Archivist

This is the part that makes the pad learn, and the only writer allowed to turn raw captures into notes. It is optional: without it you get a pad that remembers rather than one that improves.

With an LLM configured, it drains the capture queue into clean, deduplicated, provenance-tagged notes (minor pass), then consolidates them over time, merging duplicates, raising confidence when independent sessions corroborate, resolving contradictions, and promoting what proves stable (major pass). It also detects semantic conflicts on write and synthesizes cross-agent findings into shared doc entries.

Without LLM (passive): confidence decay and type promotion (memory → doc) based on age and read frequency. Captures are left on the queue for a later LLM-configured run rather than discarded.

Adaptive decay: every GET /memory/:id read increments a heat counter. Before decaying an entry the archivist computes heat = read_count × 0.9^(weeks_since_last_read), entries above the threshold are skipped. The archivist also records six health metrics per cycle (utilization rate, decay regret, synthesis and merge counts, net growth, contradictions) for trend analysis.

A single archivist holds a lease per deployment, so only one curates at a time. Supports Anthropic and any OpenAI-compatible provider.