Labsco
MCP SERVER · OFFICIAL PROJECT

SelfMemory

by SelfMemory

Give an assistant a personal memory it can add to and search across chats, filtered by tag, person, topic, or time.

Agent Memory
Summary
Two tools, but the metadata on them is where the value is.

search and add are the whole surface; what makes it usable at scale is that a memory carries tags, the people it involves and a topic, and search can filter on all three plus a time window. Because it is a hosted endpoint with OAuth, the same memory follows you between Claude Desktop, VS Code and anything else that speaks MCP.

What it is

A hosted memory service reached over MCP. Memories are stored with metadata — tags, people mentioned, a topic category — and searched semantically with those fields as filters. The same store is reachable from the Python and TypeScript SDKs and from the web app.

What you get
  • search — semantic search over your memories, narrowed by tags, people_mentioned, topic_category or a temporal_filter such as today or this_week
  • Threshold and retrieval_limit on search, so you can trade recall against precision
  • add — store a memory with tags, people mentioned, a topic category and any extra metadata you want to keep
  • Project isolation: the OAuth token scopes every call to the project you selected, so work and personal memories stay apart
Requirements

A SelfMemory account. The server is remote at https://mcp.selfmemory.com/mcp over Streamable HTTP, and authorization runs through OAuth 2.1 — your client discovers the authorization server from the protected-resource metadata and walks you through login in the browser. The scopes are memories:read and memories:write. Nothing to install locally.

Setup effort

One command — pip install selfmemory