About phoenix-tracing
phoenix-tracing is a reference skill for instrumenting LLM applications with OpenInference tracing in Phoenix. It helps developers configure tracing, define spans, and prepare trace data for production analysis across Python and TypeScript.
Key features
- 01Python and TypeScript setup guidance
- 02Automatic and manual instrumentation
- 03Span types and attribute schemas
- 04Projects, sessions, and metadata
- 05Production batching and PII masking
- 06Span annotations and feedback
Use cases
- 01Adding tracing to a Python or TypeScript LLM application
- 02Creating custom spans for LLM operations
- 03Grouping traces by project or conversation
- 04Preparing trace collection for production
Install
Copy the command and run it in a terminal.
$
npx skills add https:/After installing, type/phoenix-tracingin Claude Code to confirm it loaded.
Bundled files
References and scripts installed along with SKILL.md.
README.mdreferences/annotations-overview.mdannotations-python.mdannotations-typescript.mdfundamentals-flattening.mdfundamentals-overview.mdfundamentals-required-attributes.mdfundamentals-universal-attributes.mdinstrumentation-atif-python.mdinstrumentation-auto-python.mdinstrumentation-auto-typescript.mdinstrumentation-manual-python.mdinstrumentation-manual-typescript.mdmetadata-python.mdmetadata-typescript.mdproduction-python.mdproduction-typescript.mdprojects-python.mdprojects-typescript.mdsessions-python.mdsessions-typescript.mdsetup-python.mdsetup-typescript.mdspan-agent.mdspan-chain.mdspan-decision.mdspan-embedding.mdspan-evaluator.mdspan-guardrail.mdspan-llm.mdDetails
From the project READMEPhoenix Tracing
Comprehensive guide for instrumenting LLM applications with OpenInference tracing in Phoenix. Contains reference 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
Reference Categories
| Priority | Category | Description | Prefix |
|---|---|---|---|
| 1 | Setup | Installation and configuration | setup-* |
| 2 | Instrumentation | Auto and manual tracing | instrumentation-* |
| 3 | Span Types | 9 span kinds with attributes | span-* |
| 4 | Organization | Projects and sessions | projects-*, sessions-* |
| 5 | Enrichment | Custom metadata | metadata-* |
| 6 | Production | Batch processing, masking | production-* |
| 7 | Feedback | Annotations and evaluation | annotations-* |
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. (also covers OTel GenAI native instrumentation)
- instrumentation-auto-typescript - Auto-instrument supported frameworks
- instrumentation-manual-python - Custom spans with decorators
- instrumentation-manual-typescript - Custom spans with wrappers
- instrumentation-atif-python - Import ATIF agent trajectories (Claude Code, OpenHands, Codex, etc.)
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
- span-decision - Decision model calls (routing, classification, scoring) — experimental
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
Common Workflows
- Quick Start: setup-{lang} → instrumentation-auto-{lang} → Check Phoenix
- Custom Spans: setup-{lang} → instrumentation-manual-{lang} → span-{type}
- Session Tracking: sessions-{lang} for conversation grouping patterns
- Production: production-{lang} for batching, masking, and deployment
How to Use This Skill
Navigation Patterns:
# By category prefix
references/setup-* # Installation and configuration
references/instrumentation-* # Auto and manual tracing
references/span-* # Span type specifications
references/sessions-* # Session tracking
references/production-* # Production deployment
references/fundamentals-* # Core concepts
# By language
references/*-python.md # Python implementations
references/*-typescript.md # TypeScript implementations
Reading Order:
- Start with setup-{lang} for your language
- Choose instrumentation-auto-{lang} OR instrumentation-manual-{lang}
- Reference span-{type} files as needed for specific operations
- See fundamentals-* files for attribute specifications
References
Phoenix Documentation:
Python API Documentation:
- Python OTEL Package -
arize-phoenix-otelAPI reference - Python Client Package -
arize-phoenix-clientAPI reference
TypeScript API Documentation:
- TypeScript Packages -
@arizeai/phoenix-otel,@arizeai/phoenix-client, and other TypeScript packages
Comments
Markdown supported