> ## Documentation Index
> Fetch the complete documentation index at: https://docs.openlit.io/llms.txt
> Use this file to discover all available pages before exploring further.
# Overview
> LLM observability for traces, metrics, logs, and exceptions - all in one Telemetry page, built on OpenTelemetry
The **Telemetry** page is the single home for every OpenTelemetry signal OpenLIT ingests from your AI applications. Navigate to it at `/telemetry`. It has four tabs, all backed by the same ClickHouse tables (`otel_traces`, `otel_metrics_*`, `otel_logs`) populated by the OpenTelemetry Collector built into OpenLIT:
Distributed traces with grouping, a five-view trace detail explorer, and span/resource attributes
AI-generated review of a trace or span across strengths, improvements, cost, and token efficiency
Every OTel metric OpenLIT has received, with a trend chart and data-point detail per metric
Searchable, filterable OTel log records with a detail view per entry
The **Exceptions** tab is documented alongside Traces since it reuses the same trace detail explorer - see [Traces & Exceptions](/latest/openlit/observability/telemetry/traces).
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## Frequently asked questions
AI observability is the practice of collecting traces, metrics, and logs from LLM applications and agents so you can understand behavior, cost, latency, and errors in production - OpenLIT does this via standard OpenTelemetry data.
Yes. Logs have their own dedicated explorer under Telemetry, ingested the same way as traces and metrics through the built-in OpenTelemetry Collector's OTLP receiver.
Yes. The Metrics explorer and Dashboard Widgets read from the same underlying data - use the explorer to inspect a single metric, and dashboards for cross-signal, trend-style visualizations.