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

Phoenix 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](references/setup python.md) Install arize phoenix otel, configure endpoint [setup typescript](references/setup typescript.md) Install @arizeai/phoenix otel, configure endpoint 2. Instrumentation [instrumentation auto python](references/instrumentation auto python.md) Auto instrument OpenAI, LangChain, etc. [instrumentation auto typescript](references/instrumentation auto typescript.md) Auto instrument supported frameworks [instrumentation manual python](references/instrumentation manual python.md) Custom spans with decorators [instrumentation manual typescript](references/instrumentation manual typescript.md) Custom spans with wrappers 3. Span Types (with full attribute schemas) [span llm](references/span llm.md) LLM API calls (model, tokens, messages, cost) [span chain](references/span chain.md) Multi step workflows and pipelines [span retriever](references/span retriever.md) Document retrieval (documents, scores) [span tool](references/span tool.md) Function/API calls (name, parameters) [span agent](references/span agent.md) Multi step reasoning agents [span embedding](references/span embedding.md) Vector generation [span reranker](references/span reranker.md) Document re ranking [span guardrail](references/span guardrail.md) Safety checks [span evaluator](references/span evaluator.md) LLM evaluation 4. Organization [projects python](references/projects python.md) / [projects typescript](references/projects typescript.md) Group traces by application [sessions python](references/sessions python.md) / [sessions typescript](references/sessions typescript.md) Track conversations 5. Enrichment [metadata python](references/metadata python.md) / [metadata typescript](references/metadata typescript.md) Custom attributes 6. Production (CRITICAL) [production python](references/production python.md) / [production typescript](references/production typescript.md) Batch processing, PII masking 7. Feedback [annotations overview](references/annotations overview.md) Feedback concepts [annotations python](references/annotations python.md) / [annotations typescript](references/annotations typescript.md) Add feedback to spans Reference Files [fundamentals overview](references/fundamentals overview.md) Traces, spans, attributes basics [fundamentals required attributes](references/fundamentals required attributes.md) Required fields per span type [fundamentals universal attributes](references/fundamentals universal attributes.md) Common attributes (user.id, session.id) [fundamentals flattening](references/fundamentals flattening.md) 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: Reading Order: 1. Start with setup {lang} for your language 2. Choose instrumentation auto {lang} OR instrumentation manual {lang} 3. Reference span {type} files as needed for specific operations 4. See fundamentals files for attribute specifications References Phoenix Documentation: [Phoenix Documentation](https://docs.arize.com/phoenix) [OpenInference Spec](https://github.com/Arize ai/openinference/tree/main/spec) Python API Documentation: [Python OTEL Package](https://arize phoenix.readthedocs.io/projects/otel/en/latest/) arize phoenix otel API reference [Python Client Package](https://arize phoenix.readthedocs.io/projects/client/en/latest/) arize phoenix client API reference TypeScript API Documentation: [TypeScript Packages](https://arize ai.github.io/phoenix/) @arizeai/phoenix otel , @arizeai/phoenix client , and other TypeScript packages

phoenix-tracing