Store a single piece of information in the agent's long-term memory. Use this whenever the user shares a stable fact, preference, decision, goal, or instruction you should recall in a future conversation. Memory is typed (13 categories) and carries confidence + provenance so later retrievals can rank and filter intelligently. Content is capped at 10000 chars - store atomic, self-contained statements.

Store many memories at once (up to 100). Use this when you have a list of independent facts to persist - e.g. extracting structured data from a document. For a single item, prefer remember.

Search the agent's memories by semantic similarity. Returns the top-N most relevant items. Use this FIRST before asking the user to repeat information - the agent may already remember it. The query should be natural language ('what does the user prefer for code style?'), not keywords.

Return the most recently stored memories (newest first). Use this to surface fresh context - e.g. 'what did we just decide?' - when you don't have a specific search query.

Point-in-time recall: return only memories that were known before the given timestamp. Use this when the user asks historical questions like 'what did we know on 2025-11-01?' or to reconstruct context at a previous moment.

Differential retrieval: return memories created or updated after the given timestamp. Use this for 'what's new since X?' or to catch up on activity between sessions.

Ask a natural-language question and get an LLM-generated answer grounded ONLY in the agent's stored memories (RAG). Prefer this over recall when you need a synthesized answer rather than a ranked list. Returns the answer text plus the supporting memory sources.