harperreed 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
| Tool | Purpose |
|---|---|
mcp__memory__store |
Store a memory |
mcp__memory__search |
Semantic similarity search |
mcp__memory__list |
List recent memories |
mcp__memory__delete |
Remove a memory |
mcp__memory__get_context |
Get relevant context |
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
- Store preferences - When user expresses a preference, store it
- Store decisions - Important decisions and their rationale
- Store context - Project-specific context that spans sessions
- 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)