hermes-agent/agent/insights.py

"""
Session Insights Engine for Hermes Agent.

Analyzes historical session data from the SQLite state database to produce
comprehensive usage insights — token consumption, cost estimates, tool usage
patterns, activity trends, model/platform breakdowns, and session metrics.

Inspired by Claude Code's /insights command, adapted for Hermes Agent's
multi-platform architecture with additional cost estimation and platform
breakdown capabilities.

Usage:
    from agent.insights import InsightsEngine
    engine = InsightsEngine(db)
    report = engine.generate(days=30)
    print(engine.format_terminal(report))
"""

import json
import time
from collections import Counter, defaultdict
from datetime import datetime
from typing import Any, Dict, List, Optional

# =========================================================================
# Model pricing (USD per million tokens) — approximate as of early 2026
# =========================================================================
MODEL_PRICING = {
    # OpenAI
    "gpt-4o": {"input": 2.50, "output": 10.00},
    "gpt-4o-mini": {"input": 0.15, "output": 0.60},
    "gpt-4.1": {"input": 2.00, "output": 8.00},
    "gpt-4.1-mini": {"input": 0.40, "output": 1.60},
    "gpt-4.1-nano": {"input": 0.10, "output": 0.40},
    "gpt-4.5-preview": {"input": 75.00, "output": 150.00},
    "gpt-5": {"input": 10.00, "output": 30.00},
    "gpt-5.4": {"input": 10.00, "output": 30.00},
    "o3": {"input": 10.00, "output": 40.00},
    "o3-mini": {"input": 1.10, "output": 4.40},
    "o4-mini": {"input": 1.10, "output": 4.40},
    # Anthropic
    "claude-opus-4-20250514": {"input": 15.00, "output": 75.00},
    "claude-sonnet-4-20250514": {"input": 3.00, "output": 15.00},
    "claude-3-5-sonnet-20241022": {"input": 3.00, "output": 15.00},
    "claude-3-5-haiku-20241022": {"input": 0.80, "output": 4.00},
    "claude-3-opus-20240229": {"input": 15.00, "output": 75.00},
    "claude-3-haiku-20240307": {"input": 0.25, "output": 1.25},
    # DeepSeek
    "deepseek-chat": {"input": 0.14, "output": 0.28},
    "deepseek-reasoner": {"input": 0.55, "output": 2.19},
    # Google
    "gemini-2.5-pro": {"input": 1.25, "output": 10.00},
    "gemini-2.5-flash": {"input": 0.15, "output": 0.60},
    "gemini-2.0-flash": {"input": 0.10, "output": 0.40},
    # Meta (via providers)
    "llama-4-maverick": {"input": 0.50, "output": 0.70},
    "llama-4-scout": {"input": 0.20, "output": 0.30},
    # Z.AI / GLM (direct provider — pricing not published externally, treat as local)
    "glm-5": {"input": 0.0, "output": 0.0},
    "glm-4.7": {"input": 0.0, "output": 0.0},
    "glm-4.5": {"input": 0.0, "output": 0.0},
    "glm-4.5-flash": {"input": 0.0, "output": 0.0},
    # Kimi / Moonshot (direct provider — pricing not published externally, treat as local)
    "kimi-k2.5": {"input": 0.0, "output": 0.0},
    "kimi-k2-thinking": {"input": 0.0, "output": 0.0},
    "kimi-k2-turbo-preview": {"input": 0.0, "output": 0.0},
    "kimi-k2-0905-preview": {"input": 0.0, "output": 0.0},
    # MiniMax (direct provider — pricing not published externally, treat as local)
    "MiniMax-M2.5": {"input": 0.0, "output": 0.0},
    "MiniMax-M2.5-highspeed": {"input": 0.0, "output": 0.0},
    "MiniMax-M2.1": {"input": 0.0, "output": 0.0},
}

# Fallback: unknown/custom models get zero cost (we can't assume pricing
# for self-hosted models, custom OAI endpoints, local inference, etc.)
_DEFAULT_PRICING = {"input": 0.0, "output": 0.0}


def _has_known_pricing(model_name: str) -> bool:
    """Check if a model has known pricing (vs unknown/custom endpoint)."""
    return _get_pricing(model_name) is not _DEFAULT_PRICING


def _get_pricing(model_name: str) -> Dict[str, float]:
    """Look up pricing for a model. Uses fuzzy matching on model name.

    Returns _DEFAULT_PRICING (zero cost) for unknown/custom models —
    we can't assume costs for self-hosted endpoints, local inference, etc.
    """
    if not model_name:
        return _DEFAULT_PRICING

    # Strip provider prefix (e.g., "anthropic/claude-..." -> "claude-...")
    bare = model_name.split("/")[-1].lower()

    # Exact match first
    if bare in MODEL_PRICING:
        return MODEL_PRICING[bare]

    # Fuzzy prefix match — prefer the LONGEST matching key to avoid
    # e.g. "gpt-4o" matching before "gpt-4o-mini" for "gpt-4o-mini-2024-07-18"
    best_match = None
    best_len = 0
    for key, price in MODEL_PRICING.items():
        if bare.startswith(key) and len(key) > best_len:
            best_match = price
            best_len = len(key)
    if best_match:
        return best_match

    # Keyword heuristics (checked in most-specific-first order)
    if "opus" in bare:
        return {"input": 15.00, "output": 75.00}
    if "sonnet" in bare:
        return {"input": 3.00, "output": 15.00}
    if "haiku" in bare:
        return {"input": 0.80, "output": 4.00}
    if "gpt-4o-mini" in bare:
        return {"input": 0.15, "output": 0.60}
    if "gpt-4o" in bare:
        return {"input": 2.50, "output": 10.00}
    if "gpt-5" in bare:
        return {"input": 10.00, "output": 30.00}
    if "deepseek" in bare:
        return {"input": 0.14, "output": 0.28}
    if "gemini" in bare:
        return {"input": 0.15, "output": 0.60}

    return _DEFAULT_PRICING


def _estimate_cost(model: str, input_tokens: int, output_tokens: int) -> float:
    """Estimate the USD cost for a given model and token counts."""
    pricing = _get_pricing(model)
    return (input_tokens * pricing["input"] + output_tokens * pricing["output"]) / 1_000_000


def _format_duration(seconds: float) -> str:
    """Format seconds into a human-readable duration string."""
    if seconds < 60:
        return f"{seconds:.0f}s"
    minutes = seconds / 60
    if minutes < 60:
        return f"{minutes:.0f}m"
    hours = minutes / 60
    if hours < 24:
        remaining_min = int(minutes % 60)
        return f"{int(hours)}h {remaining_min}m" if remaining_min else f"{int(hours)}h"
    days = hours / 24
    return f"{days:.1f}d"


def _bar_chart(values: List[int], max_width: int = 20) -> List[str]:
    """Create simple horizontal bar chart strings from values."""
    peak = max(values) if values else 1
    if peak == 0:
        return ["" for _ in values]
    return ["█" * max(1, int(v / peak * max_width)) if v > 0 else "" for v in values]


class InsightsEngine:
    """
    Analyzes session history and produces usage insights.

    Works directly with a SessionDB instance (or raw sqlite3 connection)
    to query session and message data.
    """

    def __init__(self, db):
        """
        Initialize with a SessionDB instance.

        Args:
            db: A SessionDB instance (from hermes_state.py)
        """
        self.db = db
        self._conn = db._conn

    def generate(self, days: int = 30, source: str = None) -> Dict[str, Any]:
        """
        Generate a complete insights report.

        Args:
            days: Number of days to look back (default: 30)
            source: Optional filter by source platform

        Returns:
            Dict with all computed insights
        """
        cutoff = time.time() - (days * 86400)

        # Gather raw data
        sessions = self._get_sessions(cutoff, source)
        tool_usage = self._get_tool_usage(cutoff, source)
        message_stats = self._get_message_stats(cutoff, source)

        if not sessions:
            return {
                "days": days,
                "source_filter": source,
                "empty": True,
                "overview": {},
                "models": [],
                "platforms": [],
                "tools": [],
                "activity": {},
                "top_sessions": [],
            }

        # Compute insights
        overview = self._compute_overview(sessions, message_stats)
        models = self._compute_model_breakdown(sessions)
        platforms = self._compute_platform_breakdown(sessions)
        tools = self._compute_tool_breakdown(tool_usage)
        activity = self._compute_activity_patterns(sessions)
        top_sessions = self._compute_top_sessions(sessions)

        return {
            "days": days,
            "source_filter": source,
            "empty": False,
            "generated_at": time.time(),
            "overview": overview,
            "models": models,
            "platforms": platforms,
            "tools": tools,
            "activity": activity,
            "top_sessions": top_sessions,
        }

    # =========================================================================
    # Data gathering (SQL queries)
    # =========================================================================

    # Columns we actually need (skip system_prompt, model_config blobs)
    _SESSION_COLS = ("id, source, model, started_at, ended_at, "
                     "message_count, tool_call_count, input_tokens, output_tokens")

    def _get_sessions(self, cutoff: float, source: str = None) -> List[Dict]:
        """Fetch sessions within the time window."""
        if source:
            cursor = self._conn.execute(
                f"""SELECT {self._SESSION_COLS} FROM sessions
                    WHERE started_at >= ? AND source = ?
                    ORDER BY started_at DESC""",
                (cutoff, source),
            )
        else:
            cursor = self._conn.execute(
                f"""SELECT {self._SESSION_COLS} FROM sessions
                    WHERE started_at >= ?
                    ORDER BY started_at DESC""",
                (cutoff,),
            )
        return [dict(row) for row in cursor.fetchall()]

    def _get_tool_usage(self, cutoff: float, source: str = None) -> List[Dict]:
        """Get tool call counts from messages.

        Uses two sources:
        1. tool_name column on 'tool' role messages (set by gateway)
        2. tool_calls JSON on 'assistant' role messages (covers CLI where
           tool_name is not populated on tool responses)
        """
        tool_counts = Counter()

        # Source 1: explicit tool_name on tool response messages
        if source:
            cursor = self._conn.execute(
                """SELECT m.tool_name, COUNT(*) as count
                   FROM messages m
                   JOIN sessions s ON s.id = m.session_id
                   WHERE s.started_at >= ? AND s.source = ?
                     AND m.role = 'tool' AND m.tool_name IS NOT NULL
                   GROUP BY m.tool_name
                   ORDER BY count DESC""",
                (cutoff, source),
            )
        else:
            cursor = self._conn.execute(
                """SELECT m.tool_name, COUNT(*) as count
                   FROM messages m
                   JOIN sessions s ON s.id = m.session_id
                   WHERE s.started_at >= ?
                     AND m.role = 'tool' AND m.tool_name IS NOT NULL
                   GROUP BY m.tool_name
                   ORDER BY count DESC""",
                (cutoff,),
            )
        for row in cursor.fetchall():
            tool_counts[row["tool_name"]] += row["count"]

        # Source 2: extract from tool_calls JSON on assistant messages
        # (covers CLI sessions where tool_name is NULL on tool responses)
        if source:
            cursor2 = self._conn.execute(
                """SELECT m.tool_calls
                   FROM messages m
                   JOIN sessions s ON s.id = m.session_id
                   WHERE s.started_at >= ? AND s.source = ?
                     AND m.role = 'assistant' AND m.tool_calls IS NOT NULL""",
                (cutoff, source),
            )
        else:
            cursor2 = self._conn.execute(
                """SELECT m.tool_calls
                   FROM messages m
                   JOIN sessions s ON s.id = m.session_id
                   WHERE s.started_at >= ?
                     AND m.role = 'assistant' AND m.tool_calls IS NOT NULL""",
                (cutoff,),
            )

        tool_calls_counts = Counter()
        for row in cursor2.fetchall():
            try:
                calls = row["tool_calls"]
                if isinstance(calls, str):
                    calls = json.loads(calls)
                if isinstance(calls, list):
                    for call in calls:
                        func = call.get("function", {}) if isinstance(call, dict) else {}
                        name = func.get("name")
                        if name:
                            tool_calls_counts[name] += 1
            except (json.JSONDecodeError, TypeError, AttributeError):
                continue

        # Merge: prefer tool_name source, supplement with tool_calls source
        # for tools not already counted
        if not tool_counts and tool_calls_counts:
            # No tool_name data at all — use tool_calls exclusively
            tool_counts = tool_calls_counts
        elif tool_counts and tool_calls_counts:
            # Both sources have data — use whichever has the higher count per tool
            # (they may overlap, so take the max to avoid double-counting)
            all_tools = set(tool_counts) | set(tool_calls_counts)
            merged = Counter()
            for tool in all_tools:
                merged[tool] = max(tool_counts.get(tool, 0), tool_calls_counts.get(tool, 0))
            tool_counts = merged

        # Convert to the expected format
        return [
            {"tool_name": name, "count": count}
            for name, count in tool_counts.most_common()
        ]

    def _get_message_stats(self, cutoff: float, source: str = None) -> Dict:
        """Get aggregate message statistics."""
        if source:
            cursor = self._conn.execute(
                """SELECT
                     COUNT(*) as total_messages,
                     SUM(CASE WHEN m.role = 'user' THEN 1 ELSE 0 END) as user_messages,
                     SUM(CASE WHEN m.role = 'assistant' THEN 1 ELSE 0 END) as assistant_messages,
                     SUM(CASE WHEN m.role = 'tool' THEN 1 ELSE 0 END) as tool_messages
                   FROM messages m
                   JOIN sessions s ON s.id = m.session_id
                   WHERE s.started_at >= ? AND s.source = ?""",
                (cutoff, source),
            )
        else:
            cursor = self._conn.execute(
                """SELECT
                     COUNT(*) as total_messages,
                     SUM(CASE WHEN m.role = 'user' THEN 1 ELSE 0 END) as user_messages,
                     SUM(CASE WHEN m.role = 'assistant' THEN 1 ELSE 0 END) as assistant_messages,
                     SUM(CASE WHEN m.role = 'tool' THEN 1 ELSE 0 END) as tool_messages
                   FROM messages m
                   JOIN sessions s ON s.id = m.session_id
                   WHERE s.started_at >= ?""",
                (cutoff,),
            )
        row = cursor.fetchone()
        return dict(row) if row else {
            "total_messages": 0, "user_messages": 0,
            "assistant_messages": 0, "tool_messages": 0,
        }

    # =========================================================================
    # Computation
    # =========================================================================

    def _compute_overview(self, sessions: List[Dict], message_stats: Dict) -> Dict:
        """Compute high-level overview statistics."""
        total_input = sum(s.get("input_tokens") or 0 for s in sessions)
        total_output = sum(s.get("output_tokens") or 0 for s in sessions)
        total_tokens = total_input + total_output
        total_tool_calls = sum(s.get("tool_call_count") or 0 for s in sessions)
        total_messages = sum(s.get("message_count") or 0 for s in sessions)

        # Cost estimation (weighted by model)
        total_cost = 0.0
        models_with_pricing = set()
        models_without_pricing = set()
        for s in sessions:
            model = s.get("model") or ""
            inp = s.get("input_tokens") or 0
            out = s.get("output_tokens") or 0
            total_cost += _estimate_cost(model, inp, out)
            display = model.split("/")[-1] if "/" in model else (model or "unknown")
            if _has_known_pricing(model):
                models_with_pricing.add(display)
            else:
                models_without_pricing.add(display)

        # Session duration stats (guard against negative durations from clock drift)
        durations = []
        for s in sessions:
            start = s.get("started_at")
            end = s.get("ended_at")
            if start and end and end > start:
                durations.append(end - start)

        total_hours = sum(durations) / 3600 if durations else 0
        avg_duration = sum(durations) / len(durations) if durations else 0

        # Earliest and latest session
        started_timestamps = [s["started_at"] for s in sessions if s.get("started_at")]
        date_range_start = min(started_timestamps) if started_timestamps else None
        date_range_end = max(started_timestamps) if started_timestamps else None

        return {
            "total_sessions": len(sessions),
            "total_messages": total_messages,
            "total_tool_calls": total_tool_calls,
            "total_input_tokens": total_input,
            "total_output_tokens": total_output,
            "total_tokens": total_tokens,
            "estimated_cost": total_cost,
            "total_hours": total_hours,
            "avg_session_duration": avg_duration,
            "avg_messages_per_session": total_messages / len(sessions) if sessions else 0,
            "avg_tokens_per_session": total_tokens / len(sessions) if sessions else 0,
            "user_messages": message_stats.get("user_messages") or 0,
            "assistant_messages": message_stats.get("assistant_messages") or 0,
            "tool_messages": message_stats.get("tool_messages") or 0,
            "date_range_start": date_range_start,
            "date_range_end": date_range_end,
            "models_with_pricing": sorted(models_with_pricing),
            "models_without_pricing": sorted(models_without_pricing),
        }

    def _compute_model_breakdown(self, sessions: List[Dict]) -> List[Dict]:
        """Break down usage by model."""
        model_data = defaultdict(lambda: {
            "sessions": 0, "input_tokens": 0, "output_tokens": 0,
            "total_tokens": 0, "tool_calls": 0, "cost": 0.0,
        })

        for s in sessions:
            model = s.get("model") or "unknown"
            # Normalize: strip provider prefix for display
            display_model = model.split("/")[-1] if "/" in model else model
            d = model_data[display_model]
            d["sessions"] += 1
            inp = s.get("input_tokens") or 0
            out = s.get("output_tokens") or 0
            d["input_tokens"] += inp
            d["output_tokens"] += out
            d["total_tokens"] += inp + out
            d["tool_calls"] += s.get("tool_call_count") or 0
            d["cost"] += _estimate_cost(model, inp, out)
            d["has_pricing"] = _has_known_pricing(model)

        result = [
            {"model": model, **data}
            for model, data in model_data.items()
        ]
        # Sort by tokens first, fall back to session count when tokens are 0
        result.sort(key=lambda x: (x["total_tokens"], x["sessions"]), reverse=True)
        return result

    def _compute_platform_breakdown(self, sessions: List[Dict]) -> List[Dict]:
        """Break down usage by platform/source."""
        platform_data = defaultdict(lambda: {
            "sessions": 0, "messages": 0, "input_tokens": 0,
            "output_tokens": 0, "total_tokens": 0, "tool_calls": 0,
        })

        for s in sessions:
            source = s.get("source") or "unknown"
            d = platform_data[source]
            d["sessions"] += 1
            d["messages"] += s.get("message_count") or 0
            inp = s.get("input_tokens") or 0
            out = s.get("output_tokens") or 0
            d["input_tokens"] += inp
            d["output_tokens"] += out
            d["total_tokens"] += inp + out
            d["tool_calls"] += s.get("tool_call_count") or 0

        result = [
            {"platform": platform, **data}
            for platform, data in platform_data.items()
        ]
        result.sort(key=lambda x: x["sessions"], reverse=True)
        return result

    def _compute_tool_breakdown(self, tool_usage: List[Dict]) -> List[Dict]:
        """Process tool usage data into a ranked list with percentages."""
        total_calls = sum(t["count"] for t in tool_usage) if tool_usage else 0
        result = []
        for t in tool_usage:
            pct = (t["count"] / total_calls * 100) if total_calls else 0
            result.append({
                "tool": t["tool_name"],
                "count": t["count"],
                "percentage": pct,
            })
        return result

    def _compute_activity_patterns(self, sessions: List[Dict]) -> Dict:
        """Analyze activity patterns by day of week and hour."""
        day_counts = Counter()  # 0=Monday ... 6=Sunday
        hour_counts = Counter()
        daily_counts = Counter()  # date string -> count

        for s in sessions:
            ts = s.get("started_at")
            if not ts:
                continue
            dt = datetime.fromtimestamp(ts)
            day_counts[dt.weekday()] += 1
            hour_counts[dt.hour] += 1
            daily_counts[dt.strftime("%Y-%m-%d")] += 1

        day_names = ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"]
        day_breakdown = [
            {"day": day_names[i], "count": day_counts.get(i, 0)}
            for i in range(7)
        ]

        hour_breakdown = [
            {"hour": i, "count": hour_counts.get(i, 0)}
            for i in range(24)
        ]

        # Busiest day and hour
        busiest_day = max(day_breakdown, key=lambda x: x["count"]) if day_breakdown else None
        busiest_hour = max(hour_breakdown, key=lambda x: x["count"]) if hour_breakdown else None

        # Active days (days with at least one session)
        active_days = len(daily_counts)

        # Streak calculation
        if daily_counts:
            all_dates = sorted(daily_counts.keys())
            current_streak = 1
            max_streak = 1
            for i in range(1, len(all_dates)):
                d1 = datetime.strptime(all_dates[i - 1], "%Y-%m-%d")
                d2 = datetime.strptime(all_dates[i], "%Y-%m-%d")
                if (d2 - d1).days == 1:
                    current_streak += 1
                    max_streak = max(max_streak, current_streak)
                else:
                    current_streak = 1
        else:
            max_streak = 0

        return {
            "by_day": day_breakdown,
            "by_hour": hour_breakdown,
            "busiest_day": busiest_day,
            "busiest_hour": busiest_hour,
            "active_days": active_days,
            "max_streak": max_streak,
        }

    def _compute_top_sessions(self, sessions: List[Dict]) -> List[Dict]:
        """Find notable sessions (longest, most messages, most tokens)."""
        top = []

        # Longest by duration
        sessions_with_duration = [
            s for s in sessions
            if s.get("started_at") and s.get("ended_at")
        ]
        if sessions_with_duration:
            longest = max(
                sessions_with_duration,
                key=lambda s: (s["ended_at"] - s["started_at"]),
            )
            dur = longest["ended_at"] - longest["started_at"]
            top.append({
                "label": "Longest session",
                "session_id": longest["id"][:16],
                "value": _format_duration(dur),
                "date": datetime.fromtimestamp(longest["started_at"]).strftime("%b %d"),
            })

        # Most messages
        most_msgs = max(sessions, key=lambda s: s.get("message_count") or 0)
        if (most_msgs.get("message_count") or 0) > 0:
            top.append({
                "label": "Most messages",
                "session_id": most_msgs["id"][:16],
                "value": f"{most_msgs['message_count']} msgs",
                "date": datetime.fromtimestamp(most_msgs["started_at"]).strftime("%b %d") if most_msgs.get("started_at") else "?",
            })

        # Most tokens
        most_tokens = max(
            sessions,
            key=lambda s: (s.get("input_tokens") or 0) + (s.get("output_tokens") or 0),
        )
        token_total = (most_tokens.get("input_tokens") or 0) + (most_tokens.get("output_tokens") or 0)
        if token_total > 0:
            top.append({
                "label": "Most tokens",
                "session_id": most_tokens["id"][:16],
                "value": f"{token_total:,} tokens",
                "date": datetime.fromtimestamp(most_tokens["started_at"]).strftime("%b %d") if most_tokens.get("started_at") else "?",
            })

        # Most tool calls
        most_tools = max(sessions, key=lambda s: s.get("tool_call_count") or 0)
        if (most_tools.get("tool_call_count") or 0) > 0:
            top.append({
                "label": "Most tool calls",
                "session_id": most_tools["id"][:16],
                "value": f"{most_tools['tool_call_count']} calls",
                "date": datetime.fromtimestamp(most_tools["started_at"]).strftime("%b %d") if most_tools.get("started_at") else "?",
            })

        return top

    # =========================================================================
    # Formatting
    # =========================================================================

    def format_terminal(self, report: Dict) -> str:
        """Format the insights report for terminal display (CLI)."""
        if report.get("empty"):
            days = report.get("days", 30)
            src = f" (source: {report['source_filter']})" if report.get("source_filter") else ""
            return f"  No sessions found in the last {days} days{src}."

        lines = []
        o = report["overview"]
        days = report["days"]
        src_filter = report.get("source_filter")

        # Header
        lines.append("")
        lines.append("  ╔══════════════════════════════════════════════════════════╗")
        lines.append("  ║                    📊 Hermes Insights                    ║")
        period_label = f"Last {days} days"
        if src_filter:
            period_label += f" ({src_filter})"
        padding = 58 - len(period_label) - 2
        left_pad = padding // 2
        right_pad = padding - left_pad
        lines.append(f"  ║{' ' * left_pad} {period_label} {' ' * right_pad}║")
        lines.append("  ╚══════════════════════════════════════════════════════════╝")
        lines.append("")

        # Date range
        if o.get("date_range_start") and o.get("date_range_end"):
            start_str = datetime.fromtimestamp(o["date_range_start"]).strftime("%b %d, %Y")
            end_str = datetime.fromtimestamp(o["date_range_end"]).strftime("%b %d, %Y")
            lines.append(f"  Period: {start_str} — {end_str}")
            lines.append("")

        # Overview
        lines.append("  📋 Overview")
        lines.append("  " + "─" * 56)
        lines.append(f"  Sessions:          {o['total_sessions']:<12}  Messages:        {o['total_messages']:,}")
        lines.append(f"  Tool calls:        {o['total_tool_calls']:<12,}  User messages:   {o['user_messages']:,}")
        lines.append(f"  Input tokens:      {o['total_input_tokens']:<12,}  Output tokens:   {o['total_output_tokens']:,}")
        cost_str = f"${o['estimated_cost']:.2f}"
        if o.get("models_without_pricing"):
            cost_str += " *"
        lines.append(f"  Total tokens:      {o['total_tokens']:<12,}  Est. cost:       {cost_str}")
        if o["total_hours"] > 0:
            lines.append(f"  Active time:       ~{_format_duration(o['total_hours'] * 3600):<11}  Avg session:     ~{_format_duration(o['avg_session_duration'])}")
        lines.append(f"  Avg msgs/session:  {o['avg_messages_per_session']:.1f}")
        lines.append("")

        # Model breakdown
        if report["models"]:
            lines.append("  🤖 Models Used")
            lines.append("  " + "─" * 56)
            lines.append(f"  {'Model':<30} {'Sessions':>8} {'Tokens':>12} {'Cost':>8}")
            for m in report["models"]:
                model_name = m["model"][:28]
                if m.get("has_pricing"):
                    cost_cell = f"${m['cost']:>6.2f}"
                else:
                    cost_cell = "     N/A"
                lines.append(f"  {model_name:<30} {m['sessions']:>8} {m['total_tokens']:>12,} {cost_cell}")
            if o.get("models_without_pricing"):
                lines.append(f"  * Cost N/A for custom/self-hosted models")
            lines.append("")

        # Platform breakdown
        if len(report["platforms"]) > 1 or (report["platforms"] and report["platforms"][0]["platform"] != "cli"):
            lines.append("  📱 Platforms")
            lines.append("  " + "─" * 56)
            lines.append(f"  {'Platform':<14} {'Sessions':>8} {'Messages':>10} {'Tokens':>14}")
            for p in report["platforms"]:
                lines.append(f"  {p['platform']:<14} {p['sessions']:>8} {p['messages']:>10,} {p['total_tokens']:>14,}")
            lines.append("")

        # Tool usage
        if report["tools"]:
            lines.append("  🔧 Top Tools")
            lines.append("  " + "─" * 56)
            lines.append(f"  {'Tool':<28} {'Calls':>8} {'%':>8}")
            for t in report["tools"][:15]:  # Top 15
                lines.append(f"  {t['tool']:<28} {t['count']:>8,} {t['percentage']:>7.1f}%")
            if len(report["tools"]) > 15:
                lines.append(f"  ... and {len(report['tools']) - 15} more tools")
            lines.append("")

        # Activity patterns
        act = report.get("activity", {})
        if act.get("by_day"):
            lines.append("  📅 Activity Patterns")
            lines.append("  " + "─" * 56)

            # Day of week chart
            day_values = [d["count"] for d in act["by_day"]]
            bars = _bar_chart(day_values, max_width=15)
            for i, d in enumerate(act["by_day"]):
                bar = bars[i]
                lines.append(f"  {d['day']}  {bar:<15} {d['count']}")

            lines.append("")

            # Peak hours (show top 5 busiest hours)
            busy_hours = sorted(act["by_hour"], key=lambda x: x["count"], reverse=True)
            busy_hours = [h for h in busy_hours if h["count"] > 0][:5]
            if busy_hours:
                hour_strs = []
                for h in busy_hours:
                    hr = h["hour"]
                    ampm = "AM" if hr < 12 else "PM"
                    display_hr = hr % 12 or 12
                    hour_strs.append(f"{display_hr}{ampm} ({h['count']})")
                lines.append(f"  Peak hours: {', '.join(hour_strs)}")

            if act.get("active_days"):
                lines.append(f"  Active days: {act['active_days']}")
            if act.get("max_streak") and act["max_streak"] > 1:
                lines.append(f"  Best streak: {act['max_streak']} consecutive days")
            lines.append("")

        # Notable sessions
        if report.get("top_sessions"):
            lines.append("  🏆 Notable Sessions")
            lines.append("  " + "─" * 56)
            for ts in report["top_sessions"]:
                lines.append(f"  {ts['label']:<20} {ts['value']:<18} ({ts['date']}, {ts['session_id']})")
            lines.append("")

        return "\n".join(lines)

    def format_gateway(self, report: Dict) -> str:
        """Format the insights report for gateway/messaging (shorter)."""
        if report.get("empty"):
            days = report.get("days", 30)
            return f"No sessions found in the last {days} days."

        lines = []
        o = report["overview"]
        days = report["days"]

        lines.append(f"📊 **Hermes Insights** — Last {days} days\n")

        # Overview
        lines.append(f"**Sessions:** {o['total_sessions']} | **Messages:** {o['total_messages']:,} | **Tool calls:** {o['total_tool_calls']:,}")
        lines.append(f"**Tokens:** {o['total_tokens']:,} (in: {o['total_input_tokens']:,} / out: {o['total_output_tokens']:,})")
        cost_note = ""
        if o.get("models_without_pricing"):
            cost_note = " _(excludes custom/self-hosted models)_"
        lines.append(f"**Est. cost:** ${o['estimated_cost']:.2f}{cost_note}")
        if o["total_hours"] > 0:
            lines.append(f"**Active time:** ~{_format_duration(o['total_hours'] * 3600)} | **Avg session:** ~{_format_duration(o['avg_session_duration'])}")
        lines.append("")

        # Models (top 5)
        if report["models"]:
            lines.append("**🤖 Models:**")
            for m in report["models"][:5]:
                cost_str = f"${m['cost']:.2f}" if m.get("has_pricing") else "N/A"
                lines.append(f"  {m['model'][:25]} — {m['sessions']} sessions, {m['total_tokens']:,} tokens, {cost_str}")
            lines.append("")

        # Platforms (if multi-platform)
        if len(report["platforms"]) > 1:
            lines.append("**📱 Platforms:**")
            for p in report["platforms"]:
                lines.append(f"  {p['platform']} — {p['sessions']} sessions, {p['messages']:,} msgs")
            lines.append("")

        # Tools (top 8)
        if report["tools"]:
            lines.append("**🔧 Top Tools:**")
            for t in report["tools"][:8]:
                lines.append(f"  {t['tool']} — {t['count']:,} calls ({t['percentage']:.1f}%)")
            lines.append("")

        # Activity summary
        act = report.get("activity", {})
        if act.get("busiest_day") and act.get("busiest_hour"):
            hr = act["busiest_hour"]["hour"]
            ampm = "AM" if hr < 12 else "PM"
            display_hr = hr % 12 or 12
            lines.append(f"**📅 Busiest:** {act['busiest_day']['day']}s ({act['busiest_day']['count']} sessions), {display_hr}{ampm} ({act['busiest_hour']['count']} sessions)")
            if act.get("active_days"):
                lines.append(f"**Active days:** {act['active_days']}", )
            if act.get("max_streak", 0) > 1:
                lines.append(f"**Best streak:** {act['max_streak']} consecutive days")

        return "\n".join(lines)