Show HN: Vektor – local-first associative memory for AI agents
TL;DR
Vektor is a local-first memory system for AI agents – no cloud, all data stored via SQLite on-device.
Key Points
- Its core is a MAGMA graph with four memory layers that maps associative links between stored memories.
- The AUDN curation loop automatically decides for each new input: add, update, delete, or no-op.
- A background REM process compresses and consolidates memory contents, analogous to sleep consolidation in humans.
- Installable via npm (vektor-slipstream) with built-in Claude tool support; version 1.3.6 is approaching final release.
Nauti's Take
The combination of MAGMA graph, AUDN loop, and REM compression suggests a thoughtfully designed system architecture – and it is refreshing to see an agent tool that treats privacy by design as a real constraint rather than a marketing claim. That said, a one-point HN post with zero comments is not proof of production-readiness.
The open call for DB testing expertise signals the product is still pre-stable, which is honestly communicated but warrants caution before dropping it into critical agent pipelines. Anyone building persistent, off-cloud memory for AI agents should keep Vektor on the radar – just wait for a solid post-1.3.
6 review before committing.
Sources