Memtether MCP server

Your AI agents can now share memories.

Local-first - No cloud - No API fees - One physical memory.db shared by 23+ clients

Quick Start | Why MemTether | Architecture | Benchmarks | Known Limitations | 中文文档

Quick Start

mcp-name: io.github.lanbass869-cell/memtether

# 1. Install
pip install memtether

# 2. Initialize (creates a demo memory DB)
memtether init

# 3. Connect all detected AI clients (Claude Code, Cursor, Windsurf, etc.)
memtether connect --all

Try it:

memtether search "shared memory"
memtether remember "my first shared memory"
memtether stats

Or on Windows, double-click install.bat for one-click setup.

More install options

# From source
git clone https://github.com/MemTether/MemTether.git && cd MemTether && pip install -e .

# With semantic search (local embedding, no cloud)
pip install "memtether[vector]"

# With REST API server
pip install "memtether[server]"

# Docker
docker build -t memtether . && docker run -p 8420:8420 -v ./data:/app/data memtether
docker-compose up

Try it without installing:

python scripts/make_demo_db.py
MEM_DB=~/.memtether/demo.db python mem.py search "shared memory"

Why MemTether

The problem

You use Claude Code for coding, Cursor for refactoring, and Windsurf for exploration. Each has its own memory. Switch tools and your AI forgets everything.

MemTether's answer is simpler than you'd expect: make them all point to the same file.

How it is different

Feature comparison

Shortened. The full README is on GitHub.