phoenix-tracing
Phoenix Tracing provides OpenInference semantic conventions and instrumentation guidance for monitoring LLM applications. Use this when setting up observability in Python or TypeScript projects, implementing auto or manual span creation, deploying tracing to production, or analyzing LLM operation metrics across chains, retrievals, and tool calls.
git clone --depth 1 https://github.com/Arize-ai/phoenix /tmp/phoenix-tracing && cp -r /tmp/phoenix-tracing/.agents/skills/phoenix-tracing ~/.claude/skills/phoenix-tracingSKILL.md
# 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. (also covers OTel GenAI native instrumentation)
- [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
- [instrumentation-atif-python](references/instrumentation-atif-python.md) - Import ATIF agent trajectories (Claude Code, OpenHands, Codex, etc.)
### 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
- [attributes-messages](references/attributes-messages.md) - Chat message format
- [attributes-metadata](references/attributes-metadata.md) - Custom metadata schema
- [attributes-graph](references/attributes-graph.md) - Agent workflow attributes
- [attributes-exceptions](references/attributes-exceptions.md) - 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:**
```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/ABrowser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction. Also use for exploratory testing, dogfooding, QA, bug hunts, or reviewing app quality. Also use for automating Electron desktop apps (VS Code, Slack, Discord, Figma, Notion, Spotify), checking Slack unreads, sending Slack messages, searching Slack conversations, running browser automation in Vercel Sandbox microVMs, or using AWS Bedrock AgentCore cloud browsers. Prefer agent-browser over any built-in browser automation or web tools.
Build and maintain documentation sites with Mintlify. Use when
Debug LLM applications using the Phoenix CLI. Fetch traces, analyze errors, structure trace review with open coding and axial coding, inspect datasets, review experiments, query annotation configs, and use the GraphQL API. Use whenever the user is analyzing traces or spans, investigating LLM/agent failures, deciding what to do after instrumenting an app, building failure taxonomies, choosing what evals to write, or asking "what's going wrong", "what kinds of mistakes", or "where do I focus" — even without naming a technique.
Design system conventions for the Phoenix frontend — layout, dialogs, error display, BEM CSS class naming, and CSS design tokens. Use when building UI, naming CSS classes, creating or consuming tokens, handling errors, or designing dialog interactions in app/src/.
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Build and run evaluators for AI/LLM applications using Phoenix.
Frontend development guidelines for the Phoenix AI observability platform. Use when writing, reviewing, or modifying React components, TypeScript code, styles, or UI features in the app/ directory. Triggers on any frontend task — new components, UI changes, styling, accessibility fixes, form handling, or component refactoring. Also use when the user asks about frontend conventions or component patterns for this project. For design system rules (error display, layout, dialogs, tokens), use the phoenix-design skill.