AI/ML●●Solid
Deterministic symbolic memory layer for grounding LLMs
Identity-based memory vs similarity—clean separation of deterministic truth from probabilistic reasoning.
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Th3Hypn0tist
104mo ago
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Reflection-based memory layer for LLM apps. Ingest WhatsApp, Twitter, LinkedIn, Telegram — build a cognitive profile of how you think.
3 starsPython
Experience-engine – reflection-based memory layer for local LLMs
by ashishluthara·Feb 18, 2026·3 points·1 comment
AI Analysis
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The Take
Turns chat history into structured 'belief' and 'cognitive pattern' blocks you can inject into prompts, with simple APIs like run_reflection and run_synthesis that read like a research prototype. It's smart about separating V1 (domain beliefs) from V2 (transferable cognitive patterns), but it's clearly early-stage — tiny repo, Ollama-only workflow, and few commits mean you should treat it as an experimental MVP rather than a drop-in production memory system.
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Target Audience
Developers building chatbots and local LLM apps, hobbyists running Ollama, and researchers experimenting with LLM memory models
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