agent run: add --target flag to run agents on specific nodes
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
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3 changed files with 50 additions and 5 deletions
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@ -646,6 +646,31 @@ pub fn run_and_apply_with_log(
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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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/// Run an agent with explicit target keys, bypassing the agent's query.
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pub fn run_one_agent_with_keys(
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store: &mut Store,
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agent_name: &str,
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keys: &[String],
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count: usize,
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llm_tag: &str,
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log: &dyn Fn(&str),
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debug: bool,
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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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log(&format!("targeting: {}", keys.join(", ")));
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let graph = store.build_graph();
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let (prompt, extra_keys) = super::defs::resolve_placeholders(
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&def.prompt, store, &graph, keys, count,
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);
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let mut all_keys: Vec<String> = keys.to_vec();
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all_keys.extend(extra_keys);
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let agent_batch = super::prompts::AgentBatch { prompt, node_keys: all_keys };
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run_one_agent_inner(store, agent_name, &def, agent_batch, llm_tag, log, debug)
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}
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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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@ -660,6 +685,18 @@ pub fn run_one_agent(
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log("building prompt");
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let agent_batch = super::defs::run_agent(store, &def, batch_size)?;
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run_one_agent_inner(store, agent_name, &def, agent_batch, llm_tag, log, debug)
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}
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fn run_one_agent_inner(
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store: &mut Store,
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agent_name: &str,
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def: &super::defs::AgentDef,
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agent_batch: super::prompts::AgentBatch,
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_llm_tag: &str,
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log: &dyn Fn(&str),
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debug: bool,
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) -> Result<AgentResult, String> {
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let prompt_kb = agent_batch.prompt.len() / 1024;
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let tools_desc = if def.tools.is_empty() { "no tools".into() }
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else { format!("{} tools", def.tools.len()) };
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@ -679,7 +716,7 @@ pub fn run_one_agent(
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log(&format!("log: {}", log_path.display()));
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log("calling LLM");
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let output = llm::call_for_def(&def, &agent_batch.prompt)?;
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let output = llm::call_for_def(def, &agent_batch.prompt)?;
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// Append response to same log file
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use std::io::Write;
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@ -5,14 +5,19 @@ use crate::store::StoreView;
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use crate::agents::llm;
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use std::sync::atomic::{AtomicUsize, Ordering};
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pub fn cmd_run_agent(agent: &str, count: usize, dry_run: bool, debug: bool) -> Result<(), String> {
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pub fn cmd_run_agent(agent: &str, count: usize, target: &[String], dry_run: bool, debug: bool) -> Result<(), String> {
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if dry_run {
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std::env::set_var("POC_MEMORY_DRY_RUN", "1");
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}
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let mut store = store::Store::load()?;
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let log = |msg: &str| eprintln!("[{}] {}", agent, msg);
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if debug {
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if !target.is_empty() {
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// Override agent's query with explicit target keys
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crate::agents::knowledge::run_one_agent_with_keys(
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&mut store, agent, target, count, "test", &log, debug,
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)?;
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} else if debug {
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crate::agents::knowledge::run_one_agent(
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&mut store, agent, count, "test", &log, true,
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)?;
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@ -565,6 +565,9 @@ enum AgentCmd {
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/// Batch size (number of seed nodes/fragments)
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#[arg(long, default_value_t = 5)]
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count: usize,
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/// Target specific node keys (overrides agent's query)
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#[arg(long)]
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target: Vec<String>,
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/// Dry run — set POC_MEMORY_DRY_RUN=1 so mutations are no-ops
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#[arg(long)]
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dry_run: bool,
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@ -814,8 +817,8 @@ fn main() {
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AgentCmd::FactMine { path, batch, dry_run, output, min_messages }
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=> cli::agent::cmd_fact_mine(&path, batch, dry_run, output.as_deref(), min_messages),
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AgentCmd::FactMineStore { path } => cli::agent::cmd_fact_mine_store(&path),
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AgentCmd::Run { agent, count, dry_run, debug }
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=> cli::agent::cmd_run_agent(&agent, count, dry_run, debug),
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AgentCmd::Run { agent, count, target, dry_run, debug }
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=> cli::agent::cmd_run_agent(&agent, count, &target, dry_run, debug),
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AgentCmd::ReplayQueue { count } => cli::agent::cmd_replay_queue(count),
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AgentCmd::Evaluate { matchups, model, dry_run }
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=> cli::agent::cmd_evaluate_agents(matchups, &model, dry_run),
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