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Persist conversational memory with SQLite and retrieve context using metadata.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "Memory Server" yet — see the docs or source repo.
Save the user's preferences from this conversation to Memory Server: the user prefers concise replies, prefers Chinese output, and is building a customer support bot. Add tags to each memory: preference, language, project.
The tool persists structured memories and returns a save result or record identifiers.
Retrieve memories from Memory Server related to the tags "project" and "language" to restore the user's communication preferences and project background.
The tool returns matching memory records so the conversation can resume with context.
Design a long-term memory strategy for my AI assistant: write task progress, key decisions, and user feedback into Memory Server, and explain how to retrieve them later by metadata.
Produces an actionable memory storage and retrieval plan with metadata suited for long-term tracking.
Store and query namespaced key-value memory for persistent agent context.
Persistent knowledge-graph memory for MCP with semantic search and version tracking.
Give AI clients persistent long-term memory with search and organization.
Lightweight vector memory for AI agents to store, search, and delete memories.
Give AI agents persistent memory and semantic retrieval across conversations.
Give AI assistants persistent memory, entity storage, and semantic search across sessions.