Open source · Verification-first

Stormwater modelling you can reproduce, audit, remember, and trust

Agentic SWMM Workflow wraps EPA SWMM in the aiswmm runtime — natural-language orchestration, deterministic runs, QA checks, provenance, and modeling memory, with you in control.

Get started Read the preprint
  • MIT licensed
  • EPA SWMM 5.2
  • aiswmm on PyPI
  • Stable v0.7.1
aiswmm
$ pip install aiswmm
$ aiswmm
✓ runtime ready · EPA SWMM 5.2
› "Run the Tecnopolo model, check peak flows"
  · deterministic SWMM run
  · QA + provenance recorded
  · audit note written to memory
How it works

Execute, remember, refine

01

Execute

Run SWMM deterministically. Docker and local scripts keep execution repeatable, emitting traceable artifacts at every stage.

02

Remember

Provenance and QA summaries become Obsidian-compatible modeling memory — inspectable, reusable, honest about failures.

03

Refine

Audited runs surface patterns and propose skill refinements — accepted only after human review and benchmark checks.

Project overview

See the Agentic SWMM workflow in video

The workflow

One trail from request to verified result

Agentic SWMM modeling memory and controlled skill evolution loop
5.2EPA SWMM engine
MITOpen-source license
PyPIaiswmm runtime
5Validation benchmarks

Explore the project

Vision →

Why a modeling workflow should remember, audit, and stay reviewable.

Features & Workflow →

The modular skill layer, verification-first provenance, and what a run produces.

Validation & Audit →

Runnable benchmarks, evidence boundaries, and the experiment audit framework.

Download & Install →

One-line installers, the pinned Docker image, and the aiswmm Python package.