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.
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.
I haven't seen one unique product in AI, everyone is building the same thing
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.