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.

Tools exposed

Related MCP server: MCP Memory

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 three 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.)

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.