Db Memory

A vector-DB MCP server that gives Claude conversational memory: it stores solved problems + solutions as vectors and resurfaces the most relevant ones when a new request looks similar to something solved before.

• Switchable backend — VECTOR_BACKEND=local|cloud, no code change.
  • local → Chroma, embedded on disk. Offline, no account.
  • cloud → Qdrant Cloud, managed, shared across machines.
• Small/fast local embeddings — all-MiniLM-L6-v2 (384-dim). No API key, runs on CPU in ms.
• Optional web dashboard — flip on WEB_UI=on to browse the store in your browser. Off by default.

Tools exposed

Tool	What it does
search_memory(query, top_k)	Find past solved issues — returns lightweight headers (id + title + similarity) to save tokens
get_memory(ids)	Fetch the full problem + solution text for the ids you actually want
save_memory(problem, solution)	Store a solved issue for future retrieval (fire-and-forget background write). Claude confirms with you — "Did this solve your problem?" — before saving
update_memory(id, problem?, solution?)	Edit a memory in place; re-embeds if the problem text changes
delete_memory(ids)	Permanently remove out-of-date or wrong entries
memory_stats()	Show active backend + how many memories are stored

Retrieval is two-stage: search_memory returns only headers, then you call get_memory for the few you need — so a search never dumps every full solution into the context.

Setup

cd ~/code/mcp/db-memory
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
cp .env.example .env        # edit if you want cloud

Run / test standalone

mcp dev memory_server.py    # opens the MCP Inspector UI

Register with Claude Code

Local backend (default):

claude mcp add db-memory -- \
  ~/code/mcp/db-memory/.venv/bin/python ~/code/mcp/db-memory/memory_server.py

Then /mcp in Claude Code shows the tools. Claude will call search_memory when a request resembles a past one, and save_memory after solving something.

Switching to cloud

1. Create a free cluster at cloud.qdrant.io, copy the URL + API key.

2. In .env (or the MCP env):

   VECTOR_BACKEND=cloud
   QDRANT_URL=https://YOUR-CLUSTER.cloud.qdrant.io:6333
   QDRANT_API_KEY=...
   

Same embeddings, same tools — only the storage moves. (The two backends don't share data; re-save or migrate if you switch with existing memories.)

Web dashboard (optional)

A read-only page for browsing what's in the store — searchable, auto-refresh, shows every memory. Off by default; you turn it on and pick the port purely with env vars. When on, it autostarts with the MCP server (which Claude Code launches), running in a daemon thread — no extra process, no new dependencies (Python stdlib only).

Set these in the env block where the server is registered (e.g. the db-memory entry in ~/.claude.json), then restart Claude Code:

"env": { "WEB_UI": "on", "WEB_PORT": "8765", "WEB_HOST": "127.0.0.1" }

Env	Default	Meaning
WEB_UI	off	1/on/true/yes enables it; anything else keeps it off
WEB_PORT	8765	Port to serve on
WEB_HOST	127.0.0.1	Bind address — localhost-only by default; set 0.0.0.0 only to expose on your network

Then open http://localhost:8765. The page is a plain template at web/index.html that you can edit freely; the server just serves it plus two read-only JSON endpoints it calls (/api/stats, /api/memories).

Notes

• Embedding dimension is fixed by the model (384 for MiniLM). If you change EMBED_MODEL, delete the old Chroma memory_db/ or use a fresh Qdrant collection — vectors of different sizes can't mix.
• An MCP tool only runs when Claude invokes it; it can't passively log every message. For guaranteed full capture, log each turn to the same DB from your app (or a Claude Code Stop hook) and keep this server for retrieval.