I built a cross-client AI memory hub — 23 AI coding agents sharing one SQLite file (local-first, no cloud)

I run 8+ AI coding agents daily (Claude Code, Cursor, Windsurf, Codex, etc.) and they all have amnesia between sessions — worse, they don't share memory with each other.

Existing solutions (mem0, Zep, Letta) are cloud/server-based. I wanted something local and dead simple: just make all agents point to the same SQLite file.

So I built MemTether — a memory hub that works via file-level pointers (junction/symlink). No cloud, no API fees, no abstraction layer.

Key features:

\- 23 client adapters (auto-detect and connect)

\- Source attribution (knows which agent wrote each memory)

\- Bi-temporal (what was true vs what the system knew)

\- Q-Value ranking (memories that get used rank higher)

\- FTS5 + vector search (bge-m3, local embedding)

\- MCP server included

Stack: Python, SQLite FTS5, ChromaDB, FastAPI. All local.

GitHub: https://github.com/MemTether/MemTether

PyPI: pip install memtether

Blog with design decisions: https://dev.to/lanbass869cell/i-built-a-cross-client-memory-hub-for-ai-agents-heres-what-i-learned-418l

Would love feedback from people who juggle multiple AI coding tools.