memv MCP Server: Persistent Structured Memory for AI Agents

memv (open-source, Python) has released an MCP server, making its persistent, structured memory layer usable from any MCP client — including Claude Desktop, Code, Cursor, or custom hosts.

Quick Setup

Install via pip and run the server with a single command:

pip install "memvee[mcp]"
memv-mcp --db-url memory.db --llm-model openai:gpt-4o-mini

You can also embed the server inside your own Python process:

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")

Five MCP Tools

• search_memory — hybrid retrieval (vector + BM25 + RRF)
• add_memory — directly insert structured memory
• add_conversation — extract and store memories from a conversation (requires LLM)
• list_memories — list stored memories for a user
• delete_memory — delete with ownership check

Key Features

• LLM-optional: retrieval and direct add_memory work without an LLM; only add_conversation extraction needs one.
• Per-user isolation: every tool respects user boundaries, including ownership verification on delete_memory.
• Concurrent coalescing: multiple extractions for the same user merge into one task.
• Predict-calibrate extraction: inspired by Nemori, avoids storing everything.
• Bi-temporal model: contradictions expire rather than overwrite.
• Hybrid retrieval: combines vector search, BM25, and reciprocal rank fusion (RRF).

Docs: https://vstorm-co.github.io/memv/advanced/mcp-server/

GitHub: https://github.com/vstorm-co/memv