reads CSV or Dymola-format .mat results; verifier complaints ("final value of x is 1.93, expected 2.0") drive tuning
• Benchmark task ladder — 5 escalating, auto-gradable tasks with full transcript capture, in the format the OpenModelica benchmark discussion (OpenModelica#15385) calls for
• LLM-backend-agnostic — the loop depends on a one-method protocol; adapters ship for Anthropic and for any OpenAI-compatible endpoint (Ollama, LM Studio, llama.cpp, vLLM — local open-weight models included)
• Tested — 83 unit tests run without OpenModelica installed; 4 integration tests validate against a live omc

Installation

Requires Python >= 3.10. The core package has zero hard dependencies; features are opt-in extras:

pip install -e .                    # parsers + loop only (no omc needed)
pip install -e ".[omc]"             # + OMPython (talk to a real omc)
pip install -e ".[results]"         # + scipy (.mat result files; CSV needs nothing)
pip install -e ".[llm]"             # + anthropic adapter
pip install -e ".[all]"             # everything, including pytest

To use it against a real compiler you need OpenModelica (tested with 1.26–1.27) with the Modelica Standard Library installed for omc:

echo 'installPackage(Modelica); getErrorString();' > /tmp/i.mos && omc /tmp/i.mos

Note: OMEdit installs the MSL for itself automatically; headless omc sessions do not. If models using Modelica.* fail with "Class ... not found", this is why.

Verify your setup:

pytest -m "not integration"   # unit tests, no omc required
pytest -m integration         # against your live omc (+MSL, scipy for .mat)

Quick start

from omagent import AgentLoop, OMSession, all_of, expect_bounds, expect_final
from omagent.llm import ClaudeLLM   # or any object with .propose(...)

# physics acceptance criteria — complaints feed back into the fix loop
verifier = all_of(
    expect_bounds("x", lo=-0.105, hi=0.105),
    expect_final("x", 0.0, atol=0.06, rtol=0.0),
)

loop = AgentLoop(
    OMSession(),                      # real omc via OMPython
    ClaudeLLM(),                      # needs ANTHROPIC_API_KEY
    max_attempts=4,
    simulate_options={"stopTime": 10.0, "outputFormat": "csv"},
    verifier=verifier,
)
result = loop.run(
    "A mass-spring-damper: m = 1 kg, c = 100 N/m, d = 1 N.s/m, released "
    "from x = 0.1 m at rest. Name position x and velocity v.")

print(result.success, result.model_name)
print(result.final_code)
for a in result.attempts:
    print(a.n, a.stage, a.complaint)

Local / open-weight models work through any OpenAI-compatible server (Ollama, LM Studio, llama.cpp server, vLLM) with no extra dependencies:

from omagent.llm import OpenAICompatLLM
llm = OpenAICompatLLM(model="qwen2.5-coder:14b")            # Ollama default URL
# llm = OpenAICompatLLM(model="...", base_url="http://localhost:1234/v1")  # LM Studio

Or bring your own LLM by implementing one method:

class MyLLM:
    def propose(self, task, previous_code, error_summary):
        # previous_code/error_summary are None on the first (fresh) call;
        # on retries they contain the failed model and structured feedback.
        return "... complete Modelica model ..."

Run the benchmark ladder

export ANTHROPIC_API_KEY=...
python examples/run_ladder.py                 # all 5 tiers
python examples/run_ladder.py --max-tier 3    # subset by difficulty
python examples/run_ladder.py --tasks dc_motor --model claude-opus-4-8

Tiers: (1) pure-equation dynamics, (2) MSL component composition, (3) hybrid events, (4) verifier-driven design — the requirement is given, the parameter is not, (5) multi-domain electro-mechanical. Per-task JSON transcripts (attempt history, diagnostics, code, LLM rounds) land in transcripts/, with summary.json aggregating results.

Use pieces standalone

from omagent import OMSession, parse_error_string, summarize_for_llm, load_result

s = OMSession()
r = s.load_string(my_modelica_code)     # honest success verdict + diagnostics
print(summarize_for_llm(r.diagnostics)) # deduplicated digest for any prompt

sim = s.simulate("MyModel", stopTime=5.0, outputFormat="csv")
res = load_result(sim.value["resultFile"])
times, x = res.series("x")

Project layout

omagent/
  errors.py    # omc diagnostic parsing + classification
  session.py   # OMSession: testable wrapper over OMPython/omc
  loop.py      # AgentLoop + lookup-suggestion feedback
  results.py   # CSV/.mat readers + quantitative verifiers
  llm.py       # Anthropic adapter (protocol: bring your own)
  tasks.py     # benchmark task ladder definitions
  runner.py    # ladder execution + transcript persistence
examples/      # first_run.py, run_ladder.py
tests/         # 83 unit + 4 integration tests

Design notes

• Testable by construction. OMSession talks to any object with sendExpression(); tests replay recorded omc output, so the full agent loop is unit-tested without a compiler or an API key.
• Both OMPython contracts. Older OMPython returns and lets you read getErrorString(); newer OMPython raises OMCSessionException on error-level messages. Both yield identical structured failures.
• Environment failures are not model failures. The ladder runner loads the MSL when a task requires it and reports load problems as environment outcomes with zero attempts charged to the LLM.

Roadmap

• Warning-level quality gates (e.g. treat "initial conditions over specified" as a verifier complaint)
• Multi-run variance measurement and cross-model comparison in the runner
• Optional MCP tool surface, composing with OMEdit's built-in MCP server
• More ladder tiers targeting thermal/fluid domains and third-party libraries

License

BSD-3-Clause — see LICENSE.