What is an MCP memory server?

An MCP memory server is a Model Context Protocol server that gives AI assistants persistent memory. It stores knowledge from your conversations and work, then makes that knowledge searchable and retrievable in later sessions, by the same AI tool or a different one. Because MCP is an open standard, one memory server can serve Claude, ChatGPT, Cursor, Codex, and any other MCP client at the same time.

##Why do AI assistants need a memory server?

Because large language models are stateless: every conversation starts from zero, and anything outside the current context window is gone. Built-in memory features (ChatGPT's saved memories and chat-history reference, Claude's memory summary and chat search) soften this inside one vendor's product, but they are small by design (capacity-limited digests, derived only from that vendor's conversations), and none of it transfers between tools. A memory server fixes this at the protocol level: the AI calls tools like search, read, and write against a knowledge store that persists across sessions and vendors.

##How does an MCP memory server work?

An MCP memory server exposes memory operations as MCP tools. When you connect one to an AI assistant, the assistant can write notes during a conversation, search past knowledge before answering, and follow links between related topics. The server runs locally on your machine or remotely in the cloud; the AI tool connects to it the same way either way. In Basic Memory's case, the store is a set of plain Markdown files indexed into a semantic knowledge graph (notes, observations, and relations) so retrieval can follow meaning, not just keywords (see how it works).

##What types of MCP memory servers exist?

Three broad types. File-based knowledge bases (Basic Memory) store memory as readable files you own: transparent, portable, editable by humans and AI alike. API-backed servers (e.g. wrappers around services like Mem0) store extracted facts in a managed backend: convenient for app developers, opaque to end users. Vector-store experiments (the weekend projects that hit GitHub trending) stash conversation snippets in an embedding database, usually without structure, sync, or maintenance. The practical question that separates them: can you read what your AI knows about you?

##How do I choose one?

Ask four questions. Can you read and edit what's stored? Does it work with all your AI tools, or one? Can you take your data with you? And is retrieval quality measured in the open? Basic Memory's answers: yes (plain Markdown), all MCP clients, yes (copy the folder), and yes, a public, reproducible benchmark suite on the LoCoMo academic dataset. For detailed head-to-heads, see the comparisons.