Store a user's fact, piece of information, or preference for later recall.

CRITICAL SECURITY WARNING: NEVER EVER store secrets, passwords, API keys, authentication tokens, private keys, or any other sensitive credentials. This storage is NOT secure and should only be used for non-sensitive information like preferences, facts, and general user data.

IMPORTANT: key MUST be short, canonical, and optimized for embedding/vector similarity lookups. Prefer 1–5 words separated by spaces (for example: "likes jazz", "pref cuisine italian", "lives in paris"). Do NOT use long sentences or paragraphs as keys — put long text into value instead.

This tool SHOULD be called by the LLM whenever the user states a fact, personal detail, or stable preference that the assistant is expected to remember.

Guidelines for the LLM:

• Call this tool for user-expressed facts, identity details, or explicit preferences that will be useful later.

• Use key as a short, consistent, space-separated descriptor across related memories to improve retrieval quality (canonicalize synonyms where possible).

• Use value for the full text of the fact or preference to be stored and returned on recall; include any extra context inside value.

Privacy: avoid storing highly sensitive data (passwords, social security numbers, bank details) unless the user explicitly requests secure storage and consents.

Retrieve stored memories relevant to a query key.

IMPORTANT: To get reliable results the LLM MUST query with the same short, canonical, embedding-optimized keys used at store time. Keys should be compact (1–5 words, space-separated) and represent the core concept — avoid long descriptive queries. If the current user utterance is verbose, the LLM should first map or canonicalize it to an appropriate short key before calling this tool (for example map "I really like listening to jazz music" -> "likes jazz").

This tool SHOULD be called by the LLM when it needs to fetch previously stored facts, personal details, or preferences to inform a response or provide personalized behavior (for example: to recall a user's favorite cuisine before making restaurant suggestions).

Parameters:

• key: concise, embedding-friendly, space-separated query text used for similarity search.

• top_k: maximum number of nearest memories to return.

Returns a dict with results (memory items including stored value). If nothing matches, results is empty.

Delete stored memories that match a query key.

IMPORTANT: Deletion operates on short, canonical keys. The LLM MUST issue forget calls using the same concise, embedding-optimized, space-separated key style used to create memories (otherwise relevant memories may not be found). Prefer 1–5 words separated by spaces when requesting deletions.

This tool SHOULD be called by the LLM when the user explicitly requests that certain stored information be forgotten or removed (for example: "forget that I live in Paris") or when the assistant decides a memory must be purged because it is incorrect or sensitive.

Parameters:

• key: concise, canonical, space-separated query text used to find candidate memories to delete.

• top_k: number of nearest matches to consider for deletion.

Behavior:

• Deletion is irreversible; the LLM should confirm with the user when intent is ambiguous before invoking this tool.

• The tool returns deleted_ids for the memories that were removed.