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* Add 9 Arize LLM observability skills Add skills for Arize AI platform covering trace export, instrumentation, datasets, experiments, evaluators, AI provider integrations, annotations, prompt optimization, and deep linking to the Arize UI. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Add 3 Phoenix AI observability skills Add skills for Phoenix (Arize open-source) covering CLI debugging, LLM evaluation workflows, and OpenInference tracing/instrumentation. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Ignoring intentional bad spelling * Fix CI: remove .DS_Store from generated skills README and add codespell ignore Remove .DS_Store artifact from winmd-api-search asset listing in generated README.skills.md so it matches the CI Linux build output. Add queston to codespell ignore list (intentional misspelling example in arize-dataset skill). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Add arize-ax and phoenix plugins Bundle the 9 Arize skills into an arize-ax plugin and the 3 Phoenix skills into a phoenix plugin for easier installation as single packages. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Fix skill folder structures to match source repos Move arize supporting files from references/ to root level and rename phoenix references/ to rules/ to exactly match the original source repository folder structures. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Fixing file locations * Fixing readme --------- Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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Overview and Traces & Spans
This document covers the fundamental concepts of OpenInference traces and spans in Phoenix.
Overview
OpenInference is a set of semantic conventions for AI and LLM applications based on OpenTelemetry. Phoenix uses these conventions to capture, store, and analyze traces from AI applications.
Key Concepts:
- Traces represent end-to-end requests through your application
- Spans represent individual operations within a trace (LLM calls, retrievals, tool invocations)
- Attributes are key-value pairs attached to spans using flattened, dot-notation paths
- Span Kinds categorize the type of operation (LLM, RETRIEVER, TOOL, etc.)
Traces and Spans
Trace Hierarchy
A trace is a tree of spans representing a complete request:
Trace ID: abc123
├─ Span 1: CHAIN (root span, parent_id = null)
│ ├─ Span 2: RETRIEVER (parent_id = span_1_id)
│ │ └─ Span 3: EMBEDDING (parent_id = span_2_id)
│ └─ Span 4: LLM (parent_id = span_1_id)
│ └─ Span 5: TOOL (parent_id = span_4_id)
Context Propagation
Spans maintain parent-child relationships via:
trace_id- Same for all spans in a tracespan_id- Unique identifier for this spanparent_id- References parent span'sspan_id(null for root spans)
Phoenix uses these relationships to:
- Build the span tree visualization in the UI
- Calculate cumulative metrics (tokens, errors) up the tree
- Enable nested querying (e.g., "find CHAIN spans containing LLM spans with errors")
Span Lifecycle
Each span has:
start_time- When the operation began (Unix timestamp in nanoseconds)end_time- When the operation completedstatus_code- OK, ERROR, or UNSETstatus_message- Optional error messageattributes- object with all semantic convention attributes