consciousness/poc-memory/src/cli/agent.rs

// cli/agent.rs — agent subcommand handlers

use crate::store;
use crate::agents::llm;

pub fn cmd_run_agent(agent: &str, count: usize, target: &[String], query: Option<&str>, dry_run: bool, debug: bool) -> Result<(), String> {
    if dry_run {
        std::env::set_var("POC_MEMORY_DRY_RUN", "1");
    }
    let mut store = store::Store::load()?;
    let log = |msg: &str| eprintln!("[{}] {}", agent, msg);

    // Resolve targets: explicit --target, --query, or agent's default query
    let resolved_targets: Vec<String> = if !target.is_empty() {
        target.to_vec()
    } else if let Some(q) = query {
        let graph = store.build_graph();
        let stages = crate::search::Stage::parse_pipeline(q)?;
        let results = crate::search::run_query(&stages, vec![], &graph, &store, false, count);
        if results.is_empty() {
            return Err(format!("query returned no results: {}", q));
        }
        let keys: Vec<String> = results.into_iter().map(|(k, _)| k).collect();
        eprintln!("[{}] query matched {} nodes", agent, keys.len());
        keys
    } else {
        vec![] // use agent's built-in query
    };

    if !resolved_targets.is_empty() {
        // Run agent once with all targets as seeds
        crate::agents::knowledge::run_one_agent_with_keys(
            &mut store, agent, &resolved_targets, count, "test", &log, debug,
        )?;
    } else if debug {
        crate::agents::knowledge::run_one_agent(
            &mut store, agent, count, "test", &log, true,
        )?;
    } else {
        crate::agents::knowledge::run_and_apply_with_log(
            &mut store, agent, count, "test", &log,
        )?;
    }
    Ok(())
}

pub fn cmd_consolidate_batch(count: usize, auto: bool, agent: Option<String>) -> Result<(), String> {
    let store = store::Store::load()?;

    if let Some(agent_name) = agent {
        let batch = crate::agents::prompts::agent_prompt(&store, &agent_name, count)?;
        println!("{}", batch.prompt);
        Ok(())
    } else {
        crate::agents::prompts::consolidation_batch(&store, count, auto)
    }
}

pub fn cmd_replay_queue(count: usize) -> Result<(), String> {
    let store = store::Store::load()?;
    let queue = crate::neuro::replay_queue(&store, count);
    println!("Replay queue ({} items):", queue.len());
    for (i, item) in queue.iter().enumerate() {
        println!("  {:2}. [{:.3}] {:>10}  {} (interval={}d, emotion={:.1}, spectral={:.1})",
            i + 1, item.priority, item.classification, item.key,
            item.interval_days, item.emotion, item.outlier_score);
    }
    Ok(())
}

pub fn cmd_consolidate_session() -> Result<(), String> {
    let store = store::Store::load()?;
    let plan = crate::neuro::consolidation_plan(&store);
    println!("{}", crate::neuro::format_plan(&plan));
    Ok(())
}

pub fn cmd_consolidate_full() -> Result<(), String> {
    let mut store = store::Store::load()?;
    crate::consolidate::consolidate_full(&mut store)
}

pub fn cmd_digest_links(do_apply: bool) -> Result<(), String> {
    let store = store::Store::load()?;
    let links = crate::digest::parse_all_digest_links(&store);
    drop(store);
    println!("Found {} unique links from digest nodes", links.len());

    if !do_apply {
        for (i, link) in links.iter().enumerate() {
            println!("  {:3}. {} → {}", i + 1, link.source, link.target);
            if !link.reason.is_empty() {
                println!("       ({})", &link.reason[..link.reason.len().min(80)]);
            }
        }
        println!("\nTo apply: poc-memory digest-links --apply");
        return Ok(());
    }

    let mut store = store::Store::load()?;
    let (applied, skipped, fallbacks) = crate::digest::apply_digest_links(&mut store, &links);
    println!("\nApplied: {} ({} file-level fallbacks)  Skipped: {}", applied, fallbacks, skipped);
    Ok(())
}

pub fn cmd_journal_enrich(_jsonl_path: &str, _entry_text: &str, _grep_line: usize) -> Result<(), String> {
    Err("journal-enrich has been removed — use the observation agent instead.".into())
}

pub fn cmd_apply_consolidation(_do_apply: bool, _report_file: Option<&str>) -> Result<(), String> {
    Err("apply-consolidation has been removed — agents now apply changes via tool calls directly.".into())
}

pub fn cmd_knowledge_loop(_max_cycles: usize, _batch_size: usize, _window: usize, _max_depth: i32) -> Result<(), String> {
    Err("knowledge-loop has been removed — agents now use tool calls directly. Use `poc-memory agent run` instead.".into())
}

pub fn cmd_fact_mine(_path: &str, _batch: bool, _dry_run: bool, _output_file: Option<&str>, _min_messages: usize) -> Result<(), String> {
    Err("fact-mine has been removed — use the observation agent instead.".into())
}

pub fn cmd_fact_mine_store(_path: &str) -> Result<(), String> {
    Err("fact-mine-store has been removed — use the observation agent instead.".into())
}

/// Sample recent actions from each agent type, sort by quality using
/// LLM pairwise comparison, report per-type rankings.
/// Elo ratings file path
fn elo_path() -> std::path::PathBuf {
    crate::config::get().data_dir.join("agent-elo.json")
}

/// Load persisted Elo ratings, or initialize at 1000.0
fn load_elo_ratings(agent_types: &[&str]) -> std::collections::HashMap<String, f64> {
    let path = elo_path();
    let mut ratings: std::collections::HashMap<String, f64> = std::fs::read_to_string(&path)
        .ok()
        .and_then(|s| serde_json::from_str(&s).ok())
        .unwrap_or_default();
    for t in agent_types {
        ratings.entry(t.to_string()).or_insert(1000.0);
    }
    ratings
}

fn save_elo_ratings(ratings: &std::collections::HashMap<String, f64>) {
    let path = elo_path();
    if let Ok(json) = serde_json::to_string_pretty(ratings) {
        let _ = std::fs::write(path, json);
    }
}

pub fn cmd_evaluate_agents(matchups: usize, model: &str, dry_run: bool) -> Result<(), String> {
    use skillratings::elo::{elo, EloConfig, EloRating};
    use skillratings::Outcomes;

    let store = store::Store::load()?;

    let agent_types: Vec<&str> = vec![
        "linker", "organize", "replay", "connector",
        "separator", "transfer", "distill", "rename",
    ];

    // Load agent prompt files
    let prompts_dir = {
        let repo = std::path::PathBuf::from(env!("CARGO_MANIFEST_DIR")).join("agents");
        if repo.is_dir() { repo } else { crate::store::memory_dir().join("agents") }
    };

    // Collect recent actions per agent type
    let mut actions: std::collections::HashMap<String, Vec<(String, String)>> = std::collections::HashMap::new();

    for agent_type in &agent_types {
        let prompt_file = prompts_dir.join(format!("{}.agent", agent_type));
        let agent_prompt = std::fs::read_to_string(&prompt_file)
            .unwrap_or_default()
            .lines().skip(1).collect::<Vec<_>>().join("\n");
        let agent_prompt = crate::util::truncate(&agent_prompt, 500, "...");

        let prefix = format!("_consolidate-{}", agent_type);
        let mut keys: Vec<(String, i64)> = store.nodes.iter()
            .filter(|(k, _)| k.starts_with(&prefix))
            .map(|(k, n)| (k.clone(), n.timestamp))
            .collect();
        keys.sort_by(|a, b| b.1.cmp(&a.1));
        keys.truncate(20); // pool of recent actions to sample from

        let mut type_actions = Vec::new();
        for (key, _) in &keys {
            let report = store.nodes.get(key)
                .map(|n| n.content.clone())
                .unwrap_or_default();

            let mut target_content = String::new();
            let mut seen = std::collections::HashSet::new();
            for word in report.split_whitespace() {
                let clean = word.trim_matches(|c: char| !c.is_alphanumeric() && c != '-' && c != '_');
                if clean.len() > 10 && seen.insert(clean.to_string()) && store.nodes.contains_key(clean) {
                    if let Some(node) = store.nodes.get(clean) {
                        let preview = crate::util::truncate(&node.content, 200, "...");
                        target_content.push_str(&format!("\n### {}\n{}\n", clean, preview));
                        if target_content.len() > 1500 { break; }
                    }
                }
            }

            let context = format!(
                "## Agent instructions\n{}\n\n## Report output\n{}\n\n## Affected nodes\n{}",
                agent_prompt,
                crate::util::truncate(&report, 1000, "..."),
                if target_content.is_empty() { "(none found)".into() } else { target_content }
            );
            type_actions.push((key.clone(), context));
        }
        actions.insert(agent_type.to_string(), type_actions);
    }

    // Filter to types that have at least 1 action
    let active_types: Vec<&str> = agent_types.iter()
        .filter(|t| actions.get(**t).map(|a| !a.is_empty()).unwrap_or(false))
        .copied()
        .collect();

    if active_types.len() < 2 {
        return Err("Need at least 2 agent types with actions".into());
    }

    eprintln!("Evaluating {} agent types with {} matchups (model={})",
        active_types.len(), matchups, model);

    if dry_run {
        let t1 = active_types[0];
        let t2 = active_types[active_types.len() - 1];
        let a1 = &actions[t1][0];
        let a2 = &actions[t2][0];
        let sample_a = (t1.to_string(), a1.0.clone(), a1.1.clone());
        let sample_b = (t2.to_string(), a2.0.clone(), a2.1.clone());
        println!("=== DRY RUN: Example comparison ===\n");
        println!("{}", build_compare_prompt(&sample_a, &sample_b));
        return Ok(());
    }

    // Load persisted ratings
    let mut ratings = load_elo_ratings(&agent_types);
    let config = EloConfig { k: 32.0 };
    // Simple but adequate RNG: xorshift32
    let mut rng = std::time::SystemTime::now()
        .duration_since(std::time::UNIX_EPOCH).unwrap().subsec_nanos() | 1;
    let mut next_rng = || -> usize {
        rng ^= rng << 13;
        rng ^= rng >> 17;
        rng ^= rng << 5;
        rng as usize
    };

    for i in 0..matchups {
        // Pick two different random agent types
        let idx_a = next_rng() % active_types.len();
        let mut idx_b = next_rng() % active_types.len();
        if idx_b == idx_a { idx_b = (idx_b + 1) % active_types.len(); }

        let type_a = active_types[idx_a];
        let type_b = active_types[idx_b];

        // Pick random recent action from each
        let acts_a = &actions[type_a];
        let acts_b = &actions[type_b];
        let act_a = &acts_a[next_rng() % acts_a.len()];
        let act_b = &acts_b[next_rng() % acts_b.len()];

        let sample_a = (type_a.to_string(), act_a.0.clone(), act_a.1.clone());
        let sample_b = (type_b.to_string(), act_b.0.clone(), act_b.1.clone());

        let result = llm_compare(&sample_a, &sample_b, model);

        let rating_a = EloRating { rating: ratings[type_a] };
        let rating_b = EloRating { rating: ratings[type_b] };

        let outcome = match result {
            Ok(std::cmp::Ordering::Less) => Outcomes::WIN,    // A wins
            Ok(std::cmp::Ordering::Greater) => Outcomes::LOSS, // B wins
            _ => Outcomes::WIN, // default to A
        };

        let (new_a, new_b) = elo(&rating_a, &rating_b, &outcome, &config);
        ratings.insert(type_a.to_string(), new_a.rating);
        ratings.insert(type_b.to_string(), new_b.rating);

        eprint!("  matchup {}/{}: {} vs {} → {}\r",
            i + 1, matchups, type_a, type_b,
            if matches!(outcome, Outcomes::WIN) { type_a } else { type_b });
    }
    eprintln!();

    // Save updated ratings
    save_elo_ratings(&ratings);

    // Print rankings
    let mut ranked: Vec<_> = ratings.iter().collect();
    ranked.sort_by(|a, b| b.1.total_cmp(a.1));

    println!("\nAgent Elo Ratings (after {} matchups):\n", matchups);
    for (agent_type, rating) in &ranked {
        let bar_len = ((*rating - 800.0) / 10.0).max(0.0) as usize;
        let bar = "#".repeat(bar_len.min(40));
        println!("  {:12} {:7.1}  {}", agent_type, rating, bar);
    }

    Ok(())
}

fn build_compare_prompt(
    a: &(String, String, String),
    b: &(String, String, String),
) -> String {
    if a.0 == b.0 {
        // Same agent type — show instructions once
        // Split context at "## Report output" to extract shared prompt
        let split_a: Vec<&str> = a.2.splitn(2, "## Report output").collect();
        let split_b: Vec<&str> = b.2.splitn(2, "## Report output").collect();
        let shared_prompt = split_a.first().unwrap_or(&"");
        let report_a = split_a.get(1).unwrap_or(&"");
        let report_b = split_b.get(1).unwrap_or(&"");
        format!(
            "Compare two actions from the same {} agent. Which was better?\n\n\
             {}\n\n\
             ## Action A\n## Report output{}\n\n\
             ## Action B\n## Report output{}\n\n\
             Say which is better and why in 1-2 sentences, then end with:\n\
             BETTER: A  or  BETTER: B\n\
             You must pick one. No ties.",
            a.0, shared_prompt, report_a, report_b
        )
    } else {
        format!(
            "Compare these two memory graph agent actions. Which one was better \
             for building a useful, well-organized knowledge graph?\n\n\
             ## Action A ({} agent)\n{}\n\n\
             ## Action B ({} agent)\n{}\n\n\
             Say which is better and why in 1-2 sentences, then end with:\n\
             BETTER: A  or  BETTER: B\n\
             You must pick one. No ties.",
            a.0, a.2, b.0, b.2
        )
    }
}

fn llm_compare(
    a: &(String, String, String),
    b: &(String, String, String),
    model: &str,
) -> Result<std::cmp::Ordering, String> {
    let prompt = build_compare_prompt(a, b);

    let response = if model == "haiku" {
        llm::call_haiku("compare", &prompt)?
    } else {
        llm::call_sonnet("compare", &prompt)?
    };
    let response = response.trim().to_uppercase();

    if response.contains("BETTER: B") {
        Ok(std::cmp::Ordering::Greater)
    } else {
        // Default to A (includes "BETTER: A" and any unparseable response)
        Ok(std::cmp::Ordering::Less)
    }
}