# Tracing (LLM)

> LLM tracing records every step of a model-driven request — prompts, tool calls, retrievals, tokens, latency — so multi-step behavior is debuggable.

**LLM tracing is recording the complete execution of a model-driven request — every prompt, response, tool call, retrieval, token count, and latency, structured as nested spans — making systems whose behavior is probabilistic at least *inspectable*.**

It's distributed tracing adapted to a new failure surface: in LLM apps the bug is rarely an exception — it's a wrong retrieval at step 3, a malformed tool argument at step 7, a context that drifted. The trace is where those become visible ([the first move in agent debugging](/guides/troubleshooting/debugging-ai-agents)), and it moonlights as the system's economic ledger (cost per request, per step, per user — the raw data of [cost engineering](/guides/advanced/llm-cost-latency-engineering)) and as the quarry for [eval datasets](/glossary/eval-dataset) — yesterday's traced failure is tomorrow's regression case.

The tooling is mature: [Langfuse and LangSmith](/guides/comparisons/langfuse-vs-langsmith) lead the dedicated platforms (with Phoenix, Braintrust, and OpenTelemetry-native options around them), all converging on the same model — instrument once, then debug, monitor, and evaluate from the same captured truth. The production discipline this enables — tracing every step, scoring live traffic — is the [llm-observability-engineer](/agents/data-ai/llm-observability-engineer)'s whole brief.

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_Source: https://agentscamp.com/glossary/tracing — Term on AgentsCamp._