consolidate: eliminate second LLM call, apply actions inline
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 Also deletes 8 superseded/orphaned prompt .md files (784 lines) that have been replaced by .agent files.
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ProofOfConcept
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commit
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11 changed files with 119 additions and 1024 deletions
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@ -2,18 +2,21 @@
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//
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// consolidate_full() runs the full autonomous consolidation:
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// 1. Plan: analyze metrics, allocate agents
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// 2. Execute: run each agent (Sonnet calls), save reports
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// 3. Apply: extract and apply actions from reports
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// 2. Execute: run each agent, parse + apply actions inline
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// 3. Graph maintenance (orphans, degree cap)
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// 4. Digest: generate missing daily/weekly/monthly digests
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// 5. Links: apply links extracted from digests
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// 6. Summary: final metrics comparison
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//
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// apply_consolidation() processes consolidation reports independently.
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// Actions are parsed directly from agent output using the same parser
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// as the knowledge loop (WRITE_NODE, LINK, REFINE), eliminating the
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// second LLM call that was previously needed.
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use super::digest;
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use super::llm::{call_sonnet, parse_json_response};
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use super::llm::call_sonnet;
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use super::knowledge;
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use crate::neuro;
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use crate::store::{self, Store, new_relation};
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use crate::store::{self, Store};
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/// Append a line to the log buffer.
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@ -57,9 +60,10 @@ pub fn consolidate_full_with_progress(
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// --- Step 2: Execute agents ---
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log_line(&mut log_buf, "\n--- Step 2: Execute agents ---");
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let mut reports: Vec<String> = Vec::new();
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let mut agent_num = 0usize;
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let mut agent_errors = 0usize;
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let mut total_applied = 0usize;
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let mut total_actions = 0usize;
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// Build the list of (agent_type, batch_size) runs
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let mut runs: Vec<(&str, usize)> = Vec::new();
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@ -123,13 +127,24 @@ pub fn consolidate_full_with_progress(
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}
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};
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// Store report as a node
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// Store report as a node (for audit trail)
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let ts = store::format_datetime(store::now_epoch())
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.replace([':', '-', 'T'], "");
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let report_key = format!("_consolidation-{}-{}", agent_type, ts);
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store.upsert_provenance(&report_key, &response,
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store::Provenance::AgentConsolidate).ok();
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reports.push(report_key.clone());
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// Parse and apply actions inline — same parser as knowledge loop
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let actions = knowledge::parse_all_actions(&response);
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let no_ops = knowledge::count_no_ops(&response);
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let mut applied = 0;
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for action in &actions {
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if knowledge::apply_action(store, action, agent_type, &ts, 0) {
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applied += 1;
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}
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}
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total_actions += actions.len();
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total_applied += applied;
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// Record visits for successfully processed nodes
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if !agent_batch.node_keys.is_empty() {
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@ -138,36 +153,19 @@ pub fn consolidate_full_with_progress(
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}
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}
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let msg = format!(" Done: {} lines → {}", response.lines().count(), report_key);
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let msg = format!(" Done: {} actions ({} applied, {} no-ops) → {}",
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actions.len(), applied, no_ops, report_key);
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log_line(&mut log_buf, &msg);
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on_progress(&msg);
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println!("{}", msg);
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}
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log_line(&mut log_buf, &format!("\nAgents complete: {} run, {} errors",
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agent_num - agent_errors, agent_errors));
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log_line(&mut log_buf, &format!("\nAgents complete: {} run, {} errors, {} actions ({} applied)",
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agent_num - agent_errors, agent_errors, total_actions, total_applied));
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store.save()?;
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// --- Step 3: Apply consolidation actions ---
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log_line(&mut log_buf, "\n--- Step 3: Apply consolidation actions ---");
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on_progress("applying actions");
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println!("\n--- Applying consolidation actions ---");
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*store = Store::load()?;
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if reports.is_empty() {
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log_line(&mut log_buf, " No reports to apply.");
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} else {
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match apply_consolidation(store, true, None) {
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Ok(()) => log_line(&mut log_buf, " Applied."),
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Err(e) => {
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let msg = format!(" ERROR applying consolidation: {}", e);
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log_line(&mut log_buf, &msg);
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eprintln!("{}", msg);
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}
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}
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}
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// --- Step 3b: Link orphans ---
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log_line(&mut log_buf, "\n--- Step 3b: Link orphans ---");
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// --- Step 3: Link orphans ---
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log_line(&mut log_buf, "\n--- Step 3: Link orphans ---");
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on_progress("linking orphans");
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println!("\n--- Linking orphan nodes ---");
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*store = Store::load()?;
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@ -175,8 +173,8 @@ pub fn consolidate_full_with_progress(
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let (lo_orphans, lo_added) = neuro::link_orphans(store, 2, 3, 0.15);
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log_line(&mut log_buf, &format!(" {} orphans, {} links added", lo_orphans, lo_added));
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// --- Step 3c: Cap degree ---
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log_line(&mut log_buf, "\n--- Step 3c: Cap degree ---");
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// --- Step 3b: Cap degree ---
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log_line(&mut log_buf, "\n--- Step 3b: Cap degree ---");
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on_progress("capping degree");
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println!("\n--- Capping node degree ---");
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*store = Store::load()?;
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@ -244,166 +242,64 @@ pub fn consolidate_full_with_progress(
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Ok(())
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}
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/// Find the most recent set of consolidation report keys from the store.
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fn find_consolidation_reports(store: &Store) -> Vec<String> {
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let mut keys: Vec<&String> = store.nodes.keys()
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.filter(|k| k.starts_with("_consolidation-"))
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.collect();
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keys.sort();
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keys.reverse();
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if keys.is_empty() { return Vec::new(); }
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// Group by timestamp (last segment after last '-')
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let latest_ts = keys[0].rsplit('-').next().unwrap_or("").to_string();
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keys.into_iter()
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.filter(|k| k.ends_with(&latest_ts))
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.cloned()
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.collect()
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}
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fn build_consolidation_prompt(store: &Store, report_keys: &[String]) -> Result<String, String> {
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let mut report_text = String::new();
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for key in report_keys {
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let content = store.nodes.get(key)
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.map(|n| n.content.as_str())
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.unwrap_or("");
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report_text.push_str(&format!("\n{}\n## Report: {}\n\n{}\n",
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"=".repeat(60), key, content));
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}
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super::prompts::load_prompt("consolidation", &[("{{REPORTS}}", &report_text)])
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}
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/// Run the full apply-consolidation pipeline.
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/// Re-parse and apply actions from stored consolidation reports.
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/// This is for manually re-processing reports — during normal consolidation,
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/// actions are applied inline as each agent runs.
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pub fn apply_consolidation(store: &mut Store, do_apply: bool, report_key: Option<&str>) -> Result<(), String> {
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let reports = if let Some(key) = report_key {
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let reports: Vec<String> = if let Some(key) = report_key {
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vec![key.to_string()]
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} else {
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find_consolidation_reports(store)
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// Find the most recent batch of reports
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let mut keys: Vec<&String> = store.nodes.keys()
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.filter(|k| k.starts_with("_consolidation-") && !k.contains("-actions-") && !k.contains("-log-"))
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.collect();
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keys.sort();
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keys.reverse();
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if keys.is_empty() { return Ok(()); }
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let latest_ts = keys[0].rsplit('-').next().unwrap_or("").to_string();
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keys.into_iter()
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.filter(|k| k.ends_with(&latest_ts))
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.cloned()
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.collect()
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};
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if reports.is_empty() {
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println!("No consolidation reports found.");
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println!("Run consolidation-agents first.");
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return Ok(());
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}
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println!("Found {} reports:", reports.len());
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for r in &reports {
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println!(" {}", r);
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let mut all_actions = Vec::new();
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for key in &reports {
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let content = store.nodes.get(key).map(|n| n.content.as_str()).unwrap_or("");
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let actions = knowledge::parse_all_actions(content);
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println!(" {} → {} actions", key, actions.len());
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all_actions.extend(actions);
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}
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println!("\nExtracting actions from reports...");
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let prompt = build_consolidation_prompt(store, &reports)?;
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println!(" Prompt: {} chars", prompt.len());
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let response = call_sonnet("consolidate", &prompt)?;
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let actions_value = parse_json_response(&response)?;
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let actions = actions_value.as_array()
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.ok_or("expected JSON array of actions")?;
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println!(" {} actions extracted", actions.len());
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// Store actions in the store
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let timestamp = store::format_datetime(store::now_epoch())
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.replace([':', '-'], "");
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let actions_key = format!("_consolidation-actions-{}", timestamp);
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let actions_json = serde_json::to_string_pretty(&actions_value).unwrap();
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store.upsert_provenance(&actions_key, &actions_json,
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store::Provenance::AgentConsolidate).ok();
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println!(" Stored: {}", actions_key);
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let link_actions: Vec<_> = actions.iter()
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.filter(|a| a.get("action").and_then(|v| v.as_str()) == Some("link"))
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.collect();
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let manual_actions: Vec<_> = actions.iter()
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.filter(|a| a.get("action").and_then(|v| v.as_str()) == Some("manual"))
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.collect();
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if !do_apply {
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// Dry run
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println!("\n{}", "=".repeat(60));
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println!("DRY RUN — {} actions proposed", actions.len());
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println!("{}\n", "=".repeat(60));
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if !link_actions.is_empty() {
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println!("## Links to add ({})\n", link_actions.len());
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for (i, a) in link_actions.iter().enumerate() {
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let src = a.get("source").and_then(|v| v.as_str()).unwrap_or("?");
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let tgt = a.get("target").and_then(|v| v.as_str()).unwrap_or("?");
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let reason = a.get("reason").and_then(|v| v.as_str()).unwrap_or("");
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println!(" {:2}. {} → {} ({})", i + 1, src, tgt, reason);
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println!("\nDRY RUN — {} actions parsed", all_actions.len());
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for action in &all_actions {
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match &action.kind {
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knowledge::ActionKind::Link { source, target } =>
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println!(" LINK {} → {}", source, target),
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knowledge::ActionKind::WriteNode { key, .. } =>
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println!(" WRITE {}", key),
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knowledge::ActionKind::Refine { key, .. } =>
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println!(" REFINE {}", key),
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}
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}
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if !manual_actions.is_empty() {
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println!("\n## Manual actions needed ({})\n", manual_actions.len());
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for a in &manual_actions {
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let prio = a.get("priority").and_then(|v| v.as_str()).unwrap_or("?");
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let desc = a.get("description").and_then(|v| v.as_str()).unwrap_or("?");
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println!(" [{}] {}", prio, desc);
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}
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}
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println!("\n{}", "=".repeat(60));
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println!("To apply: poc-memory apply-consolidation --apply");
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println!("{}", "=".repeat(60));
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println!("\nTo apply: poc-memory apply-consolidation --apply");
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return Ok(());
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}
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// Apply
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let mut applied = 0usize;
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let mut skipped = 0usize;
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if !link_actions.is_empty() {
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println!("\nApplying {} links...", link_actions.len());
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for a in &link_actions {
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let src = a.get("source").and_then(|v| v.as_str()).unwrap_or("");
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let tgt = a.get("target").and_then(|v| v.as_str()).unwrap_or("");
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if src.is_empty() || tgt.is_empty() { skipped += 1; continue; }
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let source = match store.resolve_key(src) {
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Ok(s) => s,
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Err(e) => { println!(" ? {} → {}: {}", src, tgt, e); skipped += 1; continue; }
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};
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let target = match store.resolve_key(tgt) {
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Ok(t) => t,
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Err(e) => { println!(" ? {} → {}: {}", src, tgt, e); skipped += 1; continue; }
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};
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// Refine target to best-matching section
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let source_content = store.nodes.get(&source)
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.map(|n| n.content.as_str()).unwrap_or("");
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let target = neuro::refine_target(store, source_content, &target);
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let exists = store.relations.iter().any(|r|
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r.source_key == source && r.target_key == target && !r.deleted
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);
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if exists { skipped += 1; continue; }
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let source_uuid = match store.nodes.get(&source) { Some(n) => n.uuid, None => { skipped += 1; continue; } };
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let target_uuid = match store.nodes.get(&target) { Some(n) => n.uuid, None => { skipped += 1; continue; } };
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let rel = new_relation(
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source_uuid, target_uuid,
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store::RelationType::Auto,
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0.5,
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&source, &target,
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);
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if store.add_relation(rel).is_ok() {
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println!(" + {} → {}", source, target);
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applied += 1;
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}
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}
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}
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if !manual_actions.is_empty() {
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println!("\n## Manual actions (not auto-applied):\n");
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for a in &manual_actions {
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let prio = a.get("priority").and_then(|v| v.as_str()).unwrap_or("?");
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let desc = a.get("description").and_then(|v| v.as_str()).unwrap_or("?");
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println!(" [{}] {}", prio, desc);
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let ts = store::format_datetime(store::now_epoch()).replace([':', '-', 'T'], "");
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let mut applied = 0;
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for action in &all_actions {
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if knowledge::apply_action(store, action, "consolidate", &ts, 0) {
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applied += 1;
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}
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}
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@ -411,9 +307,6 @@ pub fn apply_consolidation(store: &mut Store, do_apply: bool, report_key: Option
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store.save()?;
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}
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println!("\n{}", "=".repeat(60));
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println!("Applied: {} Skipped: {} Manual: {}", applied, skipped, manual_actions.len());
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println!("{}", "=".repeat(60));
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println!("Applied: {}/{} actions", applied, all_actions.len());
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Ok(())
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}
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