Quick onboarding
Start from one-line macOS/Linux or Windows installers, with Docker and the Python (PyPI) package paths documented separately.
Features & Workflow
From a stormwater request to a verified, remembered result
Agentic SWMM wraps EPA SWMM in natural-language orchestration with deterministic execution — explicit provenance, project memory, and verification-first modeling at every stage.
Why this project exists
Stormwater modelling is rarely one command. A typical SWMM project can involve GIS preprocessing, rainfall formatting, parameter assignment, network assembly, INP construction, model execution, QA checks, plots, calibration, uncertainty analysis, and reporting.
Agentic SWMM provides a middle path: natural-language orchestration with deterministic SWMM execution, explicit provenance, project memory, and verification-first modelling.
The goal is not to replace SWMM or the modeller, but to make SWMM-based modelling easier to reproduce, audit, remember, and trust.
What makes it different
Five things that set the workflow apart
Agent-guided & SWMM-grounded
Agents can coordinate tasks, while model execution stays deterministic, inspectable, and CLI-runnable.
Modular skill layer
GIS, climate, building, running, plotting, calibration, uncertainty, audit, and orchestration are separated into reusable modules with MCP interfaces where available.
Verification-first provenance
Build, run, audit, and comparison stages emit traceable artifacts before outputs are treated as evidence.
Supervised skill evolution
Audited runs can surface recurring workflow patterns and propose updates to existing skills or new skills, while staying coupled to the current skill-driven framework.
The workflow
Three connected layers
The workflow has three connected layers: execution, modeling memory, and controlled skill evolution. Natural-language requests can trigger reproducible SWMM actions; audited artifacts update human-readable and machine-readable memory; repeated patterns can produce skill-refinement proposals that still require human review and benchmark verification.
What a run can produce
- generated or supplied SWMM input files such as
model.inp - SWMM report and binary outputs such as
.rptand.out - manifests, command traces, QA summaries, and parsed peak-flow metrics
- rainfall-runoff figures, calibration summaries, and fuzzy uncertainty summaries
- audit records:
experiment_provenance.json,comparison.json, andexperiment_note.md - Obsidian-ready modelling notes and modelling-memory summaries