ruvnet

Agentdb — AI skill for Claude Code

AI community

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

How to install Agentdb

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

What Agentdb does

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

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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.