aiswmm 0.7.1

Unified CLI for reproducible and auditable Agentic SWMM workflows.

Agentic SWMM Workflow

Pre-1.0 — actively developed. The latest stable point release is v0.7.0 (current default on PyPI; companion-paper reproducibility evidence is pinned at v0.6.4). The CLI / Skill / MCP surface may still evolve between minor versions before the planned 1.0 stable release. For reproducibility, pin an explicit version: pip install aiswmm==0.7.0 or docker pull ghcr.io/zhonghao1995/agentic-swmm-workflow:v0.7.0 (v0.6.4 remains available on the same channels for paper-aligned runs). Pre-releases are available via pip install --pre aiswmm (see CHANGELOG.md for the current version).

Agentic SWMM for reproducible stormwater modeling
aiswmm runtime + Skills + MCP + SWMM + verification-first workflow + Obsidian-compatible audit · also compatible with Codex, OpenClaw, and Hermes Agent.

Project Overview

Agentic SWMM Workflow is an open-source, verification-first framework for reproducible stormwater modeling with EPA SWMM. It supports automated execution, QA checks, provenance tracking, calibration support, documentation, and modeling memory, while keeping human modelers in control.

The goal is not to replace SWMM or the modeller, but to make SWMM-based modelling easier to reproduce, audit, remember, and trust. Agentic SWMM comes with aiswmm as its built-in runtime. Users can describe a modeling goal in natural language, while SWMM 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 modeling memory remain visible as reusable artifacts. Modeling 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

Quick try. The one-line installer below installs the current development build — fine for kicking the tires. For reproducible or production use, the pinned Docker image (:v0.7.0) is the recommended path (v0.6.4 remains pinned for companion-paper reproducibility) — see runtime install options.

macOS and Linux:

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

Windows PowerShell:

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

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.

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	SWMM execution-layer reproducibility, not agentic workflow reproducibility

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.

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.

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 vs Claude subscription backends, 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:

• [e-mail]
• [e-mail]

Citation

GitHub citation metadata is provided in CITATION.cff.

APA repository

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

APA manuscript / preprint

Zhang, Z., & Valeo, C. (2026). Agentic Modelling Pipeline: Reproducible Rapid Stormwater Modelling Management System with OpenClaw. https://doi.org/10.31223/X5F47G