RuVector

// Overview

RuVector is a vector store written in Rust and aimed squarely at one job: giving an agent memory that survives between sessions. It combines local semantic embeddings, persistent vector retrieval, graph relationships between records, explicit feedback learning and memory lifecycle controls in a single embeddable library rather than a server you have to operate.

The default retrieval path runs entirely on the local machine. The first semantic command downloads and caches an all-MiniLM-L6-v2 model, so there is no database server to start and no API key to configure; hosted services are optional and, as the project puts it, create a separate data boundary. Learning is driven by recorded outcomes and feedback rather than by reads alone.

Three interfaces sit on the same core: a Rust crate (ruvector-core), a Node.js package with an OnnxEmbedder and VectorDB class, and an npx-runnable CLI whose hooks subcommands remember and recall directly from the shell. Storage is a file you point at, and reopening the same path recovers the vectors, metadata, configuration and searchability. The project is MIT-licensed.

// What it does

• Local semantic embeddings from a bundled ONNX all-MiniLM-L6-v2 model, cached on first use — no embedding API required
• Persistent on-disk store: reopen the same path in another process and vectors, metadata and configuration come back
• Metadata filters and graph relationships alongside plain similarity search, so recall can be scoped by tenant, kind or time
• Feedback learning that adapts ranking from recorded outcomes rather than from reads
• Three surfaces over one engine — Rust crate, Node.js package and an npx CLI with remember/recall hooks
• Memory lifecycle controls and optional shared memory for multi-agent setups

// Getting started

Nothing needs to be installed or provisioned to try it: the CLI runs through npx and needs no database server or API key.

npx ruvector hooks remember --semantic --type decision \
  "The customer requires all inference to remain in Canada."

npx ruvector hooks recall --semantic --top-k 3 \
  "Where may customer data be processed?"

npx ruvector hooks stats

const { OnnxEmbedder, VectorDB } = require('ruvector');

const embedder = new OnnxEmbedder();
await embedder.init();

const db = new VectorDB({
  dimensions: 384,
  distanceMetric: 'cosine',
  storagePath: './agent-memory.db',
});

const vector = await embedder.embedPassage('The Toronto pilot passed its privacy review on Tuesday.');
await db.insert({ id: 'episode-1', vector, metadata: { tenant: 'acme' } });

const query = await embedder.embedQuery('Where may the customer data be processed?');
const results = await db.search({ vector: query, k: 3, filter: { tenant: 'acme' } });

Commands and code are distilled from the project's own documentation — always check the official repo for the latest.

// When to use it

• Add cross-session memory to a coding agent without standing up a vector database service
• Keep embeddings and recall entirely on the local machine when the data cannot leave it
• Store decisions, episodes and procedures as typed records and retrieve them by similarity plus metadata filters
• Build a Rust agent that needs an embedded vector index rather than a network hop per query

// How RuVector compares

RuVector alongside other open-source vector databases tools AI/TLDR tracks, ranked by GitHub stars.