Each agent is a .agent file: JSON config on the first line, blank line,
then the raw prompt markdown. Fully self-contained, fully readable.
No separate template files needed.
Agents dir: checked into repo at poc-memory/agents/. Code looks there
first (via CARGO_MANIFEST_DIR), falls back to ~/.claude/memory/agents/.
Three agents migrated: replay, linker, transfer.
Co-Authored-By: ProofOfConcept <poc@bcachefs.org>
New append-only visits.capnp log records which agent processed which
node and when. Only recorded on successful completion — transient
errors don't mark nodes as "seen."
Schema: AgentVisit{nodeUuid, nodeKey, agent, timestamp, outcome}
Storage: append_visits(), replay_visits(), in-memory VisitIndex
Recording: daemon records visits after successful LLM call
API: agent_prompt() returns AgentBatch{prompt, node_keys} so callers
know which nodes to mark as visited.
Groundwork for using visit recency in agent node selection — agents
will deprioritize recently-visited nodes.
- 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
Episodic nodes (journal entries, digests) are narratives that should
not be split even when large. Only semantic reference nodes that have
grown to cover multiple topics are candidates.
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.
