Login

u/Kimetsu-IA

r/OpenAIDev r/codex r/WhatIsMyCQS
▲ 1 r/OpenAIDev+1 crossposts

I got tired of Codex forgetting everything between sessions, so I built a memory. It's free and the numbers are decent

Every new Codex session I was re-explaining the same conventions, watching it re-explore the same repo, re-hitting the same landmine that burned it last week. So I spent the last few months building Kimetsu, a memory sidecar that wires into Codex over MCP with one command:

npm install -g kimetsu-ai kimetsu setup --host codex

After that, Codex records lessons as it works and gets them back before the next task. Memories that actually help get promoted, stale ones decay and get pruned. The whole brain is one SQLite file in your repo.

The part I care most about: storing and retrieving memories costs $0. No LLM in the memory pipeline at all, it's FTS + local embeddings + a local reranker. Most alternatives (mem0, Zep, Cognee) call a model on every write and often every read, so your memory has a meter running. Mine runs offline.

On the public benchmarks it holds up better than I expected for something model-free: 83% on LongMemEval (strong systems land 60-80, the 90+ scores use oracle retrieval or much heavier readers), and on BEAM's 1M-token bucket it scores 66% vs mem0's self-reported 62%. On a 16-task Terminal-Bench slice it was about 13x cheaper per solved task than running without it. Full methodology is on the site, I publish the harness so you can check me.

Recent thing I'm having fun with: brains are portable files now.

kimetsu brain export team.json.gz

Gives you a gzipped pack (credentials and PII get scrubbed automatically), a teammate imports it and it merges with dedup. You can also swap whole brains in and out, or install one from a URL. Onboarding a new machine is one import.

MIT/Apache, Rust, no telemetry, no cloud. Also works with other coding agents.

reddit.com
u/Kimetsu-IA — 3 days ago
▲ 3 r/OpenAIDev

I built a local, open-source memory for Codex (and other coding agents)

I've been building Kimetsu for the last few months and running it on real work at my company. It's been solid, so I open-sourced.

It's a memory layer for coding agents, but the part I think is actually interesting: it's proactive, not reactive. Most memory tools wait for the agent to query them. Kimetsu surfaces relevant past context and warns about known failure patterns before the agent repeats a mistake, without being asked. It also tunes itself on your own memories: it tracks which ones actually got used and sharpens retrieval around them, so it gets better the more you work.

  • Works with Codex and other code agents (Claude Code, Cursor, Gemini CLI).
  • Everything is local: retrieval, embeddings, storage. One SQLite file per project, no cloud, no telemetry. There's also a remote version if you want a shared brain across a team or machines.
  • Fully configurable: pick your embedding model, reranker, and the LLM used for distillation and ask (OpenAI, Anthropic, or a local Ollama model). The retrieval backend is pluggable too, with a graph mode for typed memory links.
  • Benchmarked across code agents: almost 13x cheaper per win with the brain on. Retrieval itself is measured too (recall@4 0.949, reproducible with kimetsu brain bench)

Install: npm install -g kimetsu-ai

Repo: https://github.com/RodCor/kimetsu

Built it for myself, happy to answer anything.

u/Kimetsu-IA — 12 days ago