ruvnet

Agenticow

Name: Agenticow
Author: ruvnet

AI community

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

Description

Git for Agent Memory: Copy-On-Write vector branching for embedded multi-agent memory (83x faster, 3000x smaller snapshots)

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

agenticow — Git for Agent Memory: Copy-On-Write vector branching (83× faster, 3000× smaller snapshots)

**Branch a base vector memory in ~0.5 ms / 162 bytes — independent of base size.** Exact read-through queries (parent ∪ edits, child wins). Built for embedded multi-agent memory.

[![npm](https://img.shields.io/npm/v/agenticow?color=3fe0c5)](https://www.npmjs.com/package/agenticow) [![license](https://img.shields.io/badge/license-MIT-blue)](./LICENSE) [![tests](https://img.shields.io/badge/tests-8%2F8%20passing-3fe07a)](./test) [![acceptance](https://img.shields.io/badge/acceptance-1000%20branches%20PASS-3fe07a)](#acceptance-the-1000-branch-proof)

**[Website / Demo →](https://ruvnet.github.io/agenticow/)** · **[npm](https://www.npmjs.com/package/agenticow)** · **[Benchmarks](#benchmarks)** · **[Acceptance proof](#acceptance-the-1000-branch-proof)**

![agenticow — Git for Agent Memory](./assets/hero.png)

**agenticow turns memory from a static database into a branchable runtime primitive for agents.**

Every other vector store makes you **full-copy** the index to snapshot, fork, or checkpoint it. `agenticow` **branches** it — copy-on-write, like Git. Creating a branch costs ~0.5 ms and 162 bytes whether the base holds 10,000 or 1,000,000 vectors. Query a branch and you transparently see `parent ∪ your edits`, with the child winning on id collisions and deletes honored.

npm install agenticow

Why

Agents need memory that branches: a per-user personalization layer, a sandbox to test a risky ingest, a checkpoint before a tool call, a thousand parallel experiments off one shared base. With a normal vector DB each of those is a **full copy** of the whole index. At 1M vectors that is **496 MB and 67 ms** — every time. agenticow makes it **162 bytes and 0.47 ms**, flat.