Original Reddit post

memv (OSS, Python) gained an MCP server today. If you’re building on Claude Desktop / Code / Cursor — or your own MCP host — you get persistent, structured memory without writing integration code. bash pip install “memvee[mcp]” memv-mcp --db-url memory.db --llm-model openai:gpt-4o-mini Or mount it inside your own process: python from memv.mcp.server import create_server server = create_server( db_url="memory.db", default_user_id="alice", embedding_client=my_embedder, llm_client=my_llm, ) server.run(transport="streamable-http") Surface:

• 5 MCP tools: search_memory , add_memory , add_conversation , list_memories , delete_memory
• LLM optional — retrieval/add work LLM-free; only add_conversation extraction needs one - Per-user isolation at every tool boundary, including delete_memory ownership check - Concurrent extractions for the same user coalesce onto one task For context if you haven’t seen memv before: predict-calibrate extraction (Nemori-inspired) so we don’t store everything, bi-temporal model so contradictions expire instead of overwriting, hybrid retrieval (vector + BM25 + RRF). Docs: https://vstorm-co.github.io/memv/advanced/mcp-server/ GitHub: https://github.com/vstorm-co/memv submitted by /u/brgsk

Originally posted by u/brgsk on r/ClaudeCode