Given the abundance of vaguely similar local-first AI memory layers, it might be a good idea to add a "Why Mnemo" section right at the top of README.md to explain why folks should consider using it.
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.
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.
Everybody builds one. And, then they usually figure out that making the model fill its context with a bunch of memories hurts performance more often than it helps.
Given the abundance of vaguely similar local-first AI memory layers, it might be a good idea to add a "Why Mnemo" section right at the top of README.md to explain why folks should consider using it.
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.
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.
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.
Do any of them work properly yet?
Everybody builds one. And, then they usually figure out that making the model fill its context with a bunch of memories hurts performance more often than it helps.
That's why I always ask: got benchmarks?
Is there any relevance with another tool call mnemon?