Comment by bilbo-b-baggins
5 days ago
You forgot BM25 embeddings.
https://github.com/MikeS071/ai-engram
https://github.com/lamost423/openclaw-hybrid-memory
https://medium.com/@qdrddr/agentic-memory-framework-hindsigh...
https://clawhub.ai/vnesin-sarai/hybrid-retrieval
https://www.josecasanova.com/blog/openclaw-qmd-memory
https://medium.com/@richardhightower/stop-the-hallucinations...
https://github.com/oomkapwn/enquire-mcp#-why-its-the-best
https://github.com/rohitg00/agentmemory#key-capabilities
https://github.com/Melody-0321/NE-Memory-Core
https://github.com/ClaudioDrews/memory-os
https://en.wikipedia.org/wiki/Okapi_BM25
> It is based on the probabilistic retrieval framework developed in the 1970s and 1980s
Anyway, good for ya, hope you had fun building it.
I haven't seen one unique product in AI, everyone is building the same thing
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.
1 reply →
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..
BM25 is in my other project vecdb. mnemo's retrieval is graph-first — entity deduplication, multi-hop traversal, session-scoped scoring. Different tradeoff, not an oversight.