h-wata/kioku-mesh

<p align="center"> <img src="docs/assets/kioku-mesh-logo.png" alt="kioku-mesh" width="420"> </p> <p align="center"> <a href="https://pypi.org/project/kioku-mesh/"><img src="https://img.shields.io/pypi/v/kioku-mesh.svg" alt="PyPI"></a> <a href="https://pypi.org/project/kioku-mesh/"><img src="https://img.shields.io/pypi/pyversions/kioku-mesh.svg" alt="Python"></a> <a href="LICENSE"><img src="https://img.shields.io/github/license/h-wata/kioku-mesh.svg" alt="License"></a> </p> <p align="center"> <strong>Shared memory for AI coding agents, across tools and machines.</strong> </p> <p align="center"> <img src="docs/assets/demo.gif" alt="One agent saves a decision; another agent recalls it over the mesh" width="760"> </p>

kioku (記憶) means memory.

kioku-mesh gives coding agents a shared memory store. Claude Code, Codex CLI, Gemini CLI, and other MCP clients can save and search the same observations from one machine or from several machines on a trusted LAN/VPN mesh.

The default setup is local and needs no daemon. Mesh mode is available when you want the same memory pool replicated between hosts.

Why kioku-mesh

Coding-agent context gets fragmented across machines: which laptop did that work, what did the agent on the other host decide, and why does a secondary agent have to re-read everything from scratch just to give a quick second opinion? kioku-mesh keeps that memory in one shared pool so any agent, on any of your machines, can recall it.

Unlike long-term memory tools that store everything in one place, the shared pool is a peer-to-peer mesh you run yourself across your own machines (LAN / VPN / Tailscale) — no SaaS, no central account. The same memory is readable by Claude Code, Codex CLI, Gemini CLI, and any other MCP client.

Quickstart

uv tool install kioku-mesh
# or: pip install kioku-mesh

kioku-mesh init --mode local
kioku-mesh save "Chose Postgres over SQLite for analytics"
kioku-mesh search "Postgres"

Install the MCP serv