Agentic SWMM Workflow
Pre-1.0 · stable v0.7.7 · pip install aiswmm==0.7.7 · 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, with a verification-first workflow and Obsidian-compatible audit trail.
- Five minutes, one command: an Agentic SWMM workflow that is auditable, memory-informed, and agent-ready.
- Live demo (no install, offline case replay): try it in the browser at aiswmm.com.
- SWMMCanada upstream, ready to use: inside Canada, real municipal storm networks for 8 cities or synthesis anywhere else in the country, built from Canadian open data and fetched directly into aiswmm through the
fetch_swmm_from_canada tool; outside Canada, SWMManywhere-based synthesis covers the rest of the globe. Two complementary upstream sources.
- A domain-specific agent runtime, managed-agent style: every session is self-describing (verbatim goal, auto-derived agent snapshot with tool/skill/prompt hashes, environment fingerprint, append-only event log, status lifecycle), and the orchestration plus verification rules are open source and auditable.
- Runtime-portable: the Skills and MCP servers also work with Codex, OpenClaw, and Hermes Agent.
- Published research: our paper is published in AI for Engineering (MDPI), June 9, 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 8 cities, synthesis anywhere else in the country) and SWMManywhere-based synthesis outside Canada, deterministic SWMM execution, QA checks, provenance tracking, calibration 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, v0.7.7), no local install:
docker run --rm -v "$PWD/runs:/app/runs" ghcr.io/zhonghao1995/agentic-swmm-workflow:v0.7.7 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. The installer can set up your OpenAI API key, or you can configure one later via your shell; 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.
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
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.
| Path | What it shows | Evidence boundary |
|---|
| Information-loss-guided subcatchment partition | QGIS-to-Agentic SWMM preprocessing using entropy and fuzzy-similarity concepts from Zhang & Valeo's Journal of Hydrology paper | GIS preprocessing concept, not a calibrated SWMM performance claim |
| Raw GeoPackage-to-INP benchmark | Public TUFLOW GeoPackage layers converted into SWMM-ready artifacts, QA, and audit | Structured raw GIS path, not arbitrary CAD/GIS recognition |
| Prepared-input SWMM benchmark | External 40-subcatchment Tecnopolo model execution, plotting, and direct swmm5 comparison | Prepared INP validation path |
| Prior Monte Carlo uncertainty smoke | Tecnopolo HORTON parameter perturbation and hydrograph envelope preview | Prior uncertainty smoke, not calibration |
| Optional INP-derived raw adapter benchmark | Raw-like inputs extracted from a public SWMM fixture and rebuilt through the modular path | Adapter handoff check, not greenfield watershed generation |
| Cross-environment byte-identical reproducibility | A natural-language prompt (Run the Tecnopolo (Rome 1994) demo) drives the aiswmm chain (LLM agent → MCP → swmm-runner skill) to the same byte-identical model.out as bare swmm5, across macOS and Docker. v0.7.1 re-verification: the minimum natural-language prompt length for this chain is now 11 words, and the model.out SHA256 remains identical across the v0.7.0 → v0.7.1 minor revision. |
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.
| Project | Role in the ecosystem | Status |
|---|
| agentic-hydrology-platform | Orchestration layer: top-level agentic runtime that governs data, model selection, runs, and audit across the engine branches | LSTM catchment-modelling pipeline live; cross-engine (SWMM / MIKE+) orchestration in progress |
| SWMMCanada | Data & model-building layer: ingests and cleans GIS / open data and synthesises reliable model files; the shared front end for the engines. Agentic SWMM consumes it as an upstream INP source via fetch_swmm_from_canada (real municipal pipes for 8 Canadian cities) | SWMM today; extending to MIKE+ and InfoWorks ICM |
| Agentic SWMM (this repository) | SWMM engine: verification-first EPA SWMM automation (Skills + MCP + deterministic runs + audit) | Stable v0.7.7 |
| Agentic-MIKE-Plus | MIKE+ engine: headless DHI MIKE+ automation (Skills + MCP), built on the Agentic SWMM design and the same method paper | Active development |
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
- LLM providers - OpenAI (default, via
aiswmm login) vs opt-in Anthropic backend, both API-key + standard function-calling, auth, and how to switch
- 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
