Analyze Claude Code usage trends over time using the Agent Monitor's analytics API — daily session counts, daily event counts, token volumes by type, model distribution, tool usage rankings, and agent/event type distributions across 365-day retention windows.
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Analyze usage patterns and trends from the Agent Monitor analytics data.
The user provides: $ARGUMENTS
Options: "last 7 days", "last 30 days", "last quarter", "peak hours", "tool trends", "model usage".
| Endpoint | Returns |
|---|---|
GET /api/analytics | Comprehensive analytics object (see schema below) |
GET /api/stats | { total_sessions, active_sessions, active_agents, total_agents, total_events, events_today, ws_connections, agents_by_status, sessions_by_status } |
GET /api/sessions?limit=200 | Full session records with timestamps and metadata |
GET /api/analytics){
"overview": { "total_sessions", "active_sessions", "active_agents", "total_agents", "total_events" },
"tokens": {
"total_input": N, "total_output": N,
"total_cache_read": N, "total_cache_write": N
},
"tool_usage": [{ "tool_name": "...", "count": N }], // top 20
"daily_events": [{ "date": "YYYY-MM-DD", "count": N }], // 365 days
"daily_sessions": [{ "date": "YYYY-MM-DD", "count": N }], // 365 days
"agent_types": [{ "subagent_type": "task"|"explore"|null, "count": N }],
"event_types": [{ "event_type": "PreToolUse"|"PostToolUse"|..., "count": N }],
"avg_events_per_session": N,
"total_subagents": N,
"sessions_by_status": { "active": N, "completed": N, "error": N, "abandoned": N },
"agents_by_status": { "working": N, "completed": N, "error": N, ... }
}
Plot daily_sessions and daily_events for the requested period. Compute:
From analytics tokens (baselines are pre-summed into totals at the DB level):
total_input, total_output, total_cache_read, total_cache_writetotal_cache_read / (total_cache_read + total_input) — trending up = improvingtotal_output / total_input ratio — high = Claude is verboseFrom tool_usage (top 20 tools by event count):
From agent_types + per-session model field:
From sessions_by_status:
completed / total × 100error / total × 100abandoned / total × 100From event_types:
Markdown with tables and ASCII trend indicators (▲▼→). Include period comparison when applicable.
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Inspect fired CCAM alerts and manage alert rules for token thresholds, event patterns, inactivity, and status duration. Use when acknowledging alerts, creating or editing a rule, checking cooldowns, or connecting alert rules to webhook targets.
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Identify anomalous sessions using Agent Monitor data — cost outliers from the pricing engine, token anomalies (cache miss spikes, compaction baseline surges), unusual event type ratios (PreToolUse/PostToolUse gaps, APIError clusters), behavioral deviations from workflow intelligence (complexity score outliers, error propagation anomalies), and sessions with abnormal metadata (extreme turn_count, high thinking_blocks, zero turn_duration).
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Produce a detailed report on APIError events from Agent Monitor data — counts over time, which sessions and models are affected, and the likely root cause (rate limits, overload/529, or context-window pressure) inferred from each event's summary and data payload. Use when API errors spike or when you need to explain why requests are failing.
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Benchmark one session (or a small recent set) against the rolling average using Agent Monitor data — cost, total tokens, tool count, and workflow complexity score — and report where each metric lands as a percentile of the population. Tells you whether a session was normal, cheap, or an outlier. Use when judging whether a session was typical or out of band.
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Define a spend budget for Claude Code and, optionally, create a cost alert rule that fires when usage crosses the limit, via POST /api/alerts/rules on the Agent Monitor dashboard. Reads current spend from /api/pricing/cost to size the budget sensibly and explains every rule field before writing. Use when setting a spend limit or wiring up a budget guardrail.
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Analyze prompt-cache effectiveness for Claude Code usage from the Agent Monitor dashboard — cache hit rate (total_cache_read / (total_cache_read + total_input)), cache_write vs cache_read reuse, cache-read vs cache-write spend, and the sessions with the poorest reuse. Pulls token totals from /api/analytics, per-session detail from /api/sessions, and dollar splits from /api/pricing/cost. Use when diagnosing cache spend or deciding whether prompt caching is paying off.
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