// knowledge.rs — knowledge production agents and convergence loop
//
// Rust port of knowledge_agents.py + knowledge_loop.py.
// Four agents mine the memory graph for new knowledge:
//   1. Observation — extract facts from raw conversations
//   2. Extractor   — find patterns in node clusters
//   3. Connector   — find cross-domain structural connections
//   4. Challenger  — stress-test existing knowledge nodes
//
// The loop runs agents in sequence, applies results, measures
// convergence via graph-structural metrics (sigma, CC, communities).

use crate::graph::Graph;
use super::llm;
use crate::spectral;
use crate::store::{self, Store, new_relation, RelationType};

use regex::Regex;
use serde::{Deserialize, Serialize};
use std::collections::{HashMap, HashSet};
use std::fs;
use std::path::{Path, PathBuf};

fn projects_dir() -> PathBuf {
    crate::config::get().projects_dir.clone()
}

// ---------------------------------------------------------------------------
// Action types
// ---------------------------------------------------------------------------

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Action {
    pub kind: ActionKind,
    pub confidence: Confidence,
    pub weight: f64,
    pub depth: i32,
    pub applied: Option<bool>,
    pub rejected_reason: Option<String>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ActionKind {
    WriteNode {
        key: String,
        content: String,
        covers: Vec<String>,
    },
    Link {
        source: String,
        target: String,
    },
    Refine {
        key: String,
        content: String,
    },
}

#[derive(Debug, Clone, Copy, Serialize, Deserialize)]
#[serde(rename_all = "lowercase")]
pub enum Confidence {
    High,
    Medium,
    Low,
}

impl Confidence {
    fn weight(self) -> f64 {
        match self {
            Self::High => 1.0,
            Self::Medium => 0.6,
            Self::Low => 0.3,
        }
    }

    fn value(self) -> f64 {
        match self {
            Self::High => 0.9,
            Self::Medium => 0.6,
            Self::Low => 0.3,
        }
    }

    fn parse(s: &str) -> Self {
        match s.to_lowercase().as_str() {
            "high" => Self::High,
            "low" => Self::Low,
            _ => Self::Medium,
        }
    }
}

// ---------------------------------------------------------------------------
// Action parsing
// ---------------------------------------------------------------------------

pub fn parse_write_nodes(text: &str) -> Vec<Action> {
    let re = Regex::new(r"(?s)WRITE_NODE\s+(\S+)\s*\n(.*?)END_NODE").unwrap();
    let conf_re = Regex::new(r"(?i)CONFIDENCE:\s*(high|medium|low)").unwrap();
    let covers_re = Regex::new(r"COVERS:\s*(.+)").unwrap();

    re.captures_iter(text)
        .map(|cap| {
            let key = cap[1].to_string();
            let mut content = cap[2].trim().to_string();

            let confidence = conf_re
                .captures(&content)
                .map(|c| Confidence::parse(&c[1]))
                .unwrap_or(Confidence::Medium);
            content = conf_re.replace(&content, "").trim().to_string();

            let covers: Vec<String> = covers_re
                .captures(&content)
                .map(|c| c[1].split(',').map(|s| s.trim().to_string()).collect())
                .unwrap_or_default();
            content = covers_re.replace(&content, "").trim().to_string();

            Action {
                weight: confidence.weight(),
                kind: ActionKind::WriteNode { key, content, covers },
                confidence,
                depth: 0,
                applied: None,
                rejected_reason: None,
            }
        })
        .collect()
}

pub fn parse_links(text: &str) -> Vec<Action> {
    let re = Regex::new(r"(?m)^LINK\s+(\S+)\s+(\S+)").unwrap();
    re.captures_iter(text)
        .map(|cap| Action {
            kind: ActionKind::Link {
                source: cap[1].to_string(),
                target: cap[2].to_string(),
            },
            confidence: Confidence::Low,
            weight: 0.3,
            depth: -1,
            applied: None,
            rejected_reason: None,
        })
        .collect()
}

pub fn parse_refines(text: &str) -> Vec<Action> {
    let re = Regex::new(r"(?s)REFINE\s+(\S+)\s*\n(.*?)END_REFINE").unwrap();
    re.captures_iter(text)
        .map(|cap| {
            let key = cap[1].trim_matches('*').trim().to_string();
            Action {
                kind: ActionKind::Refine {
                    key,
                    content: cap[2].trim().to_string(),
                },
                confidence: Confidence::Medium,
                weight: 0.7,
                depth: 0,
                applied: None,
                rejected_reason: None,
            }
        })
        .collect()
}

pub fn parse_all_actions(text: &str) -> Vec<Action> {
    let mut actions = parse_write_nodes(text);
    actions.extend(parse_links(text));
    actions.extend(parse_refines(text));
    actions
}

pub fn count_no_ops(text: &str) -> usize {
    let no_conn = Regex::new(r"\bNO_CONNECTION\b").unwrap().find_iter(text).count();
    let affirm = Regex::new(r"\bAFFIRM\b").unwrap().find_iter(text).count();
    let no_extract = Regex::new(r"\bNO_EXTRACTION\b").unwrap().find_iter(text).count();
    no_conn + affirm + no_extract
}

// ---------------------------------------------------------------------------
// Inference depth tracking
// ---------------------------------------------------------------------------

const DEPTH_DB_KEY: &str = "_knowledge-depths";

#[derive(Default)]
pub struct DepthDb {
    depths: HashMap<String, i32>,
}

impl DepthDb {
    pub fn load(store: &Store) -> Self {
        let depths = store.nodes.get(DEPTH_DB_KEY)
            .and_then(|n| serde_json::from_str(&n.content).ok())
            .unwrap_or_default();
        Self { depths }
    }

    pub fn save(&self, store: &mut Store) {
        if let Ok(json) = serde_json::to_string(&self.depths) {
            store.upsert_provenance(DEPTH_DB_KEY, &json,
                store::Provenance::AgentKnowledgeObservation).ok();
        }
    }

    pub fn get(&self, key: &str) -> i32 {
        self.depths.get(key).copied().unwrap_or(0)
    }

    pub fn set(&mut self, key: String, depth: i32) {
        self.depths.insert(key, depth);
    }
}

/// Agent base depths: observation=1, extractor=2, connector=3
fn agent_base_depth(agent: &str) -> Option<i32> {
    match agent {
        "observation" => Some(1),
        "extractor" => Some(2),
        "connector" => Some(3),
        "challenger" => None,
        _ => Some(2),
    }
}

pub fn compute_action_depth(db: &DepthDb, action: &Action, agent: &str) -> i32 {
    match &action.kind {
        ActionKind::Link { .. } => -1,
        ActionKind::Refine { key, .. } => db.get(key),
        ActionKind::WriteNode { covers, .. } => {
            if !covers.is_empty() {
                covers.iter().map(|k| db.get(k)).max().unwrap_or(0) + 1
            } else {
                agent_base_depth(agent).unwrap_or(2)
            }
        }
    }
}

/// Confidence threshold that scales with inference depth.
pub fn required_confidence(depth: i32, base: f64) -> f64 {
    if depth <= 0 {
        return 0.0;
    }
    1.0 - (1.0 - base).powi(depth)
}

/// Confidence bonus from real-world use.
pub fn use_bonus(use_count: u32) -> f64 {
    if use_count == 0 {
        return 0.0;
    }
    1.0 - 1.0 / (1.0 + 0.15 * use_count as f64)
}

// ---------------------------------------------------------------------------
// Action application
// ---------------------------------------------------------------------------

fn stamp_content(content: &str, agent: &str, timestamp: &str, depth: i32) -> String {
    format!("<!-- author: {} | created: {} | depth: {} -->\n{}", agent, timestamp, depth, content)
}

/// Check if a link already exists between two keys.
fn has_edge(store: &Store, source: &str, target: &str) -> bool {
    store.relations.iter().any(|r| {
        !r.deleted
            && ((r.source_key == source && r.target_key == target)
                || (r.source_key == target && r.target_key == source))
    })
}

pub fn apply_action(
    store: &mut Store,
    action: &Action,
    agent: &str,
    timestamp: &str,
    depth: i32,
) -> bool {
    let provenance = agent_provenance(agent);

    match &action.kind {
        ActionKind::WriteNode { key, content, .. } => {
            let stamped = stamp_content(content, agent, timestamp, depth);
            store.upsert_provenance(key, &stamped, provenance).is_ok()
        }
        ActionKind::Link { source, target } => {
            if has_edge(store, source, target) {
                return false;
            }
            let source_uuid = match store.nodes.get(source.as_str()) {
                Some(n) => n.uuid,
                None => return false,
            };
            let target_uuid = match store.nodes.get(target.as_str()) {
                Some(n) => n.uuid,
                None => return false,
            };
            let mut rel = new_relation(
                source_uuid, target_uuid,
                RelationType::Link,
                0.3,
                source, target,
            );
            rel.provenance = provenance;
            store.add_relation(rel).is_ok()
        }
        ActionKind::Refine { key, content } => {
            let stamped = stamp_content(content, agent, timestamp, depth);
            store.upsert_provenance(key, &stamped, provenance).is_ok()
        }
    }
}

fn agent_provenance(agent: &str) -> store::Provenance {
    match agent {
        "observation" => store::Provenance::AgentKnowledgeObservation,
        "extractor" | "pattern" => store::Provenance::AgentKnowledgePattern,
        "connector"  => store::Provenance::AgentKnowledgeConnector,
        "challenger" => store::Provenance::AgentKnowledgeChallenger,
        _ => store::Provenance::Agent,
    }
}

// ---------------------------------------------------------------------------
// Agent runners
// ---------------------------------------------------------------------------

fn load_prompt(name: &str) -> Result<String, String> {
    super::prompts::load_prompt(name, &[])
}

fn get_graph_topology(store: &Store, graph: &Graph) -> String {
    format!("Nodes: {}  Relations: {}\n", store.nodes.len(), graph.edge_count())
}

/// Strip <system-reminder> blocks from text
fn strip_system_tags(text: &str) -> String {
    let re = Regex::new(r"(?s)<system-reminder>.*?</system-reminder>").unwrap();
    re.replace_all(text, "").trim().to_string()
}

/// Extract human-readable dialogue from a conversation JSONL
fn extract_conversation_text(path: &Path, max_chars: usize) -> String {
    let Ok(content) = fs::read_to_string(path) else { return String::new() };
    let mut fragments = Vec::new();
    let mut total = 0;

    for line in content.lines() {
        let Ok(obj) = serde_json::from_str::<serde_json::Value>(line) else { continue };

        let msg_type = obj.get("type").and_then(|v| v.as_str()).unwrap_or("");

        if msg_type == "user" && obj.get("userType").and_then(|v| v.as_str()) == Some("external") {
            if let Some(text) = extract_text_content(&obj) {
                let text = strip_system_tags(&text);
                if text.starts_with("[Request interrupted") { continue; }
                if text.len() > 5 {
                    fragments.push(format!("**{}:** {}", crate::config::get().user_name, text));
                    total += text.len();
                }
            }
        } else if msg_type == "assistant" {
            if let Some(text) = extract_text_content(&obj) {
                let text = strip_system_tags(&text);
                if text.len() > 10 {
                    fragments.push(format!("**{}:** {}", crate::config::get().assistant_name, text));
                    total += text.len();
                }
            }
        }

        if total > max_chars { break; }
    }
    fragments.join("\n\n")
}

fn extract_text_content(obj: &serde_json::Value) -> Option<String> {
    let msg = obj.get("message")?;
    let content = msg.get("content")?;
    if let Some(s) = content.as_str() {
        return Some(s.to_string());
    }
    if let Some(arr) = content.as_array() {
        let texts: Vec<&str> = arr.iter()
            .filter_map(|b| {
                if b.get("type")?.as_str()? == "text" {
                    b.get("text")?.as_str()
                } else {
                    None
                }
            })
            .collect();
        if !texts.is_empty() {
            return Some(texts.join("\n"));
        }
    }
    None
}

/// Count short user messages (dialogue turns) in a JSONL
fn count_dialogue_turns(path: &Path) -> usize {
    let Ok(content) = fs::read_to_string(path) else { return 0 };
    content.lines()
        .filter_map(|line| serde_json::from_str::<serde_json::Value>(line).ok())
        .filter(|obj| {
            obj.get("type").and_then(|v| v.as_str()) == Some("user")
                && obj.get("userType").and_then(|v| v.as_str()) == Some("external")
        })
        .filter(|obj| {
            let text = extract_text_content(obj).unwrap_or_default();
            text.len() > 5 && text.len() < 500
                && !text.starts_with("[Request interrupted")
                && !text.starts_with("Implement the following")
        })
        .count()
}

/// Select conversation fragments for the observation extractor
fn select_conversation_fragments(n: usize) -> Vec<(String, String)> {
    let projects = projects_dir();
    if !projects.exists() { return Vec::new(); }

    let mut jsonl_files: Vec<PathBuf> = Vec::new();
    if let Ok(dirs) = fs::read_dir(&projects) {
        for dir in dirs.filter_map(|e| e.ok()) {
            if !dir.path().is_dir() { continue; }
            if let Ok(files) = fs::read_dir(dir.path()) {
                for f in files.filter_map(|e| e.ok()) {
                    let p = f.path();
                    if p.extension().map(|x| x == "jsonl").unwrap_or(false) {
                        if let Ok(meta) = p.metadata() {
                            if meta.len() > 50_000 {
                                jsonl_files.push(p);
                            }
                        }
                    }
                }
            }
        }
    }

    let mut scored: Vec<(usize, PathBuf)> = jsonl_files.into_iter()
        .map(|f| (count_dialogue_turns(&f), f))
        .filter(|(turns, _)| *turns >= 10)
        .collect();
    scored.sort_by(|a, b| b.0.cmp(&a.0));

    let mut fragments = Vec::new();
    for (_, f) in scored.iter().take(n * 2) {
        let session_id = f.file_stem()
            .map(|s| s.to_string_lossy().to_string())
            .unwrap_or_else(|| "unknown".into());
        let text = extract_conversation_text(f, 8000);
        if text.len() > 500 {
            fragments.push((session_id, text));
        }
        if fragments.len() >= n { break; }
    }
    fragments
}

pub fn run_observation_extractor(store: &Store, graph: &Graph, batch_size: usize) -> Result<String, String> {
    let template = load_prompt("observation-extractor")?;
    let topology = get_graph_topology(store, graph);
    let fragments = select_conversation_fragments(batch_size);

    let mut results = Vec::new();
    for (i, (session_id, text)) in fragments.iter().enumerate() {
        eprintln!("  Observation extractor {}/{}: session {}... ({} chars)",
            i + 1, fragments.len(), &session_id[..session_id.len().min(12)], text.len());

        let prompt = template
            .replace("{{TOPOLOGY}}", &topology)
            .replace("{{CONVERSATIONS}}", &format!("### Session {}\n\n{}", session_id, text));

        let response = llm::call_sonnet("knowledge", &prompt)?;
        results.push(format!("## Session: {}\n\n{}", session_id, response));
    }
    Ok(results.join("\n\n---\n\n"))
}

/// Load spectral embedding from disk
fn load_spectral_embedding() -> HashMap<String, Vec<f64>> {
    spectral::load_embedding()
        .map(|emb| emb.coords)
        .unwrap_or_default()
}

fn spectral_distance(embedding: &HashMap<String, Vec<f64>>, a: &str, b: &str) -> f64 {
    let (Some(va), Some(vb)) = (embedding.get(a), embedding.get(b)) else {
        return f64::INFINITY;
    };
    let dot: f64 = va.iter().zip(vb.iter()).map(|(a, b)| a * b).sum();
    let norm_a: f64 = va.iter().map(|x| x * x).sum::<f64>().sqrt();
    let norm_b: f64 = vb.iter().map(|x| x * x).sum::<f64>().sqrt();
    if norm_a == 0.0 || norm_b == 0.0 {
        return f64::INFINITY;
    }
    1.0 - dot / (norm_a * norm_b)
}

fn select_extractor_clusters(_store: &Store, n: usize) -> Vec<Vec<String>> {
    let embedding = load_spectral_embedding();
    let semantic_keys: Vec<&String> = embedding.keys().collect();

    let cluster_size = 5;
    let mut used = HashSet::new();
    let mut clusters = Vec::new();

    for _ in 0..n {
        let available: Vec<&&String> = semantic_keys.iter()
            .filter(|k| !used.contains(**k))
            .collect();
        if available.len() < cluster_size { break; }

        let seed = available[0];
        let mut distances: Vec<(f64, &String)> = available.iter()
            .filter(|k| ***k != *seed)
            .map(|k| (spectral_distance(&embedding, seed, k), **k))
            .filter(|(d, _)| d.is_finite())
            .collect();
        distances.sort_by(|a, b| a.0.total_cmp(&b.0));

        let cluster: Vec<String> = std::iter::once((*seed).clone())
            .chain(distances.iter().take(cluster_size - 1).map(|(_, k)| (*k).clone()))
            .collect();
        for k in &cluster { used.insert(k.clone()); }
        clusters.push(cluster);
    }
    clusters
}

pub fn run_extractor(store: &Store, graph: &Graph, batch_size: usize) -> Result<String, String> {
    let template = load_prompt("extractor")?;
    let topology = get_graph_topology(store, graph);
    let clusters = select_extractor_clusters(store, batch_size);

    let mut results = Vec::new();
    for (i, cluster) in clusters.iter().enumerate() {
        eprintln!("  Extractor cluster {}/{}: {} nodes", i + 1, clusters.len(), cluster.len());

        let node_texts: Vec<String> = cluster.iter()
            .filter_map(|key| {
                let content = store.nodes.get(key)?.content.as_str();
                Some(format!("### {}\n{}", key, content))
            })
            .collect();
        if node_texts.is_empty() { continue; }

        let prompt = template
            .replace("{{TOPOLOGY}}", &topology)
            .replace("{{NODES}}", &node_texts.join("\n\n"));

        let response = llm::call_sonnet("knowledge", &prompt)?;
        results.push(format!("## Cluster {}: {}...\n\n{}", i + 1,
            cluster.iter().take(3).cloned().collect::<Vec<_>>().join(", "), response));
    }
    Ok(results.join("\n\n---\n\n"))
}

fn select_connector_pairs(store: &Store, graph: &Graph, n: usize) -> Vec<(Vec<String>, Vec<String>)> {
    let embedding = load_spectral_embedding();
    let semantic_keys: Vec<&String> = embedding.keys().collect();

    let mut pairs = Vec::new();
    let mut used = HashSet::new();

    for seed in semantic_keys.iter().take(n * 10) {
        if used.contains(*seed) { continue; }

        let mut near: Vec<(f64, &String)> = semantic_keys.iter()
            .filter(|k| ***k != **seed && !used.contains(**k))
            .map(|k| (spectral_distance(&embedding, seed, k), *k))
            .filter(|(d, _)| *d < 0.5 && d.is_finite())
            .collect();
        near.sort_by(|a, b| a.0.total_cmp(&b.0));

        for (_, target) in near.iter().take(5) {
            if !has_edge(store, seed, target) {
                let _ = graph; // graph available for future use
                used.insert((*seed).clone());
                used.insert((*target).clone());
                pairs.push((vec![(*seed).clone()], vec![(*target).clone()]));
                break;
            }
        }
        if pairs.len() >= n { break; }
    }
    pairs
}

pub fn run_connector(store: &Store, graph: &Graph, batch_size: usize) -> Result<String, String> {
    let template = load_prompt("connector")?;
    let topology = get_graph_topology(store, graph);
    let pairs = select_connector_pairs(store, graph, batch_size);

    let mut results = Vec::new();
    for (i, (group_a, group_b)) in pairs.iter().enumerate() {
        eprintln!("  Connector pair {}/{}", i + 1, pairs.len());

        let nodes_a: Vec<String> = group_a.iter()
            .filter_map(|k| {
                let c = store.nodes.get(k)?.content.as_str();
                Some(format!("### {}\n{}", k, c))
            })
            .collect();
        let nodes_b: Vec<String> = group_b.iter()
            .filter_map(|k| {
                let c = store.nodes.get(k)?.content.as_str();
                Some(format!("### {}\n{}", k, c))
            })
            .collect();

        let prompt = template
            .replace("{{TOPOLOGY}}", &topology)
            .replace("{{NODES_A}}", &nodes_a.join("\n\n"))
            .replace("{{NODES_B}}", &nodes_b.join("\n\n"));

        let response = llm::call_sonnet("knowledge", &prompt)?;
        results.push(format!("## Pair {}: {} ↔ {}\n\n{}",
            i + 1, group_a.join(", "), group_b.join(", "), response));
    }
    Ok(results.join("\n\n---\n\n"))
}

pub fn run_challenger(store: &Store, graph: &Graph, batch_size: usize) -> Result<String, String> {
    let template = load_prompt("challenger")?;
    let topology = get_graph_topology(store, graph);

    let mut candidates: Vec<(&String, usize)> = store.nodes.keys()
        .map(|k| (k, graph.degree(k)))
        .collect();
    candidates.sort_by(|a, b| b.1.cmp(&a.1));

    let mut results = Vec::new();
    for (i, (key, _)) in candidates.iter().take(batch_size).enumerate() {
        eprintln!("  Challenger {}/{}: {}", i + 1, batch_size.min(candidates.len()), key);

        let content = match store.nodes.get(key.as_str()) {
            Some(n) => &n.content,
            None => continue,
        };

        let prompt = template
            .replace("{{TOPOLOGY}}", &topology)
            .replace("{{NODE_KEY}}", key)
            .replace("{{NODE_CONTENT}}", content);

        let response = llm::call_sonnet("knowledge", &prompt)?;
        results.push(format!("## Challenge: {}\n\n{}", key, response));
    }
    Ok(results.join("\n\n---\n\n"))
}

// ---------------------------------------------------------------------------
// Convergence metrics
// ---------------------------------------------------------------------------

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CycleResult {
    pub cycle: usize,
    pub timestamp: String,
    pub total_actions: usize,
    pub total_applied: usize,
    pub total_no_ops: usize,
    pub depth_rejected: usize,
    pub weighted_delta: f64,
    pub graph_metrics_before: GraphMetrics,
    pub graph_metrics_after: GraphMetrics,
}

#[derive(Debug, Clone, Default, Serialize, Deserialize)]
pub struct GraphMetrics {
    pub nodes: usize,
    pub edges: usize,
    pub cc: f64,
    pub sigma: f64,
    pub communities: usize,
}

impl GraphMetrics {
    pub fn from_graph(store: &Store, graph: &Graph) -> Self {
        Self {
            nodes: store.nodes.len(),
            edges: graph.edge_count(),
            cc: graph.avg_clustering_coefficient() as f64,
            sigma: graph.small_world_sigma() as f64,
            communities: graph.community_count(),
        }
    }
}

fn metric_stability(history: &[CycleResult], key: &str, window: usize) -> f64 {
    if history.len() < window { return f64::INFINITY; }

    let values: Vec<f64> = history[history.len() - window..].iter()
        .map(|h| match key {
            "sigma" => h.graph_metrics_after.sigma,
            "cc" => h.graph_metrics_after.cc,
            "communities" => h.graph_metrics_after.communities as f64,
            _ => 0.0,
        })
        .collect();

    if values.len() < 2 { return f64::INFINITY; }
    let mean = values.iter().sum::<f64>() / values.len() as f64;
    if mean == 0.0 { return 0.0; }
    let variance = values.iter().map(|v| (v - mean).powi(2)).sum::<f64>() / values.len() as f64;
    variance.sqrt() / mean.abs()
}

pub fn check_convergence(history: &[CycleResult], window: usize) -> bool {
    if history.len() < window { return false; }

    let sigma_cv = metric_stability(history, "sigma", window);
    let cc_cv = metric_stability(history, "cc", window);
    let comm_cv = metric_stability(history, "communities", window);

    let recent = &history[history.len() - window..];
    let avg_delta = recent.iter().map(|r| r.weighted_delta).sum::<f64>() / recent.len() as f64;

    eprintln!("\n  Convergence check (last {} cycles):", window);
    eprintln!("    sigma CV:      {:.4}  (< 0.05?)", sigma_cv);
    eprintln!("    CC CV:         {:.4}  (< 0.05?)", cc_cv);
    eprintln!("    community CV:  {:.4}  (< 0.10?)", comm_cv);
    eprintln!("    avg delta:     {:.2}  (< 1.00?)", avg_delta);

    let structural = sigma_cv < 0.05 && cc_cv < 0.05 && comm_cv < 0.10;
    let behavioral = avg_delta < 1.0;

    if structural && behavioral {
        eprintln!("    → CONVERGED");
        true
    } else {
        false
    }
}

// ---------------------------------------------------------------------------
// The knowledge loop
// ---------------------------------------------------------------------------

pub struct KnowledgeLoopConfig {
    pub max_cycles: usize,
    pub batch_size: usize,
    pub window: usize,
    pub max_depth: i32,
    pub confidence_base: f64,
}

impl Default for KnowledgeLoopConfig {
    fn default() -> Self {
        Self {
            max_cycles: 20,
            batch_size: 5,
            window: 5,
            max_depth: 4,
            confidence_base: 0.3,
        }
    }
}

pub fn run_knowledge_loop(config: &KnowledgeLoopConfig) -> Result<Vec<CycleResult>, String> {
    let mut store = Store::load()?;
    let mut depth_db = DepthDb::load(&store);
    let mut history = Vec::new();

    eprintln!("Knowledge Loop — fixed-point iteration");
    eprintln!("  max_cycles={}  batch_size={}", config.max_cycles, config.batch_size);
    eprintln!("  window={}  max_depth={}", config.window, config.max_depth);

    for cycle in 1..=config.max_cycles {
        let result = run_cycle(cycle, config, &mut depth_db)?;
        history.push(result);

        if check_convergence(&history, config.window) {
            eprintln!("\n  CONVERGED after {} cycles", cycle);
            break;
        }
    }

    // Save loop summary as a store node
    if let Some(first) = history.first() {
        let key = format!("_knowledge-loop-{}", first.timestamp);
        if let Ok(json) = serde_json::to_string_pretty(&history) {
            store = Store::load()?;
            store.upsert_provenance(&key, &json,
                store::Provenance::AgentKnowledgeObservation).ok();
            depth_db.save(&mut store);
            store.save()?;
        }
    }

    Ok(history)
}

fn run_cycle(
    cycle_num: usize,
    config: &KnowledgeLoopConfig,
    depth_db: &mut DepthDb,
) -> Result<CycleResult, String> {
    let timestamp = chrono::Local::now().format("%Y%m%dT%H%M%S").to_string();
    eprintln!("\n{}", "=".repeat(60));
    eprintln!("CYCLE {} — {}", cycle_num, timestamp);
    eprintln!("{}", "=".repeat(60));

    let mut store = Store::load()?;
    let graph = store.build_graph();
    let metrics_before = GraphMetrics::from_graph(&store, &graph);
    eprintln!("  Before: nodes={} edges={} cc={:.3} sigma={:.3}",
        metrics_before.nodes, metrics_before.edges, metrics_before.cc, metrics_before.sigma);

    let mut all_actions = Vec::new();
    let mut all_no_ops = 0;
    let mut depth_rejected = 0;
    let mut total_applied = 0;

    // Run each agent, rebuilding graph after mutations
    let agent_names = ["observation", "extractor", "connector", "challenger"];

    for agent_name in &agent_names {
        eprintln!("\n  --- {} (n={}) ---", agent_name, config.batch_size);

        // Rebuild graph to reflect any mutations from previous agents
        let graph = store.build_graph();

        let output = match *agent_name {
            "observation" => run_observation_extractor(&store, &graph, config.batch_size),
            "extractor" => run_extractor(&store, &graph, config.batch_size),
            "connector" => run_connector(&store, &graph, config.batch_size),
            "challenger" => run_challenger(&store, &graph, config.batch_size),
            _ => unreachable!(),
        };

        let output = match output {
            Ok(o) => o,
            Err(e) => {
                eprintln!("    ERROR: {}", e);
                continue;
            }
        };

        // Store raw output as a node (for debugging/audit)
        let raw_key = format!("_knowledge-{}-{}", agent_name, timestamp);
        let raw_content = format!("# {} Agent Results — {}\n\n{}", agent_name, timestamp, output);
        store.upsert_provenance(&raw_key, &raw_content,
            agent_provenance(agent_name)).ok();

        let mut actions = parse_all_actions(&output);
        let no_ops = count_no_ops(&output);
        all_no_ops += no_ops;

        eprintln!("    Actions: {}  No-ops: {}", actions.len(), no_ops);

        let mut applied = 0;
        for action in &mut actions {
            let depth = compute_action_depth(depth_db, action, agent_name);
            action.depth = depth;

            match &action.kind {
                ActionKind::WriteNode { key, covers, .. } => {
                    let conf_val = action.confidence.value();
                    let req = required_confidence(depth, config.confidence_base);

                    let source_uses: Vec<u32> = covers.iter()
                        .filter_map(|k| store.nodes.get(k).map(|n| n.uses))
                        .collect();
                    let avg_uses = if source_uses.is_empty() { 0 }
                        else { source_uses.iter().sum::<u32>() / source_uses.len() as u32 };
                    let eff_conf = (conf_val + use_bonus(avg_uses)).min(1.0);

                    if eff_conf < req {
                        action.applied = Some(false);
                        action.rejected_reason = Some("depth_threshold".into());
                        depth_rejected += 1;
                        continue;
                    }
                    if depth > config.max_depth {
                        action.applied = Some(false);
                        action.rejected_reason = Some("max_depth".into());
                        depth_rejected += 1;
                        continue;
                    }
                    eprintln!("      WRITE {} depth={} conf={:.2} eff={:.2} req={:.2}",
                        key, depth, conf_val, eff_conf, req);
                }
                ActionKind::Link { source, target } => {
                    eprintln!("      LINK {} → {}", source, target);
                }
                ActionKind::Refine { key, .. } => {
                    eprintln!("      REFINE {} depth={}", key, depth);
                }
            }

            if apply_action(&mut store, action, agent_name, &timestamp, depth) {
                applied += 1;
                action.applied = Some(true);
                if let ActionKind::WriteNode { key, .. } | ActionKind::Refine { key, .. } = &action.kind {
                    depth_db.set(key.clone(), depth);
                }
            } else {
                action.applied = Some(false);
            }
        }

        eprintln!("    Applied: {}/{}", applied, actions.len());
        total_applied += applied;
        all_actions.extend(actions);
    }

    depth_db.save(&mut store);

    // Recompute spectral if anything changed
    if total_applied > 0 {
        eprintln!("\n  Recomputing spectral embedding...");
        let graph = store.build_graph();
        let result = spectral::decompose(&graph, 8);
        let emb = spectral::to_embedding(&result);
        spectral::save_embedding(&emb).ok();
    }

    let graph = store.build_graph();
    let metrics_after = GraphMetrics::from_graph(&store, &graph);
    let weighted_delta: f64 = all_actions.iter()
        .filter(|a| a.applied == Some(true))
        .map(|a| a.weight)
        .sum();

    eprintln!("\n  CYCLE {} SUMMARY", cycle_num);
    eprintln!("    Applied: {}/{}  depth-rejected: {}  no-ops: {}",
        total_applied, all_actions.len(), depth_rejected, all_no_ops);
    eprintln!("    Weighted delta: {:.2}", weighted_delta);

    Ok(CycleResult {
        cycle: cycle_num,
        timestamp,
        total_actions: all_actions.len(),
        total_applied,
        total_no_ops: all_no_ops,
        depth_rejected,
        weighted_delta,
        graph_metrics_before: metrics_before,
        graph_metrics_after: metrics_after,
    })
}