How would you improve reasoning + memory in a local AI companion?

I'm building a local AI companion and I'm currently working on its cognitive layer. The goal is: User message → understand intent → decide what context is relevant → retrieve only useful memories/state → reason about the context → generate response → update memory/state It currently has long-term memory, interests, mood/emotional state, identity and project context, but I'm trying to improve the quality of context selection and reasoning, especially with a small local model. I'm curious how you'd approach: Better memory/context selection without flooding the prompt Handling conflicting or outdated memories Deciding when a memory is actually relevant Giving the model better reasoning before answering Modeling persistent mood/interests without making responses repetitive For those building local agents/companions: what approaches have worked well for you? submitted by /u/Feathered-Beast [link] [comments]

I got tired of flat RAG, so I built a 3D Multi-Agent Memory Core with 'Fading Gossip' (Open Source)

Source: r/ollama | 2026-08-23

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