The consolidation pipeline previously made a second Sonnet call to
extract structured JSON actions from agent reports. This was both
wasteful (extra LLM call per consolidation) and lossy (only extracted
links and manual items, ignoring WRITE_NODE/REFINE).
Now actions are parsed and applied inline after each agent runs, using
the same parse_all_actions() parser as the knowledge loop. The daemon
scheduler's separate apply phase is also removed.
Also deletes 8 superseded/orphaned prompt .md files (784 lines) that
have been replaced by .agent files.
These prompts are now embedded in their .agent files or no longer
called from any code path.
Co-Authored-By: Kent Overstreet <kent.overstreet@linux.dev>
- Refactor split from serial batch to independent per-node tasks
(run-agent split N spawns N parallel tasks, gated by llm_concurrency)
- Replace cosine similarity edge inheritance with agent-assigned
neighbors in the plan JSON — the LLM already understands the
semantic relationships, no need to approximate with bag-of-words
- Add --strict-mcp-config to claude CLI calls to skip MCP server
startup (saves ~5s per call)
- Remove hardcoded 2000-char split threshold — let the agent decide
what's worth splitting
- Reload store before mutations to handle concurrent split races
Phase 1 sends a large node with its neighbor communities to the LLM
and gets back a JSON split plan (child keys, descriptions, section
hints). Phase 2 fires one extraction call per child in parallel —
each gets the full parent content and extracts/reorganizes just its
portion.
This handles arbitrarily large nodes because output is always
proportional to one child, not the whole parent. Tested on the kent
node (19K chars → 3 children totaling 20K chars with clean topic
separation).
New files:
prompts/split-plan.md — phase 1 planning prompt
prompts/split-extract.md — phase 2 extraction prompt
prompts/split.md — original single-phase (kept for reference)
Modified:
agents/prompts.rs — split_candidates(), split_plan_prompt(),
split_extract_prompt(), agent_prompt "split" arm
agents/daemon.rs — job_split_agent() two-phase implementation,
RPC dispatch for "split" agent type
tui.rs — added "split" to AGENT_TYPES
New consolidation agent that reads node content and generates semantic
3-5 word kebab-case keys, replacing auto-generated slugs (5K+ journal
entries with truncated first-line slugs, 2.5K mined transcripts with
opaque UUIDs).
Implementation:
- prompts/rename.md: agent prompt template with naming conventions
- prompts.rs: format_rename_candidates() selects nodes with long
auto-generated keys, newest first
- daemon.rs: job_rename_agent() parses RENAME actions from LLM
output and applies them directly via store.rename_node()
- Wired into RPC handler (run-agent rename) and TUI agent types
- Fix epoch_to_local panic on invalid timestamps (fallback to UTC)
Rename dramatically improves search: key-component matching on
"journal#2026-02-28-violin-dream-room" makes the node findable by
"violin", "dream", or "room" — the auto-slug was unsearchable.
The LLM knows how to structure a summary. Move the essential framing
(narrative not task log, link to memory, include Links section) into
the shared prompt template. Drop the ~130 lines of per-level output
format specifications — the level name, date range, and inputs are
sufficient context.
Three near-identical generate_daily/weekly/monthly functions collapsed
into one generate_digest() parameterized by DigestLevel descriptors.
Three separate prompt templates merged into one prompts/digest.md with
level-specific instructions carried in the DigestLevel struct.
Each level defines: name, title, period label, input title, output
format instructions, child prefix (None for daily = reads journal),
and Sonnet timeout.
digest_auto simplified correspondingly — same three phases but using
the unified generator.
Co-Authored-By: Kent Overstreet <kent.overstreet@linux.dev>
- 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).
Agents were flying blind — they could see nodes to review and the
topology header, but had no way to discover what targets to link to.
Now each node shows its top 8 text-similar semantic nodes that aren't
already neighbors, giving agents a search-like capability.
Also added section-level targeting guidance to linker.md, transfer.md,
and replay.md prompts: always target the most specific section, not
the file-level node.
Co-Authored-By: ProofOfConcept <poc@bcachefs.org>
