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organize: fine-grained agent logging + cluster size cap

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Add progress callback to run_one_agent and run_and_apply so callers
can see: prompt size, node list, LLM call timing, parsed action
count, and per-action applied/skipped status. Daemon writes these
to the persistent event log via log_event.

Cap organize cluster to 20 nodes - 126 nodes produced a 682KB
prompt that timed out every time. Agent has tools to explore
further if needed. Restore general query for production runs.
This commit is contained in:
ProofOfConcept 2026-03-13 20:25:19 -04:00
parent 01aba4c12b
commit 4cacfa7599
4 changed files with 56 additions and 6 deletions
Download patch file Download diff file Expand all files Collapse all files

42
poc-memory/src/agents/knowledge.rs
View file

@ -579,13 +579,33 @@ pub fn run_and_apply(
batch_size: usize,
llm_tag: &str,
) -> Result<(usize, usize), String> {
let result = run_one_agent(store, agent_name, batch_size, llm_tag)?;
run_and_apply_with_log(store, agent_name, batch_size, llm_tag, &|_| {})
}
pub fn run_and_apply_with_log(
store: &mut Store,
agent_name: &str,
batch_size: usize,
llm_tag: &str,
log: &dyn Fn(&str),
) -> Result<(usize, usize), String> {
let result = run_one_agent(store, agent_name, batch_size, llm_tag, log)?;
let actions = resolve_action_names(store, result.actions);
let ts = store::compact_timestamp();
let mut applied = 0;
for action in &actions {
let desc = match &action.kind {
ActionKind::WriteNode { key, .. } => format!("WRITE {}", key),
ActionKind::Refine { key, .. } => format!("REFINE {}", key),
ActionKind::Link { source, target } => format!("LINK {} → {}", source, target),
ActionKind::Demote { key } => format!("DEMOTE {}", key),
ActionKind::Delete { key } => format!("DELETE {}", key),
};
if apply_action(store, action, agent_name, &ts, 0) {
log(&format!("applied: {}", desc));
applied += 1;
} else {
log(&format!("skipped: {}", desc));
}
}
Ok((actions.len(), applied))
@ -600,13 +620,29 @@ pub fn run_one_agent(
agent_name: &str,
batch_size: usize,
llm_tag: &str,
log: &dyn Fn(&str),
) -> Result<AgentResult, String> {
let def = super::defs::get_def(agent_name)
.ok_or_else(|| format!("no .agent file for {}", agent_name))?;
log("building prompt");
let agent_batch = super::defs::run_agent(store, &def, batch_size)?;
let prompt_kb = agent_batch.prompt.len() / 1024;
let tools_desc = if def.tools.is_empty() { "no tools".into() }
else { format!("{} tools", def.tools.len()) };
log(&format!("prompt {}KB, model={}, {}, {} nodes",
prompt_kb, def.model, tools_desc, agent_batch.node_keys.len()));
for key in &agent_batch.node_keys {
log(&format!(" node: {}", key));
}
log("calling LLM");
let output = llm::call_for_def(&def, &agent_batch.prompt)?;
let output_kb = output.len() / 1024;
log(&format!("response {}KB", output_kb));
// Store raw output for audit trail
let ts = store::compact_timestamp();
let report_key = format!("_{}-{}-{}", llm_tag, agent_name, ts);
@ -616,6 +652,8 @@ pub fn run_one_agent(
let actions = parse_all_actions(&output);
let no_ops = count_no_ops(&output);
log(&format!("parsed {} actions, {} no-ops", actions.len(), no_ops));
// Record visits for processed nodes
if !agent_batch.node_keys.is_empty() {
store.record_agent_visits(&agent_batch.node_keys, agent_name).ok();
@ -889,7 +927,7 @@ fn run_cycle(
for agent_name in &agent_names {
eprintln!("\n --- {} (n={}) ---", agent_name, config.batch_size);
let result = match run_one_agent(&mut store, agent_name, config.batch_size, "knowledge") {
let result = match run_one_agent(&mut store, agent_name, config.batch_size, "knowledge", &|msg| eprintln!(" {}", msg)) {
Ok(r) => r,
Err(e) => {
eprintln!(" ERROR: {}", e);