ruvnet

Agentdb

Name: Agentdb
Author: ruvnet

AI community

SOURCE https://github.com/ruvnet/agentdb

Description

Vector memory that gets smarter every time your agent uses it.

Installation

This entry records only its repository, not the path inside it, so there is no exact command to give. Open the source below and copy the folder into ~/.claude/skills/, or the file into ~/.claude/agents/.

README

[![npm version](https://img.shields.io/npm/v/agentdb.svg?style=for-the-badge&logo=npm&color=cb3837)](https://www.npmjs.com/package/agentdb) [![npm downloads](https://img.shields.io/npm/dm/agentdb.svg?style=for-the-badge&logo=npm&color=cb3837)](https://www.npmjs.com/package/agentdb) [![License](https://img.shields.io/badge/License-MIT_OR_Apache--2.0-yellow?style=for-the-badge)](LICENSE) [![TypeScript](https://img.shields.io/badge/TypeScript-5.x-3178c6?style=for-the-badge&logo=typescript&logoColor=white)](https://www.typescriptlang.org/)

[![🕸️ RuVector Engine](https://img.shields.io/badge/RuVector_Engine-Rust_+_NAPI-06b6d4?style=for-the-badge&logoColor=white&logo=rust)](https://github.com/ruvnet/ruvector) [![🧠 SONA Self-Learning](https://img.shields.io/badge/SONA-Self_Learning-8b5cf6?style=for-the-badge&logoColor=white)](#-self-learning-loop) [![🔌 MCP Compatible](https://img.shields.io/badge/MCP-41_tools-D97757?style=for-the-badge&logoColor=white&logo=anthropic)](#-mcp-integration) [![⭐ Star on GitHub](https://img.shields.io/github/stars/ruvnet/agentdb?style=for-the-badge&logo=github&color=gold)](https://github.com/ruvnet/agentdb)

AgentDB

**Vector memory that gets smarter every time your agent uses it.**

A single-file cognitive container — vectors, indexes, learning state, and a cryptographic audit trail in one `.rvf`. Self-learning search improves up to **36% from feedback alone**, with no manual tuning. Runs in Node, the browser, edge runtimes, and offline.

Why AgentDB?

Most vector databases store embeddings and call it done. AgentDB watches *which* results your agent actually used, learns from that signal, and ranks the next query better. The bandit underneath also picks the right RL algorithm, the right compression tier, and the right pattern weighting on its own — so the database itself gets sharper while you focus on the agent.