Agentic SWMM Workflow

Pre-1.0 · stable v0.9.4 · pip install aiswmm==0.9.4 · CHANGELOG

[!TIP] Headaches from tedious model setup? Try our another project SWMMCanada, our automated model-building project: draw an area anywhere in Canada and get a ready-to-run SWMM model. Up and running now.

Highlights

• Agentic SWMM for reproducible stormwater modeling: the aiswmm runtime + Skills + MCP + SWMM, verification-first, with a full audit trail.
• Live demo, no install: try it in the browser at aiswmm.com.
• One prompt to client deliverable: one English sentence fetched the real downtown Victoria storm network, ran SWMM, audited it, and exported a Word report: cases/downtown-victoria.
• The same sentence on a fifth city: downtown Kelowna, one approval, with the figures, the numbers and the model committed so the run reproduces offline: cases/kelowna.
• A model that said not to trust it: when the requested week turned out dry, the run named its own errors as disqualifying instead of handing over bad numbers: cases/downtown-victoria-on-arm.md.
• Real networks upstream: SWMMCanada covers 35 Canadian cities, SWMManywhere synthesis covers the rest of the globe.
• Bring the LLM you already pay for: ten provider routes behind one aiswmm setup wizard, with a local fallback chain.
• Published research: published in AI for Engineering (MDPI, 2026): read the paper.

Project Overview

Agentic SWMM is an open-source, verification-first framework for reproducible and extensible stormwater modelling, starting with EPA SWMM. It connects QGIS-based preprocessing, upstream model generation from SWMMCanada inside Canada (real storm networks for 35 cities, synthesis anywhere else in the country) and SWMManywhere-based synthesis outside Canada, deterministic SWMM execution, QA checks, provenance tracking, calibration and climate-scenario support, documentation, and modelling memory, while keeping human modellers in control.

The goal is not to replace SWMM or the modeller, but to build an agentic modelling layer that makes stormwater-modelling workflows easier to reproduce, audit, extend, remember, and trust. Agentic SWMM comes with aiswmm as its built-in runtime. Users can describe a modelling goal in natural language, while model execution remains deterministic, inspectable, and artifact-based. The repository's MCP servers and Skills can also be used with other agent runtimes, including Codex, Claude, OpenClaw, and Hermes.

This is not a simple chat-to-SWMM wrapper. The aiswmm runtime can help coordinate the workflow, but model files, SWMM runs, QA checks, plots, provenance records, audit notes, and modelling memory remain visible as reusable artifacts. Modelling memory can summarize repeated problems and propose Skill refinements, but accepted changes still require human review and benchmark verification.

Authors: Zhonghao Zhang & Caterina Valeo
License: MIT

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.

What makes it different

• Quick onboarding: start from one-line macOS/Linux or Windows installers, with Docker and Python package paths documented separately.
• 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.

Meet your agent in about five minutes

macOS and Linux:

curl -fsSL https://aiswmm.com/install.sh | bash

Windows PowerShell:

irm https://aiswmm.com/install.ps1 | iex

Reproducible run (pinned Docker image, v0.9.4), no local install:

docker run --rm -v "$PWD/runs:/app/runs" ghcr.io/zhonghao1995/agentic-swmm-workflow:v0.9.4 acceptance

After installation, launch the runtime with aiswmm.

One-line installers run a remote script; review it first if you want to see what executes. When it finishes it hands over to aiswmm setup, which lists every route and detects what is already running; three of them need no API key at all, including a local gateway that fronts a ChatGPT plan. For storing a key directly, see API key configuration. Never paste API keys into the aiswmm conversation.

Three ways in (one-line installer, Docker, or pip), compared side by side (what you get, prerequisites, reproducibility, when to pick each): choosing an install path. If something goes wrong, or you want a provider that needs no API key: install and troubleshooting.

Workflow

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

_{▶ Try the live demo: Greenwich Peninsula, SWMManywhere synthesis → swmm5 run → audit → render, end to end in the browser.}

Validation snapshot

The repository includes runnable benchmarks and research previews with different evidence boundaries. The README keeps only the index; figures, commands, and boundary notes live in Validation evidence.

Examples: TUFLOW and Tecnopolo.

Audit and research memory

The audit layer consolidates artifacts, QA checks, and metric provenance into an Obsidian-compatible experiment note. This example catches a recorded peak-flow value that does not match the value re-parsed from the SWMM report source section.

The downstream modelling-memory layer can summarize audited run histories into recurring failure patterns, assumptions, missing evidence, QA issues, lessons learned, and controlled proposals for updating existing skills or creating new skills. Because skills drive the workflow, these proposals stay coupled to the current Agentic SWMM framework and still require human review and benchmark verification before acceptance.

More details: Experiment audit framework and Modeling memory and skill evolution.

Learn more about the ecosystem

Agentic SWMM is the SWMM engine within a larger effort toward a trustworthy, auditable, fully automated urban-hydrology modelling platform: a top-level agentic runtime orchestrating engine-specific automation on top of a shared data-to-model front end.

Codex / Claude / OpenClaw / Hermes ready

Beyond its own aiswmm runtime, the Agentic SWMM workflow can be driven by external agent runtimes: Codex, Claude Code, OpenClaw, or Hermes. For an agent-orchestrated run, preload the agent/memory/ package and point the runtime at the top-level entry skill skills/swmm-end-to-end/SKILL.md, which decides which workflow path to take, which QA gates must pass, and when to stop rather than invent missing inputs.

Install the skills into any skills-aware runtime (Claude Code, Codex, OpenCode, …) with one command:

npx skills add Zhonghao1995/agentic-swmm-workflow

The skills carry the workflow and evidence contracts; pair them with the project install for the executable toolchain (aiswmm CLI, SWMM solver, MCP servers).

More details: Codex runtime path · OpenClaw execution path · Skill installation · MCP runtime integration.

Documentation map

• Validation evidence - benchmark scope, commands, audit example, and evidence boundaries
• Installation and CLI guide - Docker, local install, Windows options, and CLI examples
• Install and troubleshooting - the one-liner per platform, what goes wrong on a real machine and why, and which platforms are covered by an install job in CI
• Two interfaces, one engine - how natural-language goals and CLI verbs map onto the same deterministic tools and artifacts, and when to use which
• LLM provider routes - ten routes out of the box (OpenAI, Anthropic, OpenRouter, DeepSeek, Groq, Gemini, local Ollama/LM Studio, gateways, custom endpoints), the aiswmm setup wizard, per-route auth, and the local fallback chain
• Climate scenario batches - aiswmm climate: calibrate first, then compare the model's response under precipitation-scaled climate scenarios, one canonical 03_climate/ summary per run
• Experiment audit framework - provenance, comparison, and Obsidian note contracts
• Modeling memory and skill evolution - controlled memory-to-skill refinement loop
• Memory runtime - on-disk substrate, four confidence quadrants, and runtime opt-out flags
• Memory runtime CLI examples - one worked example per memory verb
• Codex runtime path - local development, audit, Obsidian, and evidence-review workflow
• OpenClaw execution path - MCP tool-call sequence for agent runtimes
• Repository map - folder-level walkthrough
• Calibration example - compact calibration support example

Where collaborators can help

Contributions are welcome in additional SWMM case studies, stronger calibration and validation workflows, DEM / land-use / soil / drainage-asset workflows, new MCP tools, QA testing, tutorials, and interoperability with GIS, ML, and hydrologic toolchains.

Contact:

• zhonghaoz@uvic.ca
• valeo@uvic.ca

Citation

GitHub citation metadata is provided in CITATION.cff. Please cite the published article.

APA article (preferred)

Zhang, Z., & Valeo, C. (2026). Agentic SWMM: Auditable and reproducible stormwater modelling workflow with Agent Skills and Model Context Protocol. AI for Engineering, 1(1), 5. https://doi.org/10.3390/aieng1010005

APA repository

Zhang, Z., & Valeo, C. (2026). agentic-swmm-workflow [Computer software]. GitHub. https://github.com/Zhonghao1995/agentic-swmm-workflow