Documentation for automated readers
A curated documentation index is available at: https://grafana.com/llms.txt
A complete documentation index is available at: https://grafana.com/llms-full.txt
These indexes can help with page discovery before fetching individual documents.
This page is also available in Markdown, which may be easier for automated readers and AI tools to parse than HTML. The Markdown version is available at https://grafana.com/docs/grafana-cloud/knowledge-graph/reference/about-base-insights.md, or by sending Accept: text/markdown to https://grafana.com/docs/grafana-cloud/knowledge-graph/reference/about-base-insights/. For broader documentation discovery, the curated index is available at https://grafana.com/llms.txt and the complete index is available at https://grafana.com/llms-full.txt.
Default insights library
The knowledge graph uses a curated library of base rules to analyze incoming time-series data and provide default insights. These insights map to the standard categories (Saturation, Amend, Anomaly, Failure, and Error). For category definitions, severity levels, and UI ring behavior, refer to Insights categories.
In terms of implementation, the knowledge graph operates with two main sets of rules that generate insights: base rules and framework-specific rules. Base rules focus on common request and resource metrics across different platforms and frameworks.
The following table summarizes base insights raised by the base rules.
| Metric type | Insight | Category |
|---|---|---|
| Request | RequestRateAnomaly | Anomaly |
| LatencyAverageAnomaly | Anomaly | |
| LatencyAverageBreach | Error | |
| LatencyP99ErrorBuildup | Error | |
| ErrorRatioAnomaly | Anomaly | |
| InboundClientErrorAnomaly | Anomaly | |
| ErrorRatioBreach | Error | |
| ErrorBuildup | Error | |
| Logging | LoggerRateAnomaly | Anomaly |
| ErrorLogRateBreach | Error | |
| Resource | Saturation | Saturation |
| ResourceRateAnomaly | Anomaly | |
| ResourceRateBreach | Error | |
| ResourceMayExhaust | Error |
Default KPI-based insights
The following examples show how common KPIs map to default insights and how those insights are detected.
| KPI | KPI metric | KPI metric description | Insight | Insight description |
|---|---|---|---|---|
| Request rate | asserts:request:rate5m | Request rate over the last 5 minutes. | RequestRateAnomaly | Uses a 26‑hour baseline and a standard‑deviation model to detect spikes or drops. Triggers when the metric stays outside the bounds for 5 minutes. |
| Latency (average) | asserts:latency:average | Average latency computed as rate(time_takencount[5m]) / request_rate_5m. | LatencyAverageBreach | Uses the last 7 days to set a threshold at the 95th percentile. Triggers when the threshold is exceeded for 5 minutes. |
| Latency (average) | asserts:latency:average | Average latency computed as rate(time_takencount[5m]) / request_rate_5m. | LatencyAverageAnomaly | Uses a 26‑hour baseline and a standard‑deviation model to detect spikes or drops. Triggers when the metric stays outside the bounds for 5 minutes. |
| Latency (p99) | asserts:latency:p99 | 99th percentile of the latency histogram over a 5‑minute window. | LatencyP99ErrorBuildup | Uses the last 7 days to set a 95th‑percentile threshold. Tracks how many minutes exceed the threshold in a 1‑hour window and triggers when the count passes a limit. |
| Error ratio | asserts:error:ratio | Error count in the last 5 minutes divided by total request count in the last 5 minutes. | ErrorRatioBreach | Uses the last 24 hours to set a 75th‑percentile threshold. Triggers when the error ratio exceeds that threshold. |
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