LLM Observability

Connect existing ecosystems like OpenTelemetry, LangChain, and LangFuse to OpenObserve. Ingest instrumentation data immediately, no pipeline re-architecting required.

Unify your data schema across all frameworks and model providers. Format incoming telemetry instantly to stop fragmentation and simplify cross-model debugging.

Visualize the full execution tree of every LLM call, from system prompt to final response. Record inputs, outputs, duration, and token counts across LangChain, LlamaIndex, OpenAI SDK or any OpenTelemetry-instrumented framework.

Stitch traces across process boundaries automatically using W3C context propagation. When agents hand off to sub-agents or call external tools, you’ll see exactly which step introduced a hallucination, timeout, or unexpected output.

Analyze token usage and costs per request and per span. Identify the most expensive steps in your LLM workflows instantly with real-time estimates based on your custom model pricing.

Inspect the exact prompts, context, and model responses at every span level. Debug context contamination, prompt drift, or unexpected outputs without guesswork, directly from the trace view.

Easily search and analyze data across traces, spans, models, and request attributes. Quickly investigate high-cost, high-latency, or abnormal-output scenarios with efficient filtering and aggregation built for LLM workloads.

Define and manage custom model pricing to match your real billing rates. This enables accurate cost calculation, forecasting, and internal chargeback/showback, without relying on generic published estimates.