Hi HN,

After building several AI agent systems, I kept running into the same frustration: memory layers that are either static vector stores or fragile prompt hacks. Retrieval is opaque, forgetting happens at the wrong time, and associations don’t form naturally.

So I threw away the two production memory systems I had and built something different. MuninnDB is a purpose-built cognitive memory database where memories (called engrams) are first-class citizens that:

- Strengthen with repeated co-activation (Hebbian learning) - Decay over time using a verbatim ACT-R formula - Automatically form bidirectional associations - Track their own Bayesian confidence - Return a full mathematical “Why” explanation on every retrieval

Everything runs as a single static Go binary (embedded Pebble LSM storage + HNSW + BM25). No external services, no Redis/Postgres/Pinecone, and no LLM in the hot path. One command (muninn init) auto-configures it with Cursor, Claude Desktop, VS Code, and any other MCP-compatible tool.

The core call is dead simple: Activate(context) returns ranked results + explainable scoring. Background workers handle learning and decay on every read.

GitHub: https://github.com/scrypster/muninndb Website + docs + install (one-liner): https://muninndb.com Quick 13-minute demo video: https://www.youtube.com/watch?v=b29wl0ehrQI

It’s very early (alpha, ~10 days old), but already functional and I’m using it daily. Would love honest feedback or questions from anyone working on agent memory, long-term RAG, or cognitive architectures.

Thanks!