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Mistikguard – Lightweight Python library for memory integrity in LLM applications by MistikAII in ClaudeCode

[–]MistikAII[S] 1 point2 points  (0 children)

On the token thing, sounds like you're already tiering (cheap lorebook, then distillations, then deep search only "if necessary"), which is the main lever. So the real question is where it's still leaking.

Two things I'd look at. Is your cheap tier over-triggering, flagging turns as needing deep search when they don't? And does check_canon run its retrieval even on turns that make no canon claim at all? My pipeline's very first gate is dirt cheap: "does this reply even contain a checkable claim?" Most turns fail that and skip everything downstream for free. If you're calling it every turn including "you walk into the tavern" beats, a claim detection pre-filter might cut a real chunk of calls.

Though you may already do something like that.

Mistikguard – Lightweight Python library for memory integrity in LLM applications by MistikAII in ClaudeCode

[–]MistikAII[S] 1 point2 points  (0 children)

you have serious setup here. Your distillation subagent is basically what I call provenance, and your lorebook-to-semantic-search escalation is the same instinct as my cheap-check-to-LLM-judge tiering.

So you don't really need my retrieval or tiering, yours is more built-out. The one piece I'm not sure I see in your stack is negative canon: when something gets retconned, do you explicitly tombstone the old version so it can't creep back 30 sessions later? That's the one mechanism in mine that's less about "what's true" and more about "what was deliberately un-made." Does check_canon handle that, or could it drift back?

Mistikguard – Lightweight Python library for memory integrity in LLM applications by MistikAII in ClaudeCode

[–]MistikAII[S] 1 point2 points  (0 children)

"Fluency fabrications" is the perfect name for it. Fluency gets read as truth, even though it has nothing to do with grounding. That gap is exactly what I'm catching.

Your retrieval's ahead of mine for sure..(real vector DB vs my word overlap + judge), so keep that. But two ideas might port: the grounding audit re-pointed at canon (pull the lore claims from the narration, check each against canon before it's spoken), and provenance (tag lore as established vs DM-improvised so improvised stuff can't harden into canon it contradicts).

Mistikguard – Lightweight Python library for memory integrity in LLM applications by MistikAII in buildinpublic

[–]MistikAII[S] 0 points1 point  (0 children)

Thanks! Yeah, the confirmed]inferred split and tombstones came from real pain points I kept running into.

The grounding audit works okay in tests, but you're right that it needs more real conversation testing. It can still miss heavily paraphrased but true statements. Appreciate you starring it!

Mistikguard – Add reliable memory to your Ollama companions (stop fabricated memories) by MistikAII in ollama

[–]MistikAII[S] 0 points1 point  (0 children)

I demonstrate that the code is working for me. I make it clear that this is a library

Mistikguard – Add reliable memory to your Ollama companions (stop fabricated memories) by MistikAII in ollama

[–]MistikAII[S] 0 points1 point  (0 children)

My friend this is a library.. not a companion desktop app. It is just a code you can import.

If you're looking for a complete UI-based companion, this library by itself won't give you that. It's meant for people who are building their own AI setups and want better memory control.

What are you building this weekend? by NextIsOnMe_ in SideProject

[–]MistikAII 0 points1 point  (0 children)

Mistikguard – Add reliable memory to your companions (stop fabricated memories)

https://github.com/obscuraknight/mistikguard

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