Agent Skill to Phoenix Tracing
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
| Specification | Value |
|---|---|
| Artifact type | SKILL (Fullstack & Backend Engineering) |
| Compatible engines | Universal / Model Agnostic |
| Input variables | None (pre-filled) |
| Approximate tokens | 1,608 |
| Source file | SKILL.md |
| Access tier | Free public (MIT) |
Pre-Filled Template
---
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
How to use this skill
- Review the pre-filled example and source files above.
- Copy the template or download
SKILL.md, then use it in your model, IDE, or workflow tool. - Compare your result with the sample output when one is provided.
Frequently Asked Questions
How do I use Agent Skill to Phoenix Tracing?
Open the skill on Promptally, review the pre-filled example, then copy the template or download SKILL.md and paste it into your model, IDE or workflow tool. Agent Skill to Phoenix Tracing is a static blueprint with no dynamic variables. Copy it and use it as-is.
Is Agent Skill to Phoenix Tracing free?
Yes. Agent Skill to Phoenix Tracing is free to view, copy and download on Promptally without creating an account. It is published under the MIT license.
What variables does Agent Skill to Phoenix Tracing need?
Agent Skill to Phoenix Tracing is a static blueprint with no dynamic variables. Copy it and use it as-is.