Fair. The differentiator is the Rust single binary +
petgraph knowledge graph. No Python runtime, no cloud,
survives restarts. Built it because nothing local fit
that profile.
how to capture, what to capture, when to capture it.. where to put it.. how to make it 'useful'.. how to reinject it or make it accessible
the harness makers may well come up with better means than flat files, but there are loads of folks out there working across different harnesses and in teams, and there's very little that works in that respect.
Fair. The differentiator is the Rust single binary + petgraph knowledge graph. No Python runtime, no cloud, survives restarts. Built it because nothing local fit that profile.
I rolled the same thing in Go months ago as I am sure at least another 1000 people have in their own way.
Would genuinely be interested to see it. link? The graph traversal approach seems underexplored compared to pure vector search.
Do any of them work properly yet?
sure they do.. but it's painful
how to capture, what to capture, when to capture it.. where to put it.. how to make it 'useful'.. how to reinject it or make it accessible
the harness makers may well come up with better means than flat files, but there are loads of folks out there working across different harnesses and in teams, and there's very little that works in that respect.
why I built mori - https://github.com/fjwood69/mori
use it solo, use it in your homelab/office, use it in the cloud with a team..