I built Codex AI Gate: a local hook-based memory and guardrail layer for complex game development

Hi r/codex,

I've been working on a small open-source project called Codex AI Gate:

https://github.com/evildebris/codex-ai-gate

The project came out of a very practical problem: using Codex on a complex game project where there are many long-lived rules, gameplay constraints, QA expectations, engine-specific gotchas, and recurring "don't do this again" lessons.

In that kind of workflow, the issue is not just "give the model more context." The deeper problem is that memory systems can exist, but the model may not reliably consult them at the right moment, especially across long sessions, restarts, and context compaction.

Codex AI Gate is my attempt to add a local guardrail layer around Codex:

• SessionStart: injects a stable project/runtime contract at the beginning of a session
• Optional focused memory/provider probes: Basic Memory, Graphiti, CodeGraph, Lore, Hermes
• PostToolUse: checks the current Git diff for risky patterns like placeholder edits, "keep unchanged" style output, or lazy partial changes
• Stop: writes lightweight session summaries
• Agent-learning candidates: turns repeated failures, test results, or session evidence into reviewable memory/skill candidates
• Human review is the default, but review mode can be configured for teams that prefer faster self-learning loops
• Windows/macOS/Linux install paths are documented

The main goal is to make Codex more reliable on projects where the rules are too complex to keep repeating manually, especially game development workflows with long-term design rules, subjective QA lessons, and project-specific technical constraints.

This is not meant to replace tests, code review, or human QA. It is more like a local "execution gate" that forces the memory and rule layer to participate before and after Codex does the work.

I'd love feedback from anyone experimenting with Codex hooks, local memory systems, RAG/graph memory, game development workflows, or long-running agent projects.

Does this architecture match problems you've seen? What would make it easier to adopt?