agents: extract shared run_one_agent, standardize output formats
Three places duplicated the agent execution loop (build prompt → call LLM → store output → parse actions → record visits): consolidate.rs, knowledge.rs, and daemon.rs. Extract into run_one_agent() in knowledge.rs that all three now call. Also standardize consolidation agent prompts to use WRITE_NODE/LINK/REFINE — the same commands the parser handles. Previously agents output CATEGORIZE/NOTE/EXTRACT/DIGEST/DIFFERENTIATE/MERGE/COMPRESS which were silently dropped after the second-LLM-call removal.
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ProofOfConcept
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8 changed files with 124 additions and 189 deletions
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@ -13,7 +13,6 @@
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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;
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use super::knowledge;
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use crate::neuro;
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use crate::store::{self, Store};
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@ -102,24 +101,10 @@ pub fn consolidate_full_with_progress(
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*store = Store::load()?;
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}
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let agent_batch = match super::prompts::agent_prompt(store, agent_type, *count) {
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Ok(b) => b,
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Err(e) => {
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let msg = format!(" ERROR building prompt: {}", e);
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log_line(&mut log_buf, &msg);
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eprintln!("{}", msg);
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agent_errors += 1;
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continue;
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}
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};
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log_line(&mut log_buf, &format!(" Prompt: {} chars (~{} tokens), {} nodes",
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agent_batch.prompt.len(), agent_batch.prompt.len() / 4, agent_batch.node_keys.len()));
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let response = match call_sonnet("consolidate", &agent_batch.prompt) {
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let result = match knowledge::run_one_agent(store, agent_type, *count, "consolidate") {
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Ok(r) => r,
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Err(e) => {
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let msg = format!(" ERROR from Sonnet: {}", e);
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let msg = format!(" ERROR: {}", e);
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log_line(&mut log_buf, &msg);
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eprintln!("{}", msg);
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agent_errors += 1;
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@ -127,34 +112,19 @@ pub fn consolidate_full_with_progress(
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}
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};
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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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// 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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for action in &result.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_actions += result.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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if let Err(e) = store.record_agent_visits(&agent_batch.node_keys, agent_type) {
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log_line(&mut log_buf, &format!(" Visit recording: {}", e));
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}
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}
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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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let msg = format!(" Done: {} actions ({} applied, {} no-ops)",
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result.actions.len(), applied, result.no_ops);
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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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@ -130,43 +130,19 @@ fn job_consolidation_agent(
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ctx.log_line("loading store");
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let mut store = crate::store::Store::load()?;
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let label = if batch > 0 {
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format!("{} (batch={})", agent, batch)
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} else {
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agent.to_string()
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};
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ctx.log_line(&format!("building prompt: {}", label));
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let agent_batch = super::prompts::agent_prompt(&store, &agent, batch)?;
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ctx.log_line(&format!("prompt: {} chars ({} nodes), calling Sonnet",
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agent_batch.prompt.len(), agent_batch.node_keys.len()));
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let response = super::llm::call_sonnet("consolidate", &agent_batch.prompt)?;
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ctx.log_line(&format!("running agent: {} (batch={})", agent, batch));
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let result = super::knowledge::run_one_agent(&mut store, &agent, batch, "consolidate")?;
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let ts = crate::store::format_datetime(crate::store::now_epoch())
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.replace([':', '-', 'T'], "");
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let report_key = format!("_consolidation-{}-{}", agent, ts);
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store.upsert_provenance(&report_key, &response,
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crate::store::Provenance::AgentConsolidate).ok();
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// Parse and apply actions inline
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let actions = super::knowledge::parse_all_actions(&response);
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let mut applied = 0;
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for action in &actions {
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for action in &result.actions {
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if super::knowledge::apply_action(&mut store, action, &agent, &ts, 0) {
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applied += 1;
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}
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}
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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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if let Err(e) = store.record_agent_visits(&agent_batch.node_keys, &agent) {
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ctx.log_line(&format!("visit recording: {}", e));
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}
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}
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ctx.log_line(&format!("done: {} actions ({} applied) → {}",
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actions.len(), applied, report_key));
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ctx.log_line(&format!("done: {} actions ({} applied)", result.actions.len(), applied));
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Ok(())
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})
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}
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@ -319,7 +319,58 @@ fn agent_provenance(agent: &str) -> store::Provenance {
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}
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// ---------------------------------------------------------------------------
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// Agent runners
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// Shared agent execution
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// ---------------------------------------------------------------------------
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/// Result of running a single agent through the common pipeline.
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pub struct AgentResult {
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pub output: String,
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pub actions: Vec<Action>,
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pub no_ops: usize,
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pub node_keys: Vec<String>,
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}
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/// Run a single agent: build prompt → call LLM → store output → parse actions → record visits.
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///
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/// This is the common pipeline shared by the knowledge loop, consolidation pipeline,
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/// and daemon. Callers handle action application (with or without depth tracking).
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pub fn run_one_agent(
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store: &mut Store,
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agent_name: &str,
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batch_size: usize,
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llm_tag: &str,
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) -> Result<AgentResult, String> {
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let def = super::defs::get_def(agent_name)
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.ok_or_else(|| format!("no .agent file for {}", agent_name))?;
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let agent_batch = super::defs::run_agent(store, &def, batch_size)?;
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let output = llm::call_sonnet(llm_tag, &agent_batch.prompt)?;
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// Store raw output 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!("_{}-{}-{}", llm_tag, agent_name, ts);
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let provenance = agent_provenance(agent_name);
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store.upsert_provenance(&report_key, &output, provenance).ok();
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let actions = parse_all_actions(&output);
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let no_ops = count_no_ops(&output);
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// Record visits for processed nodes
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if !agent_batch.node_keys.is_empty() {
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store.record_agent_visits(&agent_batch.node_keys, agent_name).ok();
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}
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Ok(AgentResult {
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output,
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actions,
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no_ops,
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node_keys: agent_batch.node_keys,
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})
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}
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// ---------------------------------------------------------------------------
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// Conversation fragment selection
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// ---------------------------------------------------------------------------
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/// Extract human-readable dialogue from a conversation JSONL
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@ -573,51 +624,18 @@ fn run_cycle(
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for agent_name in &agent_names {
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eprintln!("\n --- {} (n={}) ---", agent_name, config.batch_size);
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let def = match super::defs::get_def(agent_name) {
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Some(d) => d,
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None => {
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eprintln!(" SKIP: no .agent file for {}", agent_name);
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continue;
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}
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};
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let agent_batch = match super::defs::run_agent(&store, &def, config.batch_size) {
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Ok(b) => b,
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Err(e) => {
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eprintln!(" ERROR building prompt: {}", e);
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continue;
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}
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};
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eprintln!(" prompt: {} chars ({} nodes)", agent_batch.prompt.len(), agent_batch.node_keys.len());
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let output = llm::call_sonnet("knowledge", &agent_batch.prompt);
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// Record visits for processed nodes
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if !agent_batch.node_keys.is_empty() {
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if let Err(e) = store.record_agent_visits(&agent_batch.node_keys, agent_name) {
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eprintln!(" visit recording: {}", e);
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}
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}
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let output = match output {
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Ok(o) => o,
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let result = match run_one_agent(&mut store, agent_name, config.batch_size, "knowledge") {
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Ok(r) => r,
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Err(e) => {
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eprintln!(" ERROR: {}", e);
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continue;
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}
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};
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// Store raw output as a node (for debugging/audit)
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let raw_key = format!("_knowledge-{}-{}", agent_name, timestamp);
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let raw_content = format!("# {} Agent Results — {}\n\n{}", agent_name, timestamp, output);
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store.upsert_provenance(&raw_key, &raw_content,
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agent_provenance(agent_name)).ok();
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let mut actions = result.actions;
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all_no_ops += result.no_ops;
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let mut actions = parse_all_actions(&output);
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let no_ops = count_no_ops(&output);
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all_no_ops += no_ops;
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eprintln!(" Actions: {} No-ops: {}", actions.len(), no_ops);
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eprintln!(" Actions: {} No-ops: {}", actions.len(), result.no_ops);
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let mut applied = 0;
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for action in &mut actions {
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