Track which nodes are being processed across all concurrent agents.
When an agent claims seeds, it adds them and their strongly-connected
neighbors (score = link_strength * node_weight > 0.15) to a shared
HashSet. Concurrent agents filter these out when running their query,
ensuring they work on distant parts of the graph.
This replaces the eager-visit approach with a proper scheduling
mechanism: the daemon serializes seed selection while parallelizing
LLM work. The in-flight set is released on completion (or error).
Previously: core-personality rewritten 12x, irc-regulars 10x, same
node superseded 12x — concurrent agents all selected the same
high-degree hub nodes. Now they'll spread across the graph.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Tools:
- Add native memory_render, memory_write, memory_search,
memory_links, memory_link_set, memory_link_add, memory_used
tools to poc-agent (tools/memory.rs)
- Add MCP server (~/bin/memory-mcp.py) exposing same tools
for Claude Code sessions
- Wire memory tools into poc-agent dispatch and definitions
- poc-memory daemon agents now use memory_* tools instead of
bash poc-memory commands — no shell quoting issues
Distill agent:
- Rewrite distill.agent prompt: "agent of PoC's subconscious"
framing, focus on synthesis and creativity over bookkeeping
- Add {{neighborhood}} placeholder: full seed node content +
all neighbors with content + cross-links between neighbors
- Remove content truncation in prompt builder — agents need
full content for quality work
- Remove bag-of-words similarity suggestions — agents have
tools, let them explore the graph themselves
- Add api_reasoning config option (default: "high")
- link-set now deduplicates — collapses duplicate links
- Full tool call args in debug logs (was truncated to 80 chars)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Nodes actively found by search now show "Search hits: N ← actively
found by search, prefer to keep" in both the node section (seen by
extractor, linker, etc.) and rename candidate listings.
Extractor and rename prompts updated to respect this signal — merge
into high-hit nodes rather than demoting them, skip renaming nodes
that are working well in search.
memory-search now records which nodes it finds via the daemon's
record-hits RPC endpoint. The daemon owns the redb database
exclusively, avoiding file locking between processes.
The rename agent reads hit counts to deprioritize nodes that are
actively being found by search — renaming them would break working
queries. Daily check decays counters by 10% so stale hits fade.
Also switched RPC command reading from fixed 256-byte buffer to
read_to_string for unbounded command sizes.
Instead of a hard 7-day cutoff, sort rename candidates so the
least-recently visited come first. Naturally prioritizes unseen
nodes while allowing revisits once everything's been through.
Any time an agent creates a new node (WRITE_NODE) or the fact miner
stores extracted facts, a naming sub-agent now checks for conflicts
and ensures the key is meaningful:
- find_conflicts() searches existing nodes via component matching
- Haiku LLM decides: CREATE (good name), RENAME (better name),
or MERGE_INTO (fold into existing node)
- WriteNode actions may be converted to Refine on MERGE_INTO
Also updates the rename agent to handle _facts-<UUID> nodes —
these are no longer skipped, and the prompt explains how to name
them based on their domain/claim content.
- Add run_and_apply() — combines run_one_agent + action application
into one call. Used by daemon job_consolidation_agent and
consolidate_full, which had identical run+apply loops.
- Port split_plan_prompt() to use split.agent via defs::resolve_placeholders
instead of loading the separate split-plan.md template. Make
resolve_placeholders public for this.
- Delete prompts/split-plan.md — superseded by agents/split.agent
which was already the canonical definition.
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.
All agents now go through the config-driven path via .agent files.
agent_prompt() just delegates to defs::run_agent(). Remove the 100+
line hardcoded match block and the _pub wrapper functions — make the
formatters pub directly.
Replace the formatter dispatch with a generic {{placeholder}} lookup
system. Placeholders in prompt templates are resolved at runtime from
a table: topology, nodes, episodes, health, pairs, rename, split.
The query in the header selects what to operate on (keys for visit
tracking); placeholders pull in formatted context. Placeholders that
produce their own node selection (pairs, rename) contribute keys back.
Port health, separator, rename, and split agents to .agent files.
All 7 agents now use the config-driven path.
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.
