Four layer-2 agents that produce new knowledge from the memory graph:
mine conversations, extract patterns from clusters, find cross-domain
connections, stress-test existing nodes. Output to agent-results/.
knowledge_loop.py runs them on a schedule with quality tracking.
- New spectral module: Laplacian eigendecomposition of the memory graph.
Commands: spectral, spectral-save, spectral-neighbors, spectral-positions,
spectral-suggest. Spectral neighbors expand search results beyond keyword
matching to structural proximity.
- Search: use StoreView trait to avoid 6MB state.bin rewrite on every query.
Append-only retrieval logging. Spectral expansion shows structurally
nearby nodes after text results.
- Fix panic in journal-tail: string truncation at byte 67 could land inside
a multi-byte character (em dash). Now walks back to char boundary.
- Replay queue: show classification and spectral outlier score.
- Knowledge agents: extractor, challenger, connector prompts and runner
scripts for automated graph enrichment.
- memory-search hook: stale state file cleanup (24h expiry).
Add store_helpers.py with shared helpers that call poc-memory commands
(list-keys, render, journal-tail) instead of globbing ~/.claude/memory/*.md
and parsing section headers.
All 9 Python scripts updated: get_semantic_keys(), get_topic_file_index(),
get_recent_journal(), parse_journal_entries(), read_journal_range(),
collect_topic_stems(), and file preview rendering now go through the store.
This completes the clean switch — no script reads archived markdown files.
Faster serialization/deserialization, smaller on disk (4.2MB vs 5.9MB).
Automatic migration from state.json on first load — reads the JSON,
writes state.bin, deletes the old file.
Added list-keys, list-edges, dump-json commands so Python scripts no
longer need to parse the cache directly. Updated bulk-categorize.py
and consolidation-loop.py to use the new CLI commands.
