"""
Session Insights Engine: aggregates the SQLite state DB into usage insights
(tokens, cost estimates, tool/skill usage, activity, model/platform breakdowns).
engine = InsightsEngine(db)
report = engine.generate(days=30)
print(engine.format_terminal(report))
"""
import json
import sqlite3
import time
from collections import Counter, defaultdict
from datetime import datetime
from decimal import Decimal
from typing import Any, Dict, List, Optional
from agent.usage_pricing import (
CanonicalUsage,
estimate_usage_cost,
format_cost_label,
format_duration_compact,
has_known_pricing,
)
_TOKEN_KEYS = ("input_tokens", "output_tokens", "cache_read_tokens", "cache_write_tokens")
_SKILL_TOOLS = {"skill_view", "skill_manage"}
def _fmt_est_cost(est_cost: float) -> str:
"""Aggregate cost via the shared label helper so sub-cent totals render at 4dp, not "~$0.00"."""
return format_cost_label(Decimal(str(est_cost)))
def _estimate_cost(
session_or_model: Dict[str, Any] | str,
input_tokens: int = 0,
output_tokens: int = 0,
*,
cache_read_tokens: int = 0,
cache_write_tokens: int = 0,
provider: Optional[str] = None,
base_url: Optional[str] = None,
) -> tuple[float, str]:
"""Estimate the USD cost for a session row or a model/token tuple."""
if isinstance(session_or_model, dict):
s = session_or_model
model = s.get("model") or ""
usage = CanonicalUsage(**{k: s.get(k) or 0 for k in _TOKEN_KEYS})
provider, base_url = s.get("billing_provider"), s.get("billing_base_url")
else:
model = session_or_model or ""
usage = CanonicalUsage(
input_tokens=input_tokens,
output_tokens=output_tokens,
cache_read_tokens=cache_read_tokens,
cache_write_tokens=cache_write_tokens,
)
result = estimate_usage_cost(model, usage, provider=provider, base_url=base_url)
return float(result.amount_usd or 0.0), result.status
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]
def _short_model(model: Optional[str]) -> str:
"""Display name: strip the provider prefix; empty → "unknown"."""
return (model or "unknown").split("/")[-1]
def _parse_calls(raw: Any) -> Optional[list]:
"""tool_calls column → list, or None when not decodable as a JSON list."""
try:
if isinstance(raw, str):
raw = json.loads(raw)
except (json.JSONDecodeError, TypeError):
return None
return raw if isinstance(raw, list) else None
def _hour12(hr: int) -> str:
return f"{hr % 12 or 12}{'AM' if hr < 12 else 'PM'}"
def _day(ts: Any) -> str:
return datetime.fromtimestamp(ts).strftime("%b %d") if ts else "?"
def _scoped(before: str, after: str = "", *, src: str = " AND s.source = ?") -> tuple[str, str]:
"""(unfiltered, source-filtered) query pair sharing one body.
Built once at class definition, so no runtime value can alter query structure.
"""
return before + after, before + src + after
class InsightsEngine:
"""Analyzes session history from a SessionDB (or raw sqlite3 connection)."""
_SESSION_COLS = ("id, source, model, started_at, ended_at, "
"message_count, tool_call_count, input_tokens, output_tokens, "
"cache_read_tokens, cache_write_tokens, billing_provider, "
"billing_base_url, billing_mode, estimated_cost_usd, "
"actual_cost_usd, cost_status, cost_source, api_call_count")
_GET_SESSIONS_ALL, _GET_SESSIONS_WITH_SOURCE = _scoped(
f"SELECT {_SESSION_COLS} FROM sessions WHERE started_at >= ?",
" ORDER BY started_at DESC",
src=" AND source = ?",
)
# ``INDEXED BY`` pins the partial index so the plan is deterministic on a
# fresh state.db (before ANALYZE) for both branches; without it the
# source-filtered probe falls back to idx_messages_session_active and scans
# each session's non-tool-call rows. The pin is a HARD dependency (SQLite
# raises ``no such index``): read-only opens skip ``_init_schema``, so an
# older writer's DB may lack it — ``__init__`` probes once and falls back
# to the unpinned variants (identical rows, optimizer-chosen plan).
_MESSAGES_ASSISTANT_CALLS_INDEX = "idx_messages_assistant_calls_by_session"
_ASSISTANT_CALLS = (
f" FROM messages m INDEXED BY {_MESSAGES_ASSISTANT_CALLS_INDEX}"
" JOIN sessions s ON s.id = m.session_id"
" WHERE s.started_at >= ?"
)
_GET_TOOL_CALLS_ALL, _GET_TOOL_CALLS_WITH_SOURCE = _scoped(
"SELECT m.tool_calls" + _ASSISTANT_CALLS,
" AND m.role = 'assistant' AND m.tool_calls IS NOT NULL",
)
_GET_SKILL_CALLS_ALL, _GET_SKILL_CALLS_WITH_SOURCE = _scoped(
"SELECT m.tool_calls, m.timestamp" + _ASSISTANT_CALLS,
" AND m.role = 'assistant' AND m.tool_calls IS NOT NULL"
" AND (instr(m.tool_calls, 'skill_view') > 0"
" OR instr(m.tool_calls, 'skill_manage') > 0)",
)
_GET_TOOL_NAMES_ALL, _GET_TOOL_NAMES_WITH_SOURCE = _scoped(
"""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""",
)
_GET_MESSAGE_STATS_ALL, _GET_MESSAGE_STATS_WITH_SOURCE = _scoped(
"""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 >= ?""",
)
_GET_MODEL_USAGE_ALL, _GET_MODEL_USAGE_WITH_SOURCE = _scoped(
"SELECT u.session_id, u.model, u.billing_provider, u.billing_base_url,"
" u.api_call_count, u.input_tokens, u.output_tokens,"
" u.cache_read_tokens, u.cache_write_tokens, u.reasoning_tokens,"
" u.estimated_cost_usd, u.actual_cost_usd, u.cost_status,"
" u.cost_source, u.billing_mode"
" FROM session_model_usage u"
" JOIN sessions s ON s.id = u.session_id"
" WHERE s.started_at >= ?",
)
_PINNED = ("_GET_TOOL_CALLS", "_GET_SKILL_CALLS")
def __init__(self, db):
self.db = db
self._conn = db._conn
try:
self._has_assistant_calls_index = bool(
self._conn.execute(
"SELECT 1 FROM sqlite_master WHERE type='index' AND name=?",
(self._MESSAGES_ASSISTANT_CALLS_INDEX,),
).fetchone()
)
except sqlite3.Error:
self._has_assistant_calls_index = False
if not self._has_assistant_calls_index:
strip = f" INDEXED BY {self._MESSAGES_ASSISTANT_CALLS_INDEX}"
for base in self._PINNED:
for suffix in ("_ALL", "_WITH_SOURCE"):
setattr(self, base + suffix, getattr(self, base + suffix).replace(strip, ""))
def _query(self, base: str, cutoff: float, source: Optional[str]):
"""Run ``_WITH_SOURCE`` or ``_ALL`` (instance attrs, so the
unpinned fallback applies) and return the cursor."""
if source:
return self._conn.execute(getattr(self, base + "_WITH_SOURCE"), (cutoff, source))
return self._conn.execute(getattr(self, base + "_ALL"), (cutoff,))
def generate(self, days: int = 30, source: str = None) -> Dict[str, Any]:
"""Generate a complete insights report for the last ``days`` days,
optionally filtered by source platform."""
cutoff = time.time() - (days * 86400)
# Drain the SessionDB's async accounting queue so counters are exact
# (self.db may be a raw sqlite3 connection in tests — guard).
flush = getattr(self.db, "flush_token_counts", None)
if callable(flush):
flush()
sessions = self._get_sessions(cutoff, source)
tool_usage = self._get_tool_usage(cutoff, source)
skill_usage = self._get_skill_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": [],
"skills": self._compute_skill_breakdown([]),
"activity": {},
"top_sessions": [],
}
models = self._compute_model_breakdown(sessions, cutoff, source)
return {
"days": days,
"source_filter": source,
"empty": False,
"generated_at": time.time(),
"overview": self._compute_overview(sessions, message_stats, models),
"models": models,
"platforms": self._compute_platform_breakdown(sessions),
"tools": self._compute_tool_breakdown(tool_usage),
"skills": self._compute_skill_breakdown(skill_usage),
"activity": self._compute_activity_patterns(sessions),
"top_sessions": self._compute_top_sessions(sessions),
}
def get_usage_breakdown(self, days: int = 30, source: str = None) -> Dict[str, Any]:
"""Analytics-usage payload (tools + skills) without a full generate().
Uses the instr()-prefiltered skill query so only skill_view/skill_manage
messages are loaded, while keeping the per-tool breakdown the dashboard uses.
"""
cutoff = time.time() - (days * 86400)
return {
"tools": self._compute_tool_breakdown(self._get_tool_usage(cutoff, source)),
"skills": self._compute_skill_breakdown(self._get_skill_usage(cutoff, source)),
}
# ------------------------------------------------------------------ SQL
def _get_sessions(self, cutoff: float, source: str = None) -> List[Dict]:
return [dict(row) for row in self._query("_GET_SESSIONS", cutoff, source).fetchall()]
def _get_tool_usage(self, cutoff: float, source: str = None) -> List[Dict]:
"""Tool call counts from two sources: ``tool_name`` on 'tool' rows (set
by the gateway) and ``tool_calls`` JSON on assistant rows (covers CLI,
where tool_name is not populated). Overlapping tools take the max."""
tool_counts = Counter()
for row in self._query("_GET_TOOL_NAMES", cutoff, source).fetchall():
tool_counts[row["tool_name"]] += row["count"]
tool_calls_counts = Counter()
for row in self._query("_GET_TOOL_CALLS", cutoff, source).fetchall():
try:
for call in _parse_calls(row["tool_calls"]) or []:
name = (call.get("function", {}) if isinstance(call, dict) else {}).get("name")
if name:
tool_calls_counts[name] += 1
except (TypeError, AttributeError):
continue
if tool_calls_counts:
if tool_counts:
tool_counts = Counter({
tool: max(tool_counts.get(tool, 0), tool_calls_counts.get(tool, 0))
for tool in set(tool_counts) | set(tool_calls_counts)
})
else:
tool_counts = tool_calls_counts
return [{"tool_name": name, "count": count} for name, count in tool_counts.most_common()]
def _get_skill_usage(self, cutoff: float, source: str = None) -> List[Dict]:
"""Extract per-skill usage from assistant tool calls."""
skill_counts: Dict[str, Dict[str, Any]] = {}
for row in self._query("_GET_SKILL_CALLS", cutoff, source).fetchall():
calls = _parse_calls(row["tool_calls"])
if calls is None:
continue
timestamp = row["timestamp"]
for call in calls:
if not isinstance(call, dict):
continue
func = call.get("function", {})
tool_name = func.get("name")
if tool_name not in _SKILL_TOOLS:
continue
args = func.get("arguments")
if isinstance(args, str):
try:
args = json.loads(args)
except (json.JSONDecodeError, TypeError):
continue
if not isinstance(args, dict):
continue
skill_name = args.get("name")
if not isinstance(skill_name, str) or not skill_name.strip():
continue
entry = skill_counts.setdefault(
skill_name,
{"skill": skill_name, "view_count": 0, "manage_count": 0, "last_used_at": None},
)
entry["view_count" if tool_name == "skill_view" else "manage_count"] += 1
if timestamp is not None and (
entry["last_used_at"] is None or timestamp > entry["last_used_at"]
):
entry["last_used_at"] = timestamp
return list(skill_counts.values())
def _get_message_stats(self, cutoff: float, source: str = None) -> Dict:
row = self._query("_GET_MESSAGE_STATS", cutoff, source).fetchone()
return dict(row) if row else {
"total_messages": 0, "user_messages": 0,
"assistant_messages": 0, "tool_messages": 0,
}
def _get_model_usage(self, cutoff: float, source: str = None) -> List[Dict]:
"""Per-model usage rows; [] when the table is missing (older DB) so the
caller falls back to the per-session aggregate."""
try:
return [dict(row) for row in self._query("_GET_MODEL_USAGE", cutoff, source).fetchall()]
except sqlite3.OperationalError:
return []
# -------------------------------------------------------------- Compute
def _compute_overview(
self,
sessions: List[Dict],
message_stats: Dict,
models: Optional[List[Dict]] = None,
) -> Dict:
# Per-model breakdown includes auxiliary usage rows (vision/compression/
# titles) plus reconciled residuals, while session counters carry
# main-loop usage only — sum the breakdown when available so overview
# totals match the per-model table and aux spend isn't undercounted.
rows = models or sessions
total_input, total_output, total_cache_read, total_cache_write = (
sum(int(r.get(k) or 0) for r in rows) for k in _TOKEN_KEYS
)
total_tokens = total_input + total_output + total_cache_read + total_cache_write
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)
total_cost = actual_cost = 0.0
models_with_pricing, models_without_pricing = set(), set()
status_counts = Counter()
for s in sessions:
model = s.get("model") or ""
estimated, status = _estimate_cost(s)
total_cost += estimated
actual_cost += s.get("actual_cost_usd") or 0.0
status_counts[status] += 1
known = has_known_pricing(model, s.get("billing_provider"), s.get("billing_base_url"))
(models_with_pricing if known else models_without_pricing).add(_short_model(model))
if models:
total_cost = sum(float(m.get("cost") or 0.0) for m in models)
# Guard against negative durations from clock drift.
durations = [
s["ended_at"] - s["started_at"]
for s in sessions
if s.get("started_at") and s.get("ended_at") and s["ended_at"] > s["started_at"]
]
started = [s["started_at"] for s in sessions if s.get("started_at")]
n = len(sessions)
return {
"total_sessions": n,
"total_messages": total_messages,
"total_tool_calls": total_tool_calls,
"total_input_tokens": total_input,
"total_output_tokens": total_output,
"total_cache_read_tokens": total_cache_read,
"total_cache_write_tokens": total_cache_write,
"total_tokens": total_tokens,
"estimated_cost": total_cost,
"actual_cost": actual_cost,
"total_hours": sum(durations) / 3600 if durations else 0,
"avg_session_duration": sum(durations) / len(durations) if durations else 0,
"avg_messages_per_session": total_messages / n if sessions else 0,
"avg_tokens_per_session": total_tokens / n 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": min(started) if started else None,
"date_range_end": max(started) if started else None,
"models_with_pricing": sorted(models_with_pricing),
"models_without_pricing": sorted(models_without_pricing),
"unknown_cost_sessions": status_counts["unknown"],
"included_cost_sessions": status_counts["included"],
}
def _compute_model_breakdown(
self, sessions: List[Dict], cutoff: float, source: str = None
) -> List[Dict]:
"""Tokens/cost per model from session_model_usage, so a session that
switched models via ``/model`` splits across every model it used.
Sessions without per-model rows (pre-table data) fall back to their
single recorded aggregate. Tool calls aren't tied to an API call, so
they stay attributed to the session's recorded model."""
model_data = defaultdict(lambda: {
"sessions": set(), "input_tokens": 0, "output_tokens": 0,
"cache_read_tokens": 0, "cache_write_tokens": 0,
"reasoning_tokens": 0, "total_tokens": 0, "api_calls": 0,
"tool_calls": 0, "cost": 0.0, "actual_cost": 0.0,
})
def _accumulate(model, provider, base_url, session_id, inp, out,
cache_read, cache_write, reasoning, *,
stored_cost=None, actual_cost=None, cost_status=None):
model = model or "unknown"
display_model = _short_model(model)
d: Dict[str, Any] = model_data[display_model]
d["sessions"].add(session_id)
d["input_tokens"] += inp
d["output_tokens"] += out
d["cache_read_tokens"] += cache_read
d["cache_write_tokens"] += cache_write
d["reasoning_tokens"] += reasoning
d["total_tokens"] += inp + out + cache_read + cache_write
if stored_cost is None:
estimate, status = _estimate_cost(
model, inp, out,
cache_read_tokens=cache_read, cache_write_tokens=cache_write,
provider=provider or None, base_url=base_url,
)
else:
estimate, status = float(stored_cost or 0.0), cost_status or "unknown"
d["cost"] += estimate
d["actual_cost"] += float(actual_cost or 0.0)
d["cost_status"] = status
if has_known_pricing(model, provider or None, base_url):
d["has_pricing"] = True
else:
d.setdefault("has_pricing", False)
return display_model
count_keys = _TOKEN_KEYS + ("reasoning_tokens", "api_call_count")
usage_totals = defaultdict(lambda: dict.fromkeys(count_keys, 0) | {
"estimated_cost_usd": 0.0, "actual_cost_usd": 0.0,
})
for r in self._get_model_usage(cutoff, source):
totals: Dict[str, Any] = usage_totals[r["session_id"]]
for key in count_keys:
totals[key] += r[key] or 0
totals["estimated_cost_usd"] += r["estimated_cost_usd"] or 0.0
totals["actual_cost_usd"] += r["actual_cost_usd"] or 0.0
d = _accumulate(
r["model"], r["billing_provider"], r.get("billing_base_url"),
r["session_id"], r["input_tokens"] or 0, r["output_tokens"] or 0,
r["cache_read_tokens"] or 0, r["cache_write_tokens"] or 0,
r["reasoning_tokens"] or 0,
stored_cost=(
r["estimated_cost_usd"]
if r.get("cost_status") or r.get("cost_source")
else None
),
actual_cost=r["actual_cost_usd"],
cost_status=r.get("cost_status"),
)
model_data[d]["api_calls"] += r["api_call_count"] or 0
# Reconcile against the aggregate row: covers legacy sessions,
# interrupted migrations, and absolute cumulative updates without
# double-counting already-attributed route deltas.
for s in sessions:
totals = usage_totals[s["id"]]
inp, out, cache_read, cache_write, residual_calls = (
max(0, (s.get(k) or 0) - totals[k]) for k in _TOKEN_KEYS + ("api_call_count",)
)
residual_cost = max(
0.0, float(s.get("estimated_cost_usd") or 0.0) - totals["estimated_cost_usd"],
)
residual_actual = max(
0.0, float(s.get("actual_cost_usd") or 0.0) - totals["actual_cost_usd"],
)
if not (
inp or out or cache_read or cache_write or residual_cost
or residual_actual or residual_calls
):
continue
d = _accumulate(
s.get("model"), s.get("billing_provider"),
s.get("billing_base_url"), s["id"],
inp, out, cache_read, cache_write, 0,
stored_cost=residual_cost,
actual_cost=residual_actual,
cost_status=s.get("cost_status"),
)
model_data[d]["api_calls"] += residual_calls
for s in sessions:
tool_calls = s.get("tool_call_count") or 0
if tool_calls:
model_data[_short_model(s.get("model"))]["tool_calls"] += tool_calls
result = []
for model, data in model_data.items():
entry = {"model": model, **data, "sessions": len(data["sessions"])}
# Models seen only via tool-call attribution never hit _accumulate —
# default these so the output shape is uniform for JSON consumers.
entry.setdefault("has_pricing", False)
entry.setdefault("cost_status", "unknown")
result.append(entry)
result.sort(key=lambda x: (x["total_tokens"], x["sessions"]), reverse=True)
return result
def _compute_platform_breakdown(self, sessions: List[Dict]) -> List[Dict]:
platform_data = defaultdict(lambda: {
"sessions": 0, "messages": 0, "input_tokens": 0,
"output_tokens": 0, "cache_read_tokens": 0,
"cache_write_tokens": 0, "total_tokens": 0, "tool_calls": 0,
})
for s in sessions:
d = platform_data[s.get("source") or "unknown"]
d["sessions"] += 1
d["messages"] += s.get("message_count") or 0
for k in _TOKEN_KEYS:
d[k] += s.get(k) or 0
d["total_tokens"] += s.get(k) or 0
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]:
"""Ranked tool list with percentages."""
total_calls = sum(t["count"] for t in tool_usage) if tool_usage else 0
return [
{
"tool": t["tool_name"],
"count": t["count"],
"percentage": (t["count"] / total_calls * 100) if total_calls else 0,
}
for t in tool_usage
]
def _compute_skill_breakdown(self, skill_usage: List[Dict]) -> Dict[str, Any]:
"""Per-skill usage → summary + ranked list."""
total_skill_loads = sum(s["view_count"] for s in skill_usage) if skill_usage else 0
total_skill_edits = sum(s["manage_count"] for s in skill_usage) if skill_usage else 0
total_skill_actions = total_skill_loads + total_skill_edits
top_skills = []
for skill in skill_usage:
total_count = skill["view_count"] + skill["manage_count"]
top_skills.append({
"skill": skill["skill"],
"view_count": skill["view_count"],
"manage_count": skill["manage_count"],
"total_count": total_count,
"percentage": (total_count / total_skill_actions * 100) if total_skill_actions else 0,
"last_used_at": skill.get("last_used_at"),
})
top_skills.sort(
key=lambda s: (
s["total_count"], s["view_count"], s["manage_count"], s["last_used_at"] or 0, s["skill"],
),
reverse=True,
)
return {
"summary": {
"total_skill_loads": total_skill_loads,
"total_skill_edits": total_skill_edits,
"total_skill_actions": total_skill_actions,
"distinct_skills_used": len(skill_usage),
},
"top_skills": top_skills,
}
def _compute_activity_patterns(self, sessions: List[Dict]) -> Dict:
"""Activity by day of week, hour, and active-day streak."""
day_counts = Counter() # 0=Monday ... 6=Sunday
hour_counts = Counter()
daily_counts = Counter() # "YYYY-MM-DD" -> 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)]
max_streak = 0
if daily_counts:
dates = [datetime.strptime(d, "%Y-%m-%d") for d in sorted(daily_counts)]
current_streak = max_streak = 1
for prev, cur in zip(dates, dates[1:]):
current_streak = current_streak + 1 if (cur - prev).days == 1 else 1
max_streak = max(max_streak, current_streak)
return {
"by_day": day_breakdown,
"by_hour": hour_breakdown,
"busiest_day": max(day_breakdown, key=lambda x: x["count"]),
"busiest_hour": max(hour_breakdown, key=lambda x: x["count"]),
"active_days": len(daily_counts),
"max_streak": max_streak,
}
_TOP_METRICS = (
("Most messages", lambda s: s.get("message_count") or 0, "{} msgs"),
("Most tokens", lambda s: (s.get("input_tokens") or 0) + (s.get("output_tokens") or 0), "{:,} tokens"),
("Most tool calls", lambda s: s.get("tool_call_count") or 0, "{} calls"),
)
def _compute_top_sessions(self, sessions: List[Dict]) -> List[Dict]:
"""Notable sessions (longest, most messages, most tokens, most tool calls)."""
top = []
timed = [s for s in sessions if s.get("started_at") and s.get("ended_at")]
if timed:
longest = max(timed, key=lambda s: s["ended_at"] - s["started_at"])
top.append({
"label": "Longest session",
"session_id": longest["id"][:16],
"value": format_duration_compact(longest["ended_at"] - longest["started_at"]),
"date": _day(longest["started_at"]),
})
for label, metric, fmt in self._TOP_METRICS:
best = max(sessions, key=metric)
value = metric(best)
if value > 0:
top.append({
"label": label,
"session_id": best["id"][:16],
"value": fmt.format(value),
"date": _day(best.get("started_at")),
})
return top
# ------------------------------------------------------------- Formatting
@staticmethod
def _section(title: str) -> List[str]:
return [f" {title}", " " + "─" * 56]
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}."
o = report["overview"]
period_label = f"Last {report['days']} days"
if report.get("source_filter"):
period_label += f" ({report['source_filter']})"
padding = 58 - len(period_label) - 2
left_pad = padding // 2
lines = [
"",
" ╔══════════════════════════════════════════════════════════╗",
" ║ 📊 Hermes Insights ║",
f" ║{' ' * left_pad} {period_label} {' ' * (padding - left_pad)}║",
" ╚══════════════════════════════════════════════════════════╝",
"",
]
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 += [f" Period: {start_str} — {end_str}", ""]
lines += self._section("📋 Overview")
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']:,}")
lines.append(f" Total tokens: {o['total_tokens']:,}")
if o["total_hours"] > 0:
lines.append(f" Active time: ~{format_duration_compact(o['total_hours'] * 3600):<11} Avg session: ~{format_duration_compact(o['avg_session_duration'])}")
lines += [f" Avg msgs/session: {o['avg_messages_per_session']:.1f}", ""]
# Cost buckets: show included/unknown sessions instead of collapsing to $0.
est_cost = o.get("estimated_cost", 0.0)
included_sessions = o.get("included_cost_sessions", 0)
unknown_sessions = o.get("unknown_cost_sessions", 0)
if est_cost > 0 or included_sessions > 0 or unknown_sessions > 0:
lines += self._section("💰 Cost")
if est_cost > 0:
lines.append(f" Estimated: {_fmt_est_cost(est_cost)}")
if included_sessions > 0:
lines.append(f" Included: {included_sessions} session(s) (subscription — no provider invoice)")
if unknown_sessions > 0:
lines.append(f" Unknown: {unknown_sessions} session(s) (no pricing data)")
lines.append("")
if report["models"]:
lines += self._section("🤖 Models Used")
lines.append(f" {'Model':<30} {'Sessions':>8} {'Tokens':>12}")
for m in report["models"]:
lines.append(f" {m['model'][:28]:<30} {m['sessions']:>8} {m['total_tokens']:>12,}")
lines.append("")
platforms = report["platforms"]
if len(platforms) > 1 or (platforms and platforms[0]["platform"] != "cli"):
lines += self._section("📱 Platforms")
lines.append(f" {'Platform':<14} {'Sessions':>8} {'Messages':>10} {'Tokens':>14}")
for p in platforms:
lines.append(f" {p['platform']:<14} {p['sessions']:>8} {p['messages']:>10,} {p['total_tokens']:>14,}")
lines.append("")
if report["tools"]:
lines += self._section("🔧 Top Tools")
lines.append(f" {'Tool':<28} {'Calls':>8} {'%':>8}")
for t in report["tools"][: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("")
skills = report.get("skills", {})
top_skills = skills.get("top_skills", [])
if top_skills:
lines += self._section("🧠 Top Skills")
lines.append(f" {'Skill':<28} {'Loads':>7} {'Edits':>7} {'Last used':>11}")
for skill in top_skills[:10]:
last_used = _day(skill.get("last_used_at")) if skill.get("last_used_at") else "—"
lines.append(
f" {skill['skill'][:28]:<28} {skill['view_count']:>7,} {skill['manage_count']:>7,} {last_used:>11}"
)
summary = skills.get("summary", {})
lines.append(
f" Distinct skills: {summary.get('distinct_skills_used', 0)} "
f"Loads: {summary.get('total_skill_loads', 0):,} "
f"Edits: {summary.get('total_skill_edits', 0):,}"
)
lines.append("")
act = report.get("activity", {})
if act.get("by_day"):
lines += self._section("📅 Activity Patterns")
bars = _bar_chart([d["count"] for d in act["by_day"]], max_width=15)
for bar, d in zip(bars, act["by_day"]):
lines.append(f" {d['day']} {bar:<15} {d['count']}")
lines.append("")
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 = [f"{_hour12(h['hour'])} ({h['count']})" for h in busy_hours]
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("")
if report.get("top_sessions"):
lines += self._section("🏆 Notable Sessions")
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"):
return f"No sessions found in the last {report.get('days', 30)} days."
o = report["overview"]
lines = [
f"📊 **Hermes Insights** — Last {report['days']} days\n",
f"**Sessions:** {o['total_sessions']} | **Messages:** {o['total_messages']:,} | **Tool calls:** {o['total_tool_calls']:,}",
f"**Tokens:** {o['total_tokens']:,} (in: {o['total_input_tokens']:,} / out: {o['total_output_tokens']:,})",
]
if o["total_hours"] > 0:
lines.append(f"**Active time:** ~{format_duration_compact(o['total_hours'] * 3600)} | **Avg session:** ~{format_duration_compact(o['avg_session_duration'])}")
lines.append("")
est_cost = o.get("estimated_cost", 0.0)
included = o.get("included_cost_sessions", 0)
unknown = o.get("unknown_cost_sessions", 0)
cost_parts: list[str] = []
if est_cost > 0:
cost_parts.append(f"{_fmt_est_cost(est_cost)} estimated")
if included > 0:
cost_parts.append(f"{included} included (subscription)")
if unknown > 0:
cost_parts.append(f"{unknown} unknown")
if cost_parts:
lines += [f"**Cost:** {' | '.join(cost_parts)}", ""]
if report["models"]:
lines.append("**🤖 Models:**")
for m in report["models"][:5]:
lines.append(f" {m['model'][:25]} — {m['sessions']} sessions, {m['total_tokens']:,} tokens")
lines.append("")
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("")
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("")
skills = report.get("skills", {})
if skills.get("top_skills"):
lines.append("**🧠 Top Skills:**")
for skill in skills["top_skills"][:5]:
suffix = f", last used {_day(skill['last_used_at'])}" if skill.get("last_used_at") else ""
lines.append(
f" {skill['skill']} — {skill['view_count']:,} loads, {skill['manage_count']:,} edits{suffix}"
)
lines.append("")
act = report.get("activity", {})
if act.get("busiest_day") and act.get("busiest_hour"):
lines.append(f"**📅 Busiest:** {act['busiest_day']['day']}s ({act['busiest_day']['count']} sessions), {_hour12(act['busiest_hour']['hour'])} ({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)