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.
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?
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.