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phoenix-tracing

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OpenInference semantic conventions and instrumentation for Phoenix AI observability. Use when implementing LLM tracing, creating custom spans, or deploying to production.

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Updated 2/5/2026

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SKILL.md

Phoenix Tracing

Comprehensive guide for instrumenting LLM applications with OpenInference tracing in Phoenix. Contains rule files covering setup, instrumentation, span types, and production deployment.

When to Apply

Reference these guidelines when:

  • Setting up Phoenix tracing (Python or TypeScript)
  • Creating custom spans for LLM operations
  • Adding attributes following OpenInference conventions
  • Deploying tracing to production
  • Querying and analyzing trace data

Rule Categories

PriorityCategoryDescriptionPrefix
1SetupInstallation and configurationsetup-*
2InstrumentationAuto and manual tracinginstrumentation-*
3Span Types9 span kinds with attributesspan-*
4OrganizationProjects and sessionsprojects-*, sessions-*
5EnrichmentCustom metadatametadata-*
6ProductionBatch processing, maskingproduction-*
7FeedbackAnnotations and evaluationannotations-*

Quick Reference

1. Setup (START HERE)

  • setup-python - Install arize-phoenix-otel, configure endpoint
  • setup-typescript - Install @arizeai/phoenix-otel, configure endpoint

2. Instrumentation

  • instrumentation-auto-python - Auto-instrument OpenAI, LangChain, etc.
  • instrumentation-auto-typescript - Auto-instrument supported frameworks
  • instrumentation-manual-python - Custom spans with decorators
  • instrumentation-manual-typescript - Custom spans with wrappers

3. Span Types (with full attribute schemas)

  • span-llm - LLM API calls (model, tokens, messages, cost)
  • span-chain - Multi-step workflows and pipelines
  • span-retriever - Document retrieval (documents, scores)
  • span-tool - Function/API calls (name, parameters)
  • span-agent - Multi-step reasoning agents
  • span-embedding - Vector generation
  • span-reranker - Document re-ranking
  • span-guardrail - Safety checks
  • span-evaluator - LLM evaluation

4. Organization

  • projects-python / projects-typescript - Group traces by application
  • sessions-python / sessions-typescript - Track conversations

5. Enrichment

  • metadata-python / metadata-typescript - Custom attributes

6. Production (CRITICAL)

  • production-python / production-typescript - Batch processing, PII masking

7. Feedback

  • annotations-overview - Feedback concepts
  • annotations-python / annotations-typescript - Add feedback to spans

Reference Files

  • fundamentals-overview - Traces, spans, attributes basics
  • fundamentals-required-attributes - Required fields per span type
  • fundamentals-universal-attributes - Common attributes (user.id, session.id)
  • fundamentals-flattening - JSON flattening rules
  • attributes-messages - Chat message format
  • attributes-metadata - Custom metadata schema
  • attributes-graph - Agent workflow attributes
  • attributes-exceptions - Error tracking

Common Attributes

AttributePurposeExample
openinference.span.kindSpan type (required)"LLM", "RETRIEVER"
input.valueOperation inputJSON or text
output.valueOperation outputJSON or text
user.idUser identifier"user_123"
session.idConversation ID"session_abc"
llm.model_nameModel identifier"gpt-4"
llm.token_count.totalToken usage1500
tool.nameTool/function name"get_weather"

Common Workflows

Quick Start:

  1. setup-{lang} → Install and configure
  2. instrumentation-auto-{lang} → Enable auto-instrumentation
  3. Check Phoenix for traces

Custom Spans:

  1. setup-{lang} → Install
  2. instrumentation-manual-{lang} → Add decorators/wrappers
  3. span-{type} → Reference attributes

Production: production-{lang} → Configure batching and masking

How to Use

Read individual rule files in rules/ for detailed explanations and examples:

rules/setup-python.md
rules/instrumentation-manual-typescript.md
rules/span-llm.md

Use file prefixes to find what you need:

ls rules/span-*           # Span type specifications
ls rules/*-python.md      # Python guides
ls rules/*-typescript.md  # TypeScript guides

References

Phoenix Documentation:

Python API Documentation:

TypeScript API Documentation:

  • TypeScript Packages - @arizeai/phoenix-otel, @arizeai/phoenix-client, and other TypeScript packages

Install

Download ZIP
Requires askill CLI v1.0+

AI Quality Score

94/100Analyzed 2/12/2026

An excellent technical reference for Phoenix AI observability. It serves as a well-structured entry point for OpenInference tracing, providing clear workflows, attribute schemas, and production considerations for both Python and TypeScript.

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Metadata

Licenseunknown
Version-
Updated2/5/2026
Publishermajiayu000

Tags

apigithubgithub-actionsllmobservability