akitaonrails/ai-memory

> start OpenAI Codex in the same directory, continue without
> re-explaining the architecture, the failed approaches, or the open
> questions.
Website: aimemory.sh
## Why ai-memory
Your coding agent probably has a memory feature already. Claude Code takes its
own notes, Cursor remembers some things, and every platform is adding more. They
all have the same limits: the notes live on one machine, belong to one agent,
and drop out of view when you switch tools or hand the work to a teammate.
ai-memory removes those limits.
It follows you across agents. More than twenty harnesses (Claude Code,
Codex, Cursor, Gemini CLI, OpenCode, Grok, Devin, Kimi, Kiro, and others)
feed one shared memory. Quit Claude Code mid-task, open Codex in the same
directory, and the next agent picks up a handoff that says where you left
off, what failed, and what is still open. Handoffs are a typed protocol:
each one has an owner and is claimed exactly once.
It can carry the session, not just a summary. Start agents with
ai-memory run: quit Claude Code, run ai-memory run codex, and Codex gets
the shared workstream plus a private, temporary local projection of Claude's
visible native history for exact details the bounded startup packet omitted.
The remote server receives only the harness and session identity, never the
local transcript path or body. Direct harness launches behave exactly as
before.
It follows you across projects. A small profile of how you usually
work (your package manager, test layout, architecture habits) reaches
every new repository at session start, so you stop re-explaining yourself.
It sits below each repository's rules file as a default. It is on by
default for a single user, and on a shared server it is opt-in and private
per person. See docs/cross-project-profile.md.
It follows you across machines. Memory lives in a server you run, on
the same laptop, a homelab box, or anywhere else, so the project you left
on the desktop is the project you resume on the laptop, with the same
knowledge and the same open questions.
It works for a team. Point everyone at one server, and what one
person's sessions learn, everyone's agents can retrieve. Knowledge is
shared per project; personal handoffs stay personal. Multi-user auth,
per-person attribution, and an audit log are built in and are not a paid tier.
Your memory is plain markdown. The source of truth is a git-backed
wiki of ordinary .md files: grep it, open it in Obsidian, edit it by
hand, rsync it. The database is a derived index that can always be
rebuilt from the files. There is no vector store to maintain and no
content locked in a binary blob.
It captures the work itself, silently. Lifecycle hooks record what
happened (prompts, tool calls, session boundaries), sanitize it at a typed
privacy boundary before anything is stored, and the server consolidates it
into readable pages. You do not have to ask it to remember anything. The
default path uses zero LLM calls: capture, search, and handoffs all
work with no API key.
It ages memory without an LLM. Memory decays on a schedule you can
tune per tier, and memory you use decays slower: opening a page,
searching it, or reaching it through a link counts as use. When episodic