Phoenix Tracing
OfficialFreeInstrument LLM applications with OpenInference tracing.
Free · Opens the source repo
What Phoenix Tracing does
Phoenix Tracing provides a comprehensive framework for instrumenting large language model (LLM) applications using OpenInference semantic conventions. This skill is particularly useful for developers and data engineers who are implementing tracing in their LLM applications, whether in Python or TypeScript. It includes detailed guidelines and reference files that cover everything from initial setup to production deployment, ensuring that users can effectively monitor and analyze their LLM operations.
The skill is organized into several reference categories, each designed to address specific aspects of LLM tracing. Users can start by setting up their environment with the provided installation guides, then proceed to either auto-instrument their applications or create custom spans for more granular tracking. The span types available cover a wide range of operations, including LLM API calls, multi-step workflows, and document retrieval, allowing for a tailored approach to observability.
For those looking to deploy their applications in production, Phoenix Tracing includes critical resources on batch processing and PII masking, ensuring compliance and security. Additionally, users can enrich their trace data with custom metadata and utilize feedback mechanisms to evaluate the performance of their LLMs. This skill is ideal for teams focused on enhancing the observability of their AI systems and ensuring that they meet operational standards.
When to use it
Use Phoenix Tracing when setting up or deploying LLM applications that require detailed observability and instrumentation.
When not to use it
This skill may not be suitable for simple applications that do not require extensive tracing or for environments not using Phoenix or OpenInference standards.
What you can build with it
Setting Up Tracing
Follow the setup guides to configure Phoenix tracing for your LLM application in either Python or TypeScript.
Creating Custom Spans
Use the manual instrumentation resources to define custom spans tailored to your application's specific needs.
Deploying to Production
Leverage the production guidelines to ensure your LLM application is ready for live deployment with necessary observability.
How to install Phoenix Tracing
View source1. Install with the skills CLI
npx skills add github/awesome-copilot/phoenix-tracing --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by githubPhoenix 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.
- 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
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
references/attributes-* # Attribute specifications
# 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
Frequently asked questions about Phoenix Tracing
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