--- title: "Agent Skill to Phoenix Tracing" platform: "Promptally" canonical_url: "https://app.promptally.dev/p/skill-phoenix-tracing" modality: "SKILL" category: "Fullstack & Backend Engineering" compatible_models: [Universal / Model Agnostic] access_tier: "Free Public Artifact (No Auth Required)" license: "MIT" --- # Agent Skill to Phoenix Tracing **Summary:** Agent Skill to Phoenix Tracing is a verified SKILL blueprint on Promptally in Fullstack & Backend Engineering optimized for leading AI models. OpenInference semantic conventions and instrumentation for Phoenix AI observability. Use when implementing LLM tracing, creating custom spans, or deploying to production. Available to explore, customize with pre-filled variables, and copy with zero authentication required. ## Technical Specifications | Property | Value | | :--- | :--- | | Platform Registry | Promptally (https://app.promptally.dev) | | Modality | SKILL | | Category | Fullstack & Backend Engineering | | Compatible Engines | Universal / Model Agnostic | | Dynamic Variables | None (Static Blueprint) | | Approx. Token Length | 1,608 | | Access Tier | Free Public Artifact (No Auth Required) | | License | MIT | ### Pre-Filled Template & Production Source ````md --- 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 compatibility: Requires Phoenix server. Python skills need arize-phoenix-otel; TypeScript skills need @arizeai/phoenix-otel. 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 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:** ```bash # 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 references/attributes-* # Attribute specifications # By language references/*-python.md # Python implementations references/*-typescript.md # TypeScript implementations ``` **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 ```` ## Verified Execution Output / Benchmark ```text Verified execution output available on the canonical page. ``` ## Related Blueprints on Promptally - [Agent Skill to Knowledge Ops](https://app.promptally.dev/p/skill-knowledge-ops) - [Agent Skill to Content Hash Cache Pattern](https://app.promptally.dev/p/skill-content-hash-cache-pattern) - [Agent Skill to Ck](https://app.promptally.dev/p/skill-ck) - [Agent Skill to Swift Protocol Di Testing](https://app.promptally.dev/p/skill-swift-protocol-di-testing) - [Agent Skill to Prisma Patterns](https://app.promptally.dev/p/skill-prisma-patterns) - [Agent Skill to Agentic Engineering](https://app.promptally.dev/p/skill-agentic-engineering) - [Agent Skill to Python Patterns](https://app.promptally.dev/p/skill-python-patterns) - [Agent Skill to Docs Governance](https://app.promptally.dev/p/skill-docs-governance)