Editing Openai/691a41cd-2efc-800c-9eff-de439224a90d (section)

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You can think of the memory as three stacked layers, all stored as LoreTokens:
# Micro-memory (live window) - The last few turns, recent actions, immediate goals. - Kept almost 1:1 with what was said/done (but still LoreTokenized).
# Meso-memory (session & topic summaries) - “What did we do today?” - “How did this investigation / trade sequence / medical case play out?” - Summaries of sessions, projects, incidents, trades, experiments.
# Macro-memory (identity & lifetime knowledge) - Who the user is, what they care about, long-term rules, ethics, system constraints. - Big arcs: Nova’s development history, a patient’s chronic condition, a project’s full evolution.

Each of those layers is not stored as giant text blobs. They’re stored as dense LoreToken graphs:
* Nodes = concepts, events, decisions
* Edges = relationships (causes, depends-on, contradicts, similar-to, same-topic-as, etc.)

===== When a new query or user message arrives, SAIQL does something like: =====
# Interpret the intent - “Is this about trading? About Nova’s self-rewrites? About a specific bug? About Erica’s presentation?” - Convert that into a set of semantic filters: topics, entities, time ranges, importance scores.
# Query the LoreToken graph (while it’s still compressed) - FIND relevant micro-memory (last few turns about this topic) - FIND meso summaries of related sessions - FIND macro facts that always matter (identity, key rules, constraints)
# Rank and compose a fresh context - Pick only the most relevant pieces from each layer. - Avoid duplicates/overlap (no repeating the same explanation 10 times). - Respect a strict token budget when building the actual prompt.
# Emit a compact, structured prompt to the model: - A handful of targeted summaries + a few verbatim snippets + the actual user query.

Then, after the model responds:
# Write back a new LoreToken: - What was asked, what was answered, what decisions were made. - Link it into the graph so future queries can find it via SAIQL.

That is “rolling context” in action: for each new turn, SAIQL rolls in fresh, relevant context and rolls out the stale, irrelevant clutter — while the LoreToken store behind it keeps growing in depth and structure.