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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.
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After working with LLMs a bunch, I now want them to forget everything every time I end the conversation. Otherwise they get dumber and more confused over time. LLMs do not have memory and these "memory" systems that everyone makes don't change that fact. They just clutter up context with probably irrelevant noise. I don't want the LLM to remember everything I've ever said and try to make every project align with ofte…
Yep, the memory in the ChatGPT macOS app is also starting to piss me off. I think developers generally dislike ‘hidden’ state, which is what memory essentially becomes.
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Or just wait a week and whatever’s built into your harness de jour will be as good or better than whatever homebrew solutions are out there > Most LLMs forget everything the moment a conversation ends. mnemo fixes that Even the opening line of the README is obviously very out of date. Might be true if you’re raw-dogging a model or using a basic agent SDK
After working with LLMs a bunch, I now want them to forget everything every time I end the conversation. Otherwise they get dumber and more confused over time. LLMs do not have memory and these "memory" systems that everyone makes don't change that fact. They just clutter up context with probably irrelevant noise. I don't want the LLM to remember everything I've ever said and try to make every project align with ofte…
Nice approach, the Rust performance and single-binary deployment are compelling. Question, how do you handle contradictory facts? If John moves from Stripe to Google, does the graph resolve that, or does it store both?
It looks like this is left as an exercise for the student?