Giving AI agents long-term memory without eating up all your VRAM (Hillock v0.5)

Hey everyone, One of the biggest headaches with building local agent workflows is managing persistent memory without blowing through your VRAM budget. Pulling in heavy vector databases and using LLM calls just to parse state changes gets expensive fast. I built Hillock as a lightweight neuro-symbolic memory engine designed specifically for local edge setups (<1.2 GB VRAM on a GTX 1070 or CPU mode). Instead of embedding raw text chunks into dense vector tables, it extracts structured Subject-Predicate-Object triples into SQLite in ~5 seconds using a small bi-encoder pipeline (GLiREL + MiniLM). Query gating and pronoun resolution run on the CPU in under 1ms using 10,000-dimensional hypervectors (VSA). This acts as a hard filter: if your agent asks about something that has no verified evidence in the graph, it gets a clean refusal without burning any LLM generation cycles. I just released v0.5.0 with token streaming, 1-click startup scripts, and live CLI commands like /inspect to view an entity's stored facts and synaptic weights in real time. I dropped the GitHub link in the comments for anyone interested in testing it with their agent loops!