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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.

The Exceptions page shows all traces that contain errors or exceptions from your AI applications. You can filter by time range and exception type to quickly find and debug issues.

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Error traces

All error traces are automatically captured and displayed here, including:
  • LLM API errors: Authentication, rate limits, model issues
  • Framework errors: LangChain, LlamaIndex execution failures
  • Vector database errors: Connection and query issues
  • Application errors: Custom exceptions and validation errors

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Filtering

Use the time range selector to filter errors by period (24H, 7D, 1M, 3M, or custom range). Each error shows the trace ID, timestamp, span name, deployment type, and specific exception type.

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Exception details

Click any error to view the complete trace context with detailed exception information, stack traces, and execution flow.

Quickstart: LLM Observability

Production-ready AI monitoring setup in 2 simple steps with zero code changes

Create a dashboard

Create custom visualizations with flexible widgets, queries, and real-time AI monitoring

Integrations

60+ AI integrations with automatic instrumentation and performance tracking

Zero-code observability with the OpenLIT Controller

Discover and instrument LLM traffic across Kubernetes, Docker, and Linux using eBPF — no code changes required.