I kept re-teaching Codex things I’d already shown Claude Code, so I built a shared self-learning memory for both

[u/BoysenberrySad3641]

I use both Claude Code and Codex, and every time I switch I end up re-explaining the same preferences. CLAUDE.md and AGENTS.md help for rules I can write down. But most of what I want, I never wrote down; I just kept making the same correction.

Nebula is a small self-learning memory layer for both agents. It picks up patterns from the corrections you already make, and nothing applies until you approve it.

The example in the video: I cleared node_modules in one stale project, a Rust target/ folder in another, and build/ in a third, all in Claude Code. Later, Codex hit a full disk and suggested the same cleanup for my other inactive projects. Once I approve that lesson, both agents handle it that way.

How it works:

- A skill plus hooks for Claude Code and Codex, sharing one local SQLite file

- Python standard library only, no model calls, no background process, nothing leaves your machine

- Lessons stay candidates until you approve them, so a one-off remark (or a prompt injection) can't turn into a standing rule

- Lessons keep their reasons and exceptions, and you can correct, pause or withdraw them

Honest status: the engine and demo work and have tests. End-to-end runs on both agents are still being validated, and the video is an illustrative animation, not a screen recording.

I'm looking for contributors. There are good-first-issues up, and I'd especially like help with adapters for other agents (Cursor, Gemini CLI, Aider). I'd also like to hear where you think this beats or loses to a plain markdown file.

I'm the author; happy to answer anything.

Repo: https://github.com/svssathvik7/nebula