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Agents40k · #5265

phoenix-tracing

Helps your agent track and understand data flow in AI applications. It's useful for monitoring performance and identifying issues.

What it does

This tool helps track and understand how your AI programs work. It records each step, like a camera filming an event, so you can see what happened.

When to use it

  • ✓When setting up AI tracing.
  • ✓When creating custom steps to track in your code.
  • ✓When preparing your system for production use.

Example of what to ask your AI

“How do I set up tracing for Python?”

Want more? Open the sections below.

How to use

The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.

Skill link

https://github.com/github/awesome-copilot/blob/main/skills/phoenix-tracing/SKILL.md

Ready-to-paste message

Use this skill to help me: https://github.com/github/awesome-copilot/blob/main/skills/phoenix-tracing/SKILL.md Read the SKILL.md file at that link and follow its instructions for my task.
View on GitHub ↗Raw file
How it works

It works by logging detailed information about AI operations, such as which models were used, how long it took, and what data was processed. This creates a 'trace' that can be analyzed.

Installation

Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.

In the terminal (Claude Code, Codex, Cursor), in your project folder:

npx skills add github/awesome-copilot --skill phoenix-tracing

Or download just the file:

curl -L https://raw.githubusercontent.com/github/awesome-copilot/main/skills/phoenix-tracing/SKILL.md -o SKILL.md
Technical content (original SKILL.md)

Original text by the author, straight from GitHub.

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)

2. Instrumentation

3. Span Types (with full attribute schemas)

4. Organization

5. Enrichment

6. Production (CRITICAL)

7. Feedback

Reference Files

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

…

FAQ

What are 'spans'?

'Spans' are like individual records of a specific action within the trace, such as a call to an AI model or a data retrieval.

What is 'OpenInference'?

OpenInference is a standard that helps ensure tracing information is consistent across different tools.

Do I need to know how to code to use this?

To set up and use basic features, some programming knowledge is helpful. However, analyzing the traces can be done by someone who doesn't code.

Details

Original description by the author

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

repo: github/awesome-copilot

Popularity

GitHub stars
40k
Ranking position
#5265
License
MIT
Updated
10/15/2026

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