memory - Semantic Memory

Vector-based memory storage with embeddings for semantic similarity search.

When to use memory

• •Store important context for future sessions
• •Recall relevant information from past conversations
• •Build persistent knowledge about user preferences
• •Semantic search across stored memories

Available MCP tools

Common patterns

Store a memory

mcp__memory__store(
  content="User prefers TypeScript over JavaScript for new projects",
  tags=["preferences", "programming"]
)

Search memories

mcp__memory__search(query="What does the user prefer for web development?", limit=5)

Get context for a topic

mcp__memory__get_context(topic="user's coding preferences")

List recent memories

mcp__memory__list(limit=10)

Best practices

1. •Store preferences - When user expresses a preference, store it
2. •Store decisions - Important decisions and their rationale
3. •Store context - Project-specific context that spans sessions
4. •Search before assuming - Check memory before making assumptions

CLI commands (if MCP unavailable)

memory add "Content to remember" --tag preference
memory search "query"             # Semantic search
memory list                       # Recent memories
memory export -f markdown         # Export

Data location

~/.local/share/memory/memory.db (SQLite with embeddings as BLOB, respects XDG_DATA_HOME)