Kin Memory

KinBot gives every Kin persistent long-term memory — a dual-channel system that combines automatic extraction with explicit storage, searchable via hybrid vector + full-text search.

How it works

Automatic extraction

After every LLM turn, KinBot runs an extraction pipeline that identifies important information from the conversation and saves it as memories. This happens silently in the background — the Kin doesn’t need to do anything.

Each extracted memory includes a source context — a brief description of the conversational context in which the fact was mentioned (e.g. “While discussing weekend plans, user mentioned…”). This gives memories episodic flavor, helping the Kin understand not just what was said but when and why.

Explicit memorization

Kins can also deliberately save information using the memorize tool:

memorize("Nicolas prefers dark mode and French responses", category: "preference", subject: "Nicolas")

Memory categories

Each memory has a category:

Category	Use case
fact	Objective information (names, dates, technical details)
preference	User preferences and habits
decision	Decisions that were made and their rationale
knowledge	Learned domain knowledge

Importance scoring

Memories have an importance score from 1-10. Higher-importance memories are prioritized during retrieval. The automatic pipeline and the Kin can both set importance.

Retrieval

Before each LLM turn, KinBot:

1. Takes the current user message
2. Optionally rewrites the query using recent conversation context for better semantic matching
3. Searches memories using hybrid search: vector similarity (embeddings) + full-text keyword matching (FTS5)
4. Injects the most relevant memories into the system prompt

This means the Kin always has relevant context without needing to explicitly recall information.

Manual recall

Kins can also search memory explicitly:

• recall("Nicolas's infrastructure setup") — semantic + keyword search
• list_memories(category: "decision") — browse by category
• search_history("kubernetes deployment") — search past conversation messages

Memory tools

Tool	Purpose
recall	Search memories (semantic + keyword, includes shared)
memorize	Save new information (private or shared)
update_memory	Update an existing memory (content, category, scope)
forget	Delete a memory
list_memories	Browse memories by category or scope
review_memories	LLM-powered audit for contradictions, duplicates, stale entries
search_history	Search conversation message history

Shared memories

Memories default to private (only the owning Kin can see them), but Kins can mark memories as shared to make them searchable by all other Kins. This is useful for cross-domain facts like infrastructure details, user-wide preferences, or organizational decisions.

• Use memorize(..., scope: "shared") or update_memory(..., scope: "shared")
• recall automatically searches both private and shared memories
• Shared memories include author attribution (e.g. [shared by Assistant])

Session compacting

When a conversation grows beyond the model’s context window, KinBot compacts older messages into a summary. Key points:

• Original messages are never deleted — they’re preserved in the database
• The compacting summary is injected at the start of the context window
• The Kin is informed about compacting: it knows how many messages are visible vs. total, and whether older history was compacted

Memory and privacy

• Memories are per-Kin by default — each Kin has its own memory store
• Shared memories are readable by all Kins but still owned by the creator
• Vault secrets are never stored in memories (redaction prevents leaking into compacted summaries)