You forget what was decided. The model forgets harder. Most memory tools capture what happened; MemContinuum does something different. - Rationale records what was decided, who decided it, and how that decision changed — then pushes the governing chain into the agent's prompt before it edits the file, so nobody has to remember to look. - Anatomy holds what the code already has — its concepts, owners and boundaries — so it stops being reinvented. - Decisions bind directly to the code they govern.
Hello, colleagues.
I've bitten off something ambitious.
Analyzing the problems surfacing on my own project ShotPorter https://ShotPorter.com, I decided I needed a long-term memory system.
Looking at what already exists, I found nothing that solved my problems, so I built my own. After using it for a while, I decided it was worth polishing and releasing publicly.
It became MemContinuum — long-term decision memory for Claude Code projects. And it is not just another memory system, collecting facts happened and words written.
**The problems it addresses.**
On a long project you forget what was decided about a given question and why. The model forgets harder. Subagents know nothing at all — the orchestrator dispatches them nearly blind onto narrow tasks. The result is reinvention instead of reuse: duplicate implementations, drift, tokens burned re-solving solved problems, and settled questions resurfacing as "wait, why is this written this way?"
**How it differs from the memory systems I looked at:**
- Two layers: ANATOMY - an indexed map of the code, and RATIONALES - decision chains recorded against it — what was decided, **who** decided it, how it changed over time, plus incidents and rejected alternatives with reasons.
- Reading is automatic. Before an agent edits a file, the decision chain governing that path is injected into its prompt. Nobody has to remember to look.
- Writing is unavoidable but not automatic. The agent gets a question it must answer; "nothing to record" is a legitimate answer. It's moderated by judgment, not a scraper dumping everything into a pile by keyword or timestamp.
- One AI writes records — the orchestrator. Subagents and external reviewers (Codex, Grok) propose through an inbox; proposals become records after triage.
- Per project, local, no server. Markdown as the source of truth, SQLite as a disposable index.
Built for coding projects specifically: without indexable code only half the brain works.
Current state: 0.2.0rc4, honestly labelled a release candidate. MIT. Claude Code only for now. The README is long and detailed if you want the full picture.
Feedback of any kind is very welcome.
Besides me, a team of authors worked on this project:
- Claude Code: Fable 5/5.1 as lead engineer and project manager; Opus as inspector; Sonnet as coder; Haiku as tester
- Codex: 5.6 Sol / 6 Astra as reviewer and outside consultant
- Grok 4.6 as second reviewer
https://github.com/krakozavr/Mem...