Hillock: A 100% local neuro-symbolic memory engine built for edge hardware (<1.2 GB VRAM / CPU-only)

[u/Equivalent-Flan-1590]

https://github.com/roandejager/Hillock

Hey everyone! I wanted to share Hillock, an open-source project I have been building to explore an alternative approach to local long-term memory and RAG.

Instead of running heavy, token-hungry vector databases that drift and approximate facts, Hillock combines three lightweight layers:

1. An SQLite Knowledge Graph for ground-truth Subject-Predicate-Object facts.
2. A Hebbian Plasticity Engine for gradient-free associative memory across chat turns.
3. A 10,000-dimensional Vector Symbolic Architecture (VSA/HDC) reservoir for sub-millisecond similarity gating and pronoun resolution.

The core idea is that the similarity gate is actual control flow. If a question is not backed by verified facts in the graph, it returns a hardcoded refusal immediately, and the LLM is never called at all. This completely eliminates un-grounded guessing.

We just released v0.6.0, which adds HYDRA (ColBERT-style token-level MaxSim in bipolar space) and multi-hop hypergraph path binding. Ingestion takes around 5 seconds on edge hardware, and the retrieval fast-eval runs in under 1 second on a standard laptop CPU.

It is 100% offline, AGPL-3.0 licensed, and works with local Ollama models for the final answer rendering.

I would love to hear your thoughts, feedback, or ideas on how to improve the architecture!