> ## Documentation Index > Fetch the complete documentation index at: https://docs.myrmagent.ai/llms.txt > Use this file to discover all available pages before exploring further. # Statistics > Endpoints for analytics, growth metrics, daily activity journals, and AI-powered daily wrap summaries. # Statistics API Endpoints for querying agent activity analytics, growth metrics, daily work journals, and AI-generated daily summaries. ## Daily Journal Retrieve a consolidated view of all agent activity for a specific day. ``` GET /api/v1/statistics/daily-journal?date=YYYY-MM-DD&agent_id=optional ``` ### Parameters | Parameter | Type | Required | Description | | ---------- | ------ | -------- | --------------------------- | | `date` | string | Yes | Date in `YYYY-MM-DD` format | | `agent_id` | string | No | Filter by specific agent ID | ### Response ```json theme={null} { "code": 0, "data": { "date": "2026-05-31", "overview": { "total_sessions": 5, "total_tokens": 42000, "total_cost": 0.35, "tool_call_count": 28, "approval_count": 2, "cron_run_count": 1, "kanban_event_count": 3, "sessions_by_source": { "web": 3, "telegram": 1, "api": 1 } }, "sessions": [ { "id": "abc-123", "title": "Code review session", "source": "web", "started_at": "2026-05-31T09:15:00Z", "total_tokens": 12000, "total_cost": 0.10 } ], "approvals": [], "cron_runs": [], "kanban_events": [], "timeline": [ { "type": "session", "time": "2026-05-31T09:15:00Z", "title": "Code review session", "detail": { "source": "web", "tokens": 12000 } } ] } } ``` ### Data Sources The journal aggregates data from 6 existing sources without any additional storage: | Source | Data | | -------------------- | -------------------------------------------- | | Chat | Session metadata (title, source, timestamps) | | Message | Token counts and cost per session | | ApprovalRecord | Human approval events | | CronRunModel | Scheduled task executions | | KanbanTaskEventModel | Kanban board events | | EventLog | Tool call counts (file-based) | ### Error Responses | Code | Description | | ---- | ------------------------------------------ | | 400 | Invalid date format (must be `YYYY-MM-DD`) | | 400 | Missing `date` parameter | ## Daily Wrap (AI Summary) Get an AI-generated natural-language summary of a day's activity, including keywords and next-day suggestions. Results are cached in SQLite to minimize LLM costs. ``` GET /api/v1/statistics/daily-wrap?date=YYYY-MM-DD ``` ### Parameters | Parameter | Type | Required | Description | | --------- | ------ | -------- | --------------------------- | | `date` | string | Yes | Date in `YYYY-MM-DD` format | ### Response ```json theme={null} { "code": 0, "data": { "date": "2026-06-27", "summary": "Productive day focused on code review and bug fixes. Completed 5 sessions across web and Telegram channels.", "keywords": ["code review", "bug fix", "telegram"], "suggestions": ["Continue the refactoring started in session #3", "Review pending approvals"], "generated_at": "2026-06-27T23:05:00Z", "cached": true } } ``` ### Regenerate Force a fresh AI summary, bypassing the cache: ``` POST /api/v1/statistics/daily-wrap/regenerate?date=YYYY-MM-DD ``` Returns the same response structure as the GET endpoint with `cached: false`. ### Requirements * A **Lite Model** must be configured in Settings (used for low-cost summary generation) * If no activity exists for the date, returns `reason: "no_activity"` with `summary: null` * If no Lite Model is configured, returns `reason: "lite_model_not_configured"` with `summary: null` ### How It Works 1. Aggregates data from the same 6 sources as Daily Journal (sessions, tokens, approvals, cron runs, kanban events, cost) 2. Builds a structured prompt and sends it to the configured Lite Model 3. Parses the LLM response (supports JSON and plain-text fallback) 4. Caches the result in a dedicated `daily_wrap_cache` SQLite table (one row per date) ## Per-Agent Usage Analytics Break down token consumption and cost by individual agent — see which agents consume the most resources, with 7-day trend sparklines. ``` GET /api/v1/statistics/usage/by-agent?days=7 ``` ### Parameters | Parameter | Type | Required | Description | | --------- | ------- | -------- | ------------------------------------------------------- | | `days` | integer | No | Number of days for sparkline data (default: 7, max: 30) | ### Response ```json theme={null} { "success": true, "data": { "agents": [ { "agentId": "builtin-general", "name": "General Assistant", "avatar": "icon:general", "totalTokens": 125000, "totalUsd": 1.25, "totalCalls": 42, "sessions": 15, "percentTokens": 65, "percentUsd": 72, "sparkline": [ { "date": "2026-06-03", "tokens": 18000, "usd": 0.18 }, { "date": "2026-06-04", "tokens": 22000, "usd": 0.22 } ] } ], "total_agents": 3, "grand_total_tokens": 192000, "grand_total_usd": 1.74 } } ``` ### Key Features * Results sorted by total USD cost (highest first) * Percentage breakdown shows each agent's share of total consumption * Sparkline data enables 7-day SVG trend visualization in the UI * Automatically resolves agent names and avatars from the Agent registry * The `AgentUsageCard` component auto-hides when there is only 1 agent (no comparison value) ## Session Execution Trace Retrieve the complete execution trace for a session — tool calls, LLM invocations, errors, human feedback events, and memory operations — structured for timeline replay. ``` GET /api/v1/statistics/session/{session_id}/trace ``` ### Response ```json theme={null} { "code": 0, "data": { "session_id": "sess-abc123", "metadata": { "user_id": "user-1", "agent_id": "builtin-general", "task_type": "chat", "trace_id": "trace-xyz" }, "outcome": "success", "start_time": 1720000000.0, "end_time": 1720000030.0, "duration_ms": 30000, "task_input": "Help me refactor this module", "output": "Done! I've refactored the module into 3 files.", "tool_calls": [ { "sequence": 1, "tool_name": "read_file", "start_time": 1720000002.0, "end_time": 1720000003.5, "duration_ms": 1500, "success": true, "error": null, "input_data": { "path": "/src/module.py" }, "output_summary": "Read 200 lines" } ], "llm_calls": [ { "sequence": 1, "start_time": 1720000001.0, "end_time": 1720000005.0, "model_name": "claude-sonnet-4-20250514", "prompt_preview": "[user] Help me refactor...", "message_count": 3, "duration_ms": 4000, "ttft_ms": 180, "prompt_tokens": 1200, "completion_tokens": 800, "total_tokens": 2000 } ], "errors": [], "human_feedback": [], "memory_events": [ { "id": "mem-1", "phase": "extraction", "status": "completed", "timestamp": 1720000028.0, "title": "Working Memory Extract", "summary": "Learned user prefers small focused modules", "target_kind": "memory", "target_id": "mem-target-1", "influence_count": 2 } ], "total_events": 12, "total_tokens": 2000 } } ``` ### Key Features * **Zero-storage replay**: Traces are reconstructed on-demand from the append-only event log — no extra database or video files needed * **Seven event types**: tool\_start, tool\_end, llm\_call, human\_feedback, memory, error, message * **Frontend Session Replay Player**: The WebUI provides a three-pane interactive player (Chat View / Mind View / Inspector) with scrubber, playback speed control, keyboard navigation, and jump-to-error * **Dataset export**: Traces can be batch-exported to ShareGPT/Alpaca/OpenAI JSONL format for fine-tuning via the `dataset_export` pipeline ## Growth Statistics Growth dashboard metrics are available through the unified **Learning Journey** page (`/journey`, former `/growth` auto-redirects) and provide: * Skill KPI summary (total, success rate, evolution count) * Smart savings summary (cost reduction through caching and routing) * 84-day activity heatmaps and multi-dimensional health radar charts * Weekly trend analysis and AI-powered daily wrap summaries * Evolution timeline with skill lifecycle events * **Knowledge Graph** — interactive Claim/Evidence 2D force-directed visualization with namespace filtering * **Skill Usage Efficiency Trends** — per-skill success rate, average duration, and call frequency over time (daily granularity) ## Harness Observability Metrics (Prometheus) The Myrm Engine exposes underlying Prometheus telemetry metrics for deep DevOps & SRE monitoring. These metrics run on the `/metrics` endpoint and incur zero operational overhead. ### Advanced Agent Metrics * `myrm_time_to_first_action_seconds` (Histogram): Captures the precise Time-To-First-Action (TTFA), calculating the exact time from receiving a user's instruction until the agent invokes its first tool. * `myrm_policy_denial_total` (Counter): Tracks the number of times agent actions were blocked, redacted, or denied by the Security Guardrails and Path Policies. * `myrm_tool_execution_total` & `myrm_tool_execution_failed_total` (Counters): Monitors execution results for all individual skills, providing real-time data for calculating the overall "Tool Effectiveness Ratio" on Grafana dashboards. ### Production Alerting Rules (Cloud Deployment) For cloud-hosted deployments, Myrm ships with 12 production-grade Prometheus alerting rules covering sandbox health and reliability: | Alert | Severity | Trigger | | -------------------------- | -------- | ------------------------------ | | ContainerPoolExhausted | Critical | Pool available = 0 for 5 min | | ContainerPoolLow | Warning | Pool available \< 2 for 10 min | | HealthCheckFailureRateHigh | Critical | Failure rate > 20% for 5 min | | ContainerCreationSlow | Warning | P99 creation > 10s for 5 min | | ContainerOOMKills | Critical | Any OOM kill detected | | HotPoolExhausted | Critical | Hot pool = 0 for 5 min | Each alert includes a `runbook_url` linking to the Enterprise Runbook for remediation steps. ### Grafana Dashboard A ready-to-import Grafana dashboard (`grafana-dashboard-sandbox.json`) provides real-time visualization of container pool status, hit rates, health check success rates, creation latency, and hot pool availability. Import via Grafana UI or provisioning directory mount. ### Smart Enable/Disable Metrics collection is automatically enabled or disabled based on deployment mode — local and desktop deployments run with zero overhead, while cloud deployments expose the full `/metrics` endpoint for Prometheus scraping. ## Agent Liveness SSOT A single aggregated endpoint that tells you whether your agent is busy, idle, or degraded — no need to poll multiple APIs. ``` GET /api/v1/health/liveness ``` ### Response ```json theme={null} { "state": "busy", "agents": { "activeCount": 2, "maxConcurrent": 3, "availableSlots": 1, "sessions": [ { "sessionId": "abc-123", "chatId": "chat-456", "agentType": "general", "elapsedSeconds": 12.5 } ] }, "channels": { "wechat": { "status": "connected" }, "telegram": { "status": "connected" } }, "memory": { "level": "NORMAL", "percent": 42.3 }, "uptimeSeconds": 3600.5 } ``` ### State Logic | State | Condition | | ---------- | -------------------------------------------------------------------------------- | | `busy` | At least one agent session is actively running | | `degraded` | No active sessions, but a channel is disconnected or memory pressure is elevated | | `idle` | No active sessions, all channels healthy, memory normal | ### Use Cases * **Frontend tray / pet indicator**: Poll this endpoint to show a tri-state icon (spinning = busy, green = idle, amber = degraded) * **Cloud monitoring**: `curl /api/v1/health/liveness | jq .state` integrates with Prometheus/Grafana alerting * **Multi-pane workspace**: Show agent availability across browser tabs without opening settings ### Key Properties * **Pure read-only aggregation**: Zero I/O, zero database queries — all data comes from in-memory gateway state * **Sub-millisecond response time**: Suitable for frequent polling (every 2-5 seconds) * **Works across all deployment modes**: Local WebUI, Tauri desktop, and cloud-hosted