cmd_history was silently hiding the deleted flag, making it
impossible to tell from the output that a node had been deleted.
This masked the kernel-patterns deletion — looked like the node
existed in the log but wouldn't load.
Also adds merge-logs and diag-key diagnostic binaries, and makes
Node::to_capnp public for use by external tools.
Co-Authored-By: Kent Overstreet <kent.overstreet@linux.dev>
calibrate.agent: Haiku-based agent that reads a node and all its
neighbors, then assigns appropriate link strengths relative to each
other. Designed for high-volume runs across the whole graph.
graph link-set: Set strength of an existing link (0.0-1.0).
dominating-set query stage: Greedy 3-covering dominating set — finds
the minimum set of nodes such that every node in the input is within
1 hop of at least 3 selected nodes. Use with calibrate agent to
ensure every link gets assessed from multiple perspectives.
Usage: poc-memory query "content ~ 'bcachefs' | dominating-set"
--target and --query now queue individual daemon tasks instead of
running sequentially in the CLI. Each node gets its own choir task
with LLM resource locking. Falls back to local execution if daemon
isn't running.
RPC extended: "run-agent linker 1 target:KEY" spawns a targeted task.
Run an agent on nodes matching a query:
poc-memory agent run linker --query 'key ~ "bcachefs" | limit 10'
Resolves the query to node keys, then passes all as seeds to the agent.
For large batches, should be queued to daemon (future work).
experience_mine and journal_enrich are replaced by the observation
agent. enrich.rs reduced from 465 to 40 lines — only extract_conversation
and split_on_compaction remain (used by observation fragment selection).
-455 lines.
Remove unused StoreView imports, unused store imports, dead
install_default_file, dead make_report_slug, dead fact-mine/
experience-mine spawning loops in daemon. Fix mut warnings.
Zero compiler warnings now.
Adds run_one_agent_with_keys() which bypasses the agent's query and
uses explicitly provided node keys. This allows testing agents on
specific graph neighborhoods:
poc-memory agent run linker --target bcachefs --debug
Consolidate agent logging to one file per run in llm-logs/{agent}/.
Prompt written before LLM call, response appended after. --debug
additionally prints the same content to stdout.
Remove duplicate eprintln! calls and AgentResult.prompt field.
Kill experience_mine and fact_mine job functions from daemon —
observation.agent handles all transcript mining.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Add --debug flag that prints the full prompt and LLM response to
stdout, making it easy to iterate on agent prompts. Also adds
prompt field to AgentResult so callers can inspect what was sent.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
New command: `poc-memory agent run <agent> [--count N] [--dry-run]`
Runs a single agent by name through the full pipeline (build prompt,
call LLM, apply actions). With --dry-run, sets POC_MEMORY_DRY_RUN=1
so all mutations are no-ops but the agent can still read the graph.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
All mutating commands (write, delete, rename, link-add, journal write,
used, wrong, not-useful, gap) check POC_MEMORY_DRY_RUN after argument
validation but before mutation. If set, process exits silently — agent
tool calls are visible in the LLM output so we can see what it tried
to do without applying changes.
Read commands (render, search, graph link, journal tail) work normally
in dry-run mode so agents can still explore the graph.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The LCG was producing only 2 distinct matchup pairs due to poor
constants. Switch to xorshift32 for proper coverage of all type pairs.
Co-Authored-By: Kent Overstreet <kent.overstreet@linux.dev>
Replace sort-based ranking with proper Elo system:
- Each agent TYPE has a persistent Elo rating (agent-elo.json)
- Each matchup: pick two random types, grab a recent action from
each, LLM compares, update ratings
- Ratings persist across daily evaluations — natural recency bias
from continuous updates against current opponents
- K=32 for fast adaptation to prompt changes
Usage: poc-memory agent evaluate --matchups 30 --model haiku
Co-Authored-By: Kent Overstreet <kent.overstreet@linux.dev>
TIE causes inconsistency in sort (A=B, B=C but A>C breaks ordering).
Force the comparator to always pick a winner. Default to A if response
is unparseable.
Co-Authored-By: Kent Overstreet <kent.overstreet@linux.dev>
- Use CARGO_MANIFEST_DIR for agent file path (same as defs.rs)
- Dedup affected nodes extracted from reports
- --dry-run shows example comparison prompt without LLM calls
Co-Authored-By: Kent Overstreet <kent.overstreet@linux.dev>
Chain-of-thought: "say which is better and why" forces clearer
judgment and gives us analysis data for improving agents.
Co-Authored-By: Kent Overstreet <kent.overstreet@linux.dev>
When both actions are from the same agent, show the instructions once
and just compare the two report outputs + affected nodes. Saves tokens
and makes the comparison cleaner.
Co-Authored-By: Kent Overstreet <kent.overstreet@linux.dev>
Each comparison now shows the LLM:
- Agent instructions (the .agent prompt file)
- Report output (what the agent did)
- Affected nodes content (what it changed)
The comparator sees intent, action, and impact — can judge whether
a deletion was correct, whether links are meaningful, whether
WRITE_NODEs capture real insights.
Co-Authored-By: Kent Overstreet <kent.overstreet@linux.dev>
Yes, really. Rust's stdlib sort_by with an LLM pairwise comparator.
Each comparison is an API call asking "which action was better?"
Sample N actions per agent type, throw them all in a Vec, sort.
Where each agent's samples cluster = that agent's quality score.
Reports per-type average rank and quality ratio.
Supports both haiku (fast/cheap) and sonnet (quality) as comparator.
Usage: poc-memory agent evaluate --samples 5 --model haiku
Co-Authored-By: Kent Overstreet <kent.overstreet@linux.dev>
