OpenTelemetry organizes telemetry into a handful of signals — the three you’ll encounter most often are traces, metrics, and logs. Traces are the focus for AI applications: they’re how you see what your app did, step by step, for a single request. Spans are the building blocks of a trace. Traces group into sessions when you’re tracking multi-turn conversations. This page covers the data shapes. The rest of the pages in this section cover the OTel and OpenInference machinery that produces and ships them.

Signals

A signal is a category of telemetry data. The three most-used signals in OpenTelemetry today:

Signal	What it is	Used to track
Traces	The path of a request through your application. A span is the unit of work that, with its peers, forms a tree (the trace).	LLM calls, tool invocations, retrievals, agent steps.
Metrics	A measurement captured at runtime.	Aggregates like latency percentiles, request counts, token usage over time.
Logs	A recording of an event.	One-off events, errors, audit entries.

Each signal has its own exporter and provider. The OpenTelemetry specification also defines baggage as a signal, with events and profiles in development — see the OpenTelemetry signals documentation for the current full set. Arize AX focuses on traces, since traces are what make AI application behavior legible.

Span Structure

A span is a structured log representing a single unit of work — one LLM call, one tool invocation, one retrieval. It’s the atomic unit of a trace, and it carries everything you need to understand what happened:

Field	Description
Name	A short label for the operation (e.g. `chat.completion`, `retrieve_docs`).
Start and end timestamps	When the operation began and finished.
Span context	An immutable object representing the span’s identity. Contains the span ID, trace ID, trace flags, and trace state.
Parent span ID	The ID of the parent span. Empty for a root span.
Attributes	Key-value pairs describing what happened. This is where the OpenInference semantic conventions live (`llm.model_name`, `llm.input_messages`, `input.value`, etc.).
Span events	Structured log messages attached to a span at a specific moment.
Span links	Pointers to related spans in other traces.
Span status	`Ok`, `Error`, or `Unset`.
Span kind	The category of operation. OTel defines its own kinds for network spans; OpenInference adds AI-specific kinds — see Span Kinds.

For the canonical specification, see Span Structure.

Spans, Traces, and Sessions

Spans, traces, and sessions form a three-level hierarchy. The analogy that often helps:

A session is a conversation. A trace is one turn — one user input, one bot output. The spans inside the trace are the steps that produced that output.

Span

The individual step the application takes. Spans can be nested via parent_id:

Child span — parent_id equals the span ID of another span in the same trace.
Root span — parent_id is null. The top of the tree.
Orphan span — parent_id references a span that doesn’t exist in the project. Usually a sign of incomplete instrumentation or context propagation gone wrong.

Arize AX UI behavior: Arize AX reads the trace-level input and output from the root span. If those columns are empty on the root, the trace list shows blank I/O — even when child spans have data. Configure source mapping or set input.value / output.value on the root during instrumentation. This matters most for manually built traces; auto-instrumentors usually work without custom mapping.

Trace

A collection of spans sharing the same trace_id. One trace represents the end-to-end work for a single request — a single user turn in a chat, a single API call, a single agent run. Trace in Arize AX from the OpenAI Agents SDK example, showing agent, chain, tool, and LLM spans nested together

Session

A collection of traces sharing the same session.id. A session typically represents a multi-turn conversation — every user message and every bot response across that conversation is its own trace, all linked together by a shared session ID. You set session.id on your spans either:

via the using_session context manager, or
by setting the session.id attribute directly on each span.

For practical setup, see Set up sessions.

How They Fit Together in a Trace

In a typical AI application, a single user turn might produce a trace that looks like this:

Session (session.id = "user-42-conv-7")
└── Trace (one user turn)
    └── Agent span                                         [root]
        ├── LLM span        "decide which tool to call"
        ├── Tool span       "search the knowledge base"
        │   └── Retriever span
        └── LLM span        "answer using retrieved docs"

Each span has its own attributes (model name, input/output, token counts), its own status, and its own start/end time. The tree structure comes entirely from parent_id references, and the Arize AX UI renders it visually so you can drill into any span to see exactly what happened.

Next step

You know what spans, traces, and sessions are. Next, the first of the four OTel tracing components — the Resource, which describes who is producing the telemetry:

Signals, Spans, Traces, and Sessions

Signals

Span Structure

Spans, Traces, and Sessions

Span

Trace

Session

How They Fit Together in a Trace

Next step

Next: Resource

​Signals

​Span Structure

​Spans, Traces, and Sessions

​Span

​Trace

​Session

​How They Fit Together in a Trace

​Next step

Next: Resource

Signals

Span Structure

Spans, Traces, and Sessions

Span

Trace

Session

How They Fit Together in a Trace

Next step