Search through agent memory to retrieve relevant information using semantic similarity matching. Specify queries to find stored data with configurable relevance thresholds and result limits.
Store project information in a memory slot for future tasks by specifying a meaningful memory name and content in Markdown format, using Serena's MCP server.
Retrieve statistical information about short-term memories to monitor memory usage patterns and analyze conversation data for memory management optimization.
Provides an intelligent, graph-based memory system for LLM agents using the Zettelkasten principle, enabling automatic note construction, semantic linking, memory evolution, and autonomous graph maintenance with background optimization processes.