> ## Documentation Index > Fetch the complete documentation index at: https://hillock.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # Hillock: Local Neuro-Symbolic Memory for AI Agents > Hillock is a three-tier neuro-symbolic memory engine: SQLite Knowledge Graph, Hebbian plasticity, and HDC for hallucination-free, fully offline AI memory. Hillock is a memory engine for AI agents that solves a problem every local LLM developer eventually hits: retrieval-augmented generation that still hallucinates, burns through VRAM, and falls apart the moment your document topics get specific. Instead of dense vector embeddings and probabilistic nearest-neighbor lookups, Hillock extracts structured Subject-Predicate-Object triples from your documents, stores them in a relational SQLite Knowledge Graph, and applies a hard mathematical gate using Hyperdimensional Computing before any query reaches the language model. If the answer isn't in the graph, Hillock says so — it never fills the gap with fabrication. ## Key benefits The HDC cosine-similarity gate operates at threshold 0.55. Queries that don't match stored hypervectors are blocked and returned as honest refusals — never passed to the LLM for creative gap-filling. The full three-tier pipeline fits in less than 1.2 GB of VRAM, comfortably within GTX 1070-class hardware. CPU-only mode requires no GPU at all — useful for Raspberry Pi and edge deployments. No API keys, no cloud calls, no telemetry. Hillock talks only to your local Ollama instance over `localhost`. Your documents never leave your machine. TALON (Tensor-Accelerated Local Ontology Network) uses tensor-based O(1) matrix classification to extract facts — no LLM pass during ingestion. A 30-sentence document ingests in roughly 5 seconds. Hillock exposes an OpenAI-compatible REST server on `http://localhost:8000/v1`. Any tool that speaks the OpenAI chat completions format — Open-WebUI, AnythingLLM, Obsidian — works out of the box. Switch between **STRICT** (single-sentence facts only), **BALANCED** (fact + light context + source citation), and **CONVERSATIONAL** (warm, associative responses using Hebbian memory links). ## How it works Hillock's pipeline has three stages that happen every time you ingest a document or ask a question: * **Ingest:** TALON parses your document with spaCy and GLiREL, resolves coreferences via FastCoref, and writes normalized SPO triples into the SQLite Knowledge Graph. No LLM is involved in this step. * **Store:** The Knowledge Graph holds entity nodes and typed relation edges. The Hebbian Plasticity Engine strengthens association weights between co-activated entities over repeated queries, surfacing contextually relevant priming. * **Retrieve with HDC gate:** At query time, the Hyperdimensional Reservoir encodes the query into a 10,000-dimensional binary hypervector and computes cosine similarity against stored fact hypervectors. Only facts that clear the 0.55 threshold are forwarded to the LLM renderer — everything else triggers a refusal. ## Who is Hillock for? Hillock is purpose-built for four groups of engineers and researchers: **AI engineers** building agents that need auditable, deterministic memory over private document sets — contracts, internal wikis, research papers — where hallucination is a business risk, not just an annoyance. **Local LLM developers** running Ollama, LM Studio, or llama.cpp on consumer GPUs who've outgrown vector databases and need something that fits inside their existing VRAM budget alongside the main language model. **Privacy-focused researchers** working with sensitive data — medical records, legal documents, proprietary datasets — where sending document content to any cloud endpoint is a non-starter. **Edge hardware users** targeting Raspberry Pi, Jetson Nano, or low-power x86 hardware, where Hillock's CPU-only mode and minimal RAM footprint make deployment practical without a dedicated GPU. Install Hillock, pull llama3.2 from Ollama, and run your first query from the CLI. See a side-by-side comparison of Hillock vs. Chroma, Pinecone, and standard RAG pipelines. This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.