Features & Workflow

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 rerun, inspect, remember, and trust.

What makes it different

  • Quick onboarding: start from an explicit Docker run, or use local bootstrap scripts after reviewing them.
  • 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.

Workflow

Modeling Memory and Skill Evolution

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 .rpt and .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, and experiment_note.md
  • Obsidian-ready modelling notes and modelling-memory summaries