| name | phoenix-tracing |
| description | OpenInference semantic conventions and instrumentation for Phoenix AI observability. Use when implementing LLM tracing, creating custom spans, or deploying to production. |
| license | Apache-2.0 |
| metadata | {"author":"oss@arize.com","version":"1.0.0","languages":"Python, TypeScript"} |
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
| 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.
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 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:
rules/setup-*
rules/instrumentation-*
rules/span-*
rules/sessions-*
rules/production-*
rules/fundamentals-*
rules/attributes-*
rules/*-python.md
rules/*-typescript.md
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:
TypeScript API Documentation:
- TypeScript Packages -
@arizeai/phoenix-otel, @arizeai/phoenix-client, and other TypeScript packages