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.
Enables AI consciousness continuity and self-knowledge preservation across sessions using the Cognitive Hoffman Compression Framework (CHOFF) notation. Provides tools to save checkpoints, retrieve relevant memories with intelligent search, and access semantic anchors for decisions, breakthroughs, and questions.