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Overview

Overview dashboard populated after a multi-turn chat session — eight KPI cards along the top (Calls, Tokens, Cost USD, Error rate, p95 latency, Heap used, Process CPU, Active LLM ops), the Request rate chart below showing per-minute spikes, followed by sections for LLM, Tokens & Cost, Tools / MCP, Vector / RAG, System, Logs & outcome, and a recent activity grid

Overview after a brief chat session — one card or chart per concern, with every section linking conceptually to the dedicated tab that owns it. The eight KPI cards at the top stay visible regardless of activity; the charts below fill in as data accumulates.

Route — sidebar → top item → observability?tab=overview

Purpose — single-page summary of every other tab's headline number. Open it first when you don't yet know what you're looking for.

When to look here

  • "Has anything changed in the last 30 minutes?" — every KPI has a sparkline showing the trend across the window.
  • "Where do I drill in?" — every section header links conceptually to a dedicated tab (Tokens & Cost, MCP Servers, etc.). Spot the anomaly here, then jump.
  • "What's the system doing right now?" — heap, CPU, active LLM ops gauges live on this page.
  • "Last 10 traces" — the activity grid at the bottom shows the most recent traces; click any row to open Trace Detail.

Controls

All dashboards share the Observability global settings — time window (LAST_5M · LAST_10M · LAST_20M · LAST_30M · LAST_1H · LAST_3H, default LAST_30M), refresh interval, custom From/To range. Sparklines and charts retick to the selected window. Overview adds no tab-specific controls.

KPI cards (eight)

Each headline metric carries a 60-sample sparkline showing the trend:

Card Shows Source
Calls Total chat turns in the window TraceRecord count
Tokens Sum of input + output tokens TraceRecord.totalTokens (falls back to inputTokens + outputTokens when total is null)
Cost (USD) Total cost across the window ModelPricingService.cost() × token counts
Error rate Percentage of traces with status=ERROR TraceRecord.status
p95 latency 95th-percentile turn duration ObservabilityTimeSeries.compute().p95LatencyMs
Heap used Current JVM heap utilisation (live gauge) MeterRegistry jvm.memory.used (heap area)
Process CPU Current process CPU percentage (live gauge) MeterRegistry process.cpu.usage
Active LLM ops Number of in-flight Spring AI operations MeterRegistry LongTaskTimer active counts (ChatClient + Advisor + Vector store)

Charts — five sections, sixteen charts

Each section maps to one dedicated tab.

LLM section → AI Models

Chart Type Reading
Request rate Line, calls / min — drag to zoom Bursts indicate batch workloads or runaway loops
Latency p50 / p95 / p99 Multi-line, ms p95 — p50 gap widening means tail-latency regression
Provider mix Donut, by call count Sanity check that the configured provider is actually receiving traffic
Top models Horizontal bar (top 8) Spot accidental model drift if an unfamiliar model appears

Tokens & Cost section → Tokens & Cost

Chart Type Reading
Cost over time Area, per-minute USD Sharp upward step → expensive model invoked unexpectedly
Tokens (input + output) Stacked bar, 2-series Disproportionate output → verbose model; disproportionate input → context-stuffing

Tools / MCP section → Tool Studio · MCP Servers

Chart Type Reading
Tool calls / minute Line Spike → agent in a tool-heavy phase (e.g. multi-step research)
Transport mix Donut — in-process / stdio / Streamable HTTP / SSE If one transport dominates unexpectedly, the agent is rerouting through it
Top tools Horizontal bar (by call count) "Which tool is the agent reaching for most"
Top MCP servers Horizontal bar (excludes in-process) "Which external dependency is busiest"

Vector / RAG section → Vector Database

Chart Type Reading
Vector ops / minute Line — db.vector.client.operation spans Drop to zero → RAG offline; sustained high → expensive retrieval
top_k distribution Horizontal bar Sanity-check the top_k parameter spread; outliers indicate misconfigured agents

System section → Host

Chart Type Reading
Heap used (MB) Rolling line — 60-sample history Climbing without GC drop → leak suspicion
Process CPU (%) Rolling line — 60-sample history Sustained high CPU during quiet periods → background work issue

Logs & outcome section → Logs · Traces

Chart Type Reading
Logback events by level Horizontal bar — lifetime totals Sudden growth in ERROR/WARN → drill into Logs tab
Outcome mix Stacked bar — OK / Error per bucket Visualises trend rather than absolute level

Recent activity table

The bottom of the page is a grid of the most recent 10 traces, refreshed in real time from the ring buffer's live stream:

Column Source
Time TraceRecord.startEpochMs (HH:mm format)
Conv TraceRecord.conversationId (truncated to 10 chars)
Provider TraceRecord.provider (from gen_ai.system)
Model TraceRecord.model (from gen_ai.response.model or gen_ai.request.model)
In / Out Input and output tokens, slash-separated
Duration TraceRecord.durationMs, human-formatted
Status OK / ERROR / TIMEOUT / CANCELLED

Click any row → opens the Trace Detail dialog. This is the fastest path from "something looks off in the overview" to the full span timeline. The same dialog is reachable from the Traces tab.

Cross-references

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