agent-memory-ultimate

# Agent Memory Ultimate

### Your agent forgets everything. Let's fix that.

You spend 20 minutes explaining your architecture. Next session: *"Could you remind me what we're working on?"*

You decide on PostgreSQL over MongoDB with detailed reasoning. After compaction: *"What database are you using?"*

You tell it your wife's name is Sasha. Three sessions later it calls her Sarah.

This isn't a bug. This is vanilla OpenClaw — context fills up, compaction kicks in, thread lost. Every. Single. Time.

### What we built

A memory skill that makes your agent *significantly* better at retaining what matters. Not perfect — that's still an unsolved problem in AI — but good enough that it stops asking "what project?" for the third time this week.

- **Semantic vector search** — finds relevant context even when you phrase things differently
- **Two-tier index** — fast retrieval instead of scanning 50KB of logs
- **Automatic consolidation** — decisions, preferences, and facts persist across sessions
- **Natural memory decay** — old irrelevant stuff fades, recent memories surface faster
- **60–80% token savings** — loads only what's relevant instead of dumping everything into context
- **SQLite-backed** — everything local, everything persistent, survives restarts
- **Fully offline** — no cloud, no API calls, no data leaving your machine

We didn't guess at the architecture. It's backed by two research papers:

- [**HIPPOCAMPUS**](https://github.com/globalcaos/clawdbot-moltbot-openclaw/blob/main/docs/papers/hippocampus.md) — biologically-inspired memory formation with O(1) concept lookup
- [**ENGRAM**](https://github.com/globalcaos/clawdbot-moltbot-openclaw/blob/main/docs/papers/context-compaction.md) — context compaction as cache eviction, not summarization. Summarization destroys detail. Cache eviction preserves it.

Because we're that kind of obsessive.

### What you get

An agent that remembers your tech stack, your preferences, your team's names, and the decisions you alre...