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Arize-ai

phoenix-tracing

by @Arize-ai in AI & LLM
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# Install this skill:
npx skills add Arize-ai/phoenix --skill "phoenix-tracing"

Install specific skill from multi-skill repository

# Description

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

# SKILL.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
metadata:
author: [email protected]
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 Attributes

Attribute Purpose Example
openinference.span.kind Span type (required) "LLM", "RETRIEVER"
input.value Operation input JSON or text
output.value Operation output JSON or text
user.id User identifier "user_123"
session.id Conversation ID "session_abc"
llm.model_name Model identifier "gpt-4"
llm.token_count.total Token usage 1500
tool.name Tool/function name "get_weather"

Common Workflows

Quick Start:

  1. setup-{lang} → Install and configure
  2. instrumentation-auto-{lang} → Enable auto-instrumentation
  3. Check Phoenix for traces

Custom Spans:

  1. setup-{lang} → Install
  2. instrumentation-manual-{lang} → Add decorators/wrappers
  3. span-{type} → Reference attributes

Production: production-{lang} → Configure batching and masking

How to Use

Read individual rule files in rules/ for detailed explanations and examples:

rules/setup-python.md
rules/instrumentation-manual-typescript.md
rules/span-llm.md

Use file prefixes to find what you need:

ls rules/span-*           # Span type specifications
ls rules/*-python.md      # Python guides
ls rules/*-typescript.md  # TypeScript guides

References

Phoenix Documentation:

Python API Documentation:

TypeScript API Documentation:

  • TypeScript Packages - @arizeai/phoenix-otel, @arizeai/phoenix-client, and other TypeScript packages

# Related Skills

# Supported AI Coding Agents

This skill is compatible with the SKILL.md standard and works with all major AI coding agents:

⚡ Amp 🚀 Antigravity 🤖 Claude Code 🦀 Clawdbot 📝 Codex ▶️ Cursor 🤖 Droid 💎 Gemini CLI 🐙 GitHub Copilot 🪿 Goose 📊 Kilo Code 🔧 Kiro CLI 💻 OpenCode 🦘 Roo Code 🌲 Trae 🏄 Windsurf

Learn more about the SKILL.md standard and how to use these skills with your preferred AI coding agent.