I’m going to try this out. Curious how effective it’s been for your own use, OP? Anything particularly interesting or where it still falls over and requires tuning that you’ve observed? I find these types of projects interesting in sort of an anthropological sort of way. So far, I’ve yet to try anything that hasn’t still required a great deal of intervention to keep agents using guardrails without scolding them to do so. I guess this might force them more reliably, but I’ll need to try it and find out.
The hybrid search scoring is really well thought out. One question: are the 0.4/0.4/0.2 weights fixed or configurable? To me it feels like different use cases would want different balances (e.g. a coding assistant would want higher recency weights than a research tool would).
The 0.4/0.4/0.2 weights are just based of hand wavy trail an error, will look into other options. I could make them configurable?
I think that'd be cool, maybe just a simple config option in pyproject.toml or a .rekal/config.yml would cover most use cases: they could default to 0.4/0.4/0.2 but could be overriden.
Added a mcp command for it, but i might add it as a comfig as well?
All memories should not decay at the same rate. these weights should be configurable !
Thanks, i will look into making them configurable.
Nice clean project - Python 3.14 as a requirement might be an issue for adoption...
Thanks! You're right, that's just a relic of my dev environment. Will broaden the version requirement.