Show IH: I was my AI coding agent's memory — so I automated myself out of that job
For months I had an unpaid part-time job: being my coding agent's memory.
Long sessions (Codex, Claude Code, Cursor — the pattern shows up across harnesses) eventually "compact": the context window fills, old state gets summarized, and the agent keeps going — minus the why. Decisions turn into vague one-liners, dead ends get retried, and I re-type the same constraint for the third time that week.
So I built agent-crystallize and open-sourced it: a local-first CLI that saves durable Markdown artifacts into your repo before the boundary eats the context — lightweight checkpoints while you work, a fuller "crystal" (decisions + evidence + open loops + resume prompt) before compaction or session end. The next session resumes from the artifact instead of from my memory of what I told it last time.
Favorite moment so far: the session where I published it hit compaction mid-launch and recovered from its own checkpoint. The product demoed itself.
- Repo: github.com/stewie-sh/agent-crystallize (Apache-2.0, no account, no server; install is one
npm install -gaway — command in the README) - Why compaction loss compounds — the full write-up
Curious about your coping ritual: do you babysit the context meter and save by hand, spawn a fresh session per task, or just accept the amnesia?
The compaction-loss problem is real and it compounds in a specific way I didn't expect until it bit us: it's not just the reasoning that gets lost, it's the negative results. We built a small digital-products storefront and every dead end (a marketplace signup that hit a bot wall, a subreddit that bans self-promo) needs to survive the next session's compaction or the agent just retries the same dead thing a week later, confidently. Our fix was cruder than yours: a plain append-only journal file the agent is told to read before acting, git-style, instead of trusting its own context. Your crystallize idea of writing the artifact before compaction eats it, not after, is the better version of that. Following the repo.
What stood out to me is that you're treating context as an asset instead of a temporary resource.
The problem isn't just that AI forgets. It's that the reasoning behind important decisions gradually disappears, forcing humans to recreate it. Preserving that reasoning may end up being more valuable than preserving the conversation itself.