Return to Data Core

Using GitHub and AI to Build Crestron SIMPL Windows Modules

2026-05-13
Crestron module workflow thumbnail showing AI-assisted module logic connected to code and AV controls

Using GitHub and AI to Build Crestron SIMPL Windows Modules

Working Title

AI-Assisted Crestron SIMPL Windows Development with GitHub and SIMPL+ Compiler Access

Environment

  • Platform: Crestron SIMPL Windows
  • Language: SIMPL+ and SIMPL Windows logic
  • Source control: GitHub
  • AI workflow: AI-assisted code generation and refactoring
  • Compiler: Crestron SIMPL+ compiler
  • Hardware: Crestron control processors, model varies by project
  • Firmware: Unknown
  • SIMPL Windows version: Unknown
  • SIMPL+ compiler version: Unknown

Problem Statement

Crestron SIMPL Windows programming has traditionally been a local, manual workflow. Modules are built inside SIMPL Windows, SIMPL+ code is edited and compiled through the Crestron toolchain, and project files often live on a programmer's workstation without the same source control discipline used in modern software development.

That works for small one-off jobs, but it becomes harder to manage when modules need to be reused, revised, reviewed, or shared across multiple systems. The bigger issue is that SIMPL+ code benefits from the same habits as normal software: version control, readable structure, repeatable builds, and careful change tracking.

The goal of this workflow is to bring Crestron programming closer to a modern development process without breaking the practical realities of SIMPL Windows.

AI-Readable Companion Markdown

For future AI assistants or automation runs that need a direct Markdown reference, this article has a companion note:

AI Reference: Crestron SIMPL Windows, GitHub, and AI Module Workflow

That file summarizes the workflow as machine-readable project guidance: repository layout, agent instructions, validation checklist, and the core compile-repair loop.

Workflow Overview

The workflow centers on GitHub as the source of truth for custom Crestron modules and supporting code. SIMPL Windows remains the system design and integration environment, while GitHub is used to track the SIMPL+ source, module revisions, documentation, and known working versions.

AI is used as a development assistant for module scaffolding, cleanup, documentation, and repetitive logic patterns. The important shift is that the AI should not stop at producing code for a programmer to paste into the SIMPL+ editor. In a stronger workflow, the AI has access to the local Crestron toolchain, runs the SIMPL+ compiler, reads the compiler output, fixes the source, and repeats until the module builds with 0 warnings and 0 errors. The final authority is still the SIMPL+ compiler and a controlled test environment.

Source Control Strategy

A typical repository layout can look like this:

crestron-modules/
  modules/
    display-control/
      src/
        display-control.usp
      compiled/
        display-control.ush
      docs/
        README.md
      examples/
        display-control-demo.smw
  shared/
    parsing/
    serial-helpers/
  README.md

The important rule is to keep the editable source files in GitHub. Compiled artifacts can be included when useful for deployment, but the source should always be present so the module can be reviewed, updated, and rebuilt.

Giving AI Access to the SIMPL+ Compiler

The SIMPL+ compiler is still part of the Crestron development toolchain. This is the crucial step: the AI workflow becomes much more reliable when the AI can call the compiler directly instead of handing back code that still needs to be copied into the SIMPL+ IDE by hand.

The goal is not "AI writes a module." The goal is "AI writes, compiles, reads the compiler result, fixes the module, and proves the build is clean."

The practical process is:

  1. Write or revise the SIMPL+ source in the repo.
  2. Use AI to generate or clean up the module structure.
  3. Let the AI invoke the installed SIMPL+ compiler against that source file.
  4. Capture the compiler output, including warnings, errors, and generated artifacts.
  5. Have the AI correct the source based on the actual compiler output.
  6. Repeat the compile loop until the result is 0 warnings and 0 errors.
  7. Commit the clean source and compiled module artifacts back to GitHub.
  8. Use the compiled module in SIMPL Windows and continue with hardware or simulator testing.

This keeps the AI workflow grounded. The AI can produce useful SIMPL+ patterns, but Crestron's compiler confirms whether the syntax and module structure are valid. A module should not be treated as complete just because the AI response looks correct. It should be treated as complete only after the compiler reports a clean build.

If the compiler is only available through the Crestron IDE on a workstation, the workstation still matters. The difference is that the agent should operate inside that environment, use the installed compiler as a build tool, and feed the real compiler results back into the edit loop.

AI-Assisted Module Development

The strongest use case for AI is not blindly generating entire control systems. The better workflow is to have AI assist with the kind of focused modules that usually slow down real Crestron work: API wrappers, protocol parsers, socket handlers, callback wiring, feedback mapping, and reusable logic blocks that have to compile cleanly before they are trusted.

In my own module repo, the useful targets are not just simple power/input examples. They are modules like:

  • A Neat Pulse SIMPL# / SIMPL+ cloud API module that polls Neat Pulse for call status, occupancy, room name, model, firmware, CO2, VOC, humidity, temperature, and illumination.
  • A SIMPL+ wrapper that exposes the SIMPL# driver cleanly to SIMPL Windows using RegisterDelegate and CALLBACK FUNCTION instead of the wrong event pattern.
  • Samsung Frame MDC IP control over TCP, including packet formatting, checksum handling, polling, socket reconnect behavior, power/input/mute feedback, and virtual remote commands.
  • Shure MXA920 mute and lobe feedback parsing, including receive-buffer handling so multiple frames in one TCP chunk are processed correctly.
  • Kramer VS-88H Protocol 2000 routing logic for matrix switching and analog route feedback.
  • Planar Colorlight Z5 protocol control, NVX routing helpers, scheduler logic, password/change-mode modules, and other reusable field modules.

A useful prompt might be:

Review this existing Crestron module repo and help improve the Neat Pulse API
module. The SIMPL# driver should perform HTTPS GET requests to the Neat Pulse
API, parse JSON for call status, occupancy, environmental sensor values, room
name, model, and firmware, then expose clean delegate callbacks to SIMPL+.

The SIMPL+ wrapper must use RegisterDelegate with CALLBACK FUNCTION, expose
digital, analog, and serial feedback signals that are easy to wire in SIMPL
Windows, and compile through the SIMPL+ compiler with 0 warnings and 0 errors.
Do not invent a new architecture unless the existing repo pattern is broken.

That is a more realistic AI workflow. The AI is not just writing fresh code from a blank prompt. It is reading the existing module, understanding the Crestron constraints, preserving the signal contract that SIMPL Windows expects, fixing the SIMPL+ / SIMPL# boundary, and using the compiler output as the truth source.

After AI updates the module, the next step is to review the signal names, simplify the logic, run the SIMPL+ compiler, and keep iterating until the compiler reports 0 warnings and 0 errors. Only after that should the module move into SIMPL Windows for integration and device-protocol testing.

Why GitHub Matters for SIMPL Windows Work

GitHub gives Crestron module development a memory. Instead of having versions scattered across folders like Final, Final_v2, and Actually_Final, each change can be tracked with a commit.

That helps with:

  • Reusing modules between jobs
  • Rolling back broken changes
  • Reviewing changes before deployment
  • Maintaining a library of known-good modules
  • Documenting device quirks and field fixes
  • Sharing code across workstations

For AV integration work, this is especially valuable because many problems are discovered in the field. A GitHub-backed workflow lets those field fixes become permanent improvements instead of one-off patches trapped on a laptop.

Recommended Commit Pattern

Use small commits that describe the actual module change:

Add Samsung display serial control module
Fix LG power feedback parser
Document M4250 Dante VLAN helper signals
Refactor Q-SYS named control wrapper

Avoid committing a large batch of unrelated module edits in one change. Smaller commits make it easier to identify when a behavior changed.

Practical Guardrails

AI-generated Crestron code should always be treated as a draft until compiled and tested. The pass/fail gate is not whether the code looks plausible. The pass/fail gate is whether the AI can compile the module through the installed SIMPL+ compiler with 0 warnings and 0 errors.

Before using a generated module on a live system, verify:

  • SIMPL+ compiler passes with 0 warnings and 0 errors
  • Signal names match the SIMPL Windows program
  • Serial strings match the manufacturer protocol
  • Timing and buffers are appropriate for the device
  • Feedback parsing works with real device responses
  • The module fails safely when the device is offline

Example Module Notes

A module README should include:

# Display Control Module

## Supported Devices
- Manufacturer: Unknown
- Model: Unknown
- Control method: RS-232 or TCP/IP

## Signals
- Power_On
- Power_Off
- Input_HDMI_1
- Input_HDMI_2
- Power_Is_On
- Power_Is_Off
- To_Device$
- From_Device$

## Compile Notes
Built and validated through the Crestron SIMPL+ compiler.
Compiler version: Unknown
Compile result: 0 warnings, 0 errors

## Field Notes
Document any protocol quirks, timing requirements, or known firmware behavior here.

Implementation Notes

This workflow is strongest when it is tied to the actual Crestron workstation and project repository:

  • Keep editable .usp source in GitHub.
  • Keep compiled artifacts only when they are useful for deployment or repeat testing.
  • Document the SIMPL Windows version, compiler version, processor target, and module signal contract when known.
  • Treat the compiler output as the pass/fail gate.
  • Treat hardware testing as a separate validation step after the module compiles cleanly.

The same pattern can be reused for display control, matrix routing, camera control, DSP feedback, API wrappers, and other focused modules where repeatable source control matters.

Publisher Note

Author: Darrell Cowan Publisher: AV Edge Tech

Published by Darrell Cowan of AV Edge Tech as part of a practical field-focused documentation workflow for AV programmers, integrators, and technical leads working with real control systems. The perspective here comes from hands-on AV development work: building Crestron modules, troubleshooting systems in the field, documenting repeatable fixes, and using modern software practices to make AV programming more maintainable.

The goal of this wiki is to turn day-to-day technical lessons into reusable documentation that helps other AV professionals move faster without losing the discipline required for reliable deployed systems.

Final Takeaway

Using GitHub with SIMPL Windows and SIMPL+ creates a cleaner development loop for Crestron programming. AI can help draft and organize modules quickly, but the workflow only becomes reliable when the AI has access to the SIMPL+ compiler and is required to iterate until the code compiles with 0 warnings and 0 errors. GitHub commits and real hardware testing then become the next layers of validation.

The result is a Crestron module library that is easier to maintain, easier to reuse, and better suited for long-term AV support.