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/phoenix-tracing

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by githubgithub/awesome-copilot40k stars
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OpenInference semantic conventions and instrumentation for Phoenix AI observability. Use when implementing LLM tracing, creating custom spans, or deploying to production.

Use this Skill: https://skilld.dev/gh/github/awesome-copilot/phoenix-tracing

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referencessetup-python.md

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Phoenix Tracing: Python Setup

Setup Phoenix tracing in Python with arize-phoenix-otel.

Metadata

Attribute Value
Priority Critical - required for all tracing
Setup Time <5 min

Quick Start (3 lines)

from phoenix.otel import register
register(project_name="my-app", auto_instrument=True)

Connects to http://localhost:6006, auto-instruments all supported libraries.

Installation

pip install arize-phoenix-otel

Supported: Python 3.10-3.13

Configuration

Environment Variables (Recommended)

export PHOENIX_API_KEY="your-api-key"  # Required for Phoenix Cloud
export PHOENIX_COLLECTOR_ENDPOINT="http://localhost:6006"  # Or Cloud URL
export PHOENIX_PROJECT_NAME="my-app"  # Optional

Python Code

from phoenix.otel import register

tracer_provider = register(
    project_name="my-app",              # Project name
    endpoint="http://localhost:6006",   # Phoenix endpoint
    auto_instrument=True,               # Auto-instrument supported libs
    batch=True,                         # Batch processing (default: True)
)

Parameters:

  • project_name: Project name (overrides PHOENIX_PROJECT_NAME)
  • endpoint: Phoenix URL (overrides PHOENIX_COLLECTOR_ENDPOINT)
  • auto_instrument: Enable auto-instrumentation (default: False)
  • batch: Use BatchSpanProcessor (default: True, production-recommended)
  • protocol: "http/protobuf" (default) or "grpc"

Auto-Instrumentation

Install instrumentors for your frameworks:

pip install openinference-instrumentation-openai      # OpenAI SDK
pip install openinference-instrumentation-langchain   # LangChain
pip install openinference-instrumentation-llama-index # LlamaIndex
# ... install others as needed

Then enable auto-instrumentation:

register(project_name="my-app", auto_instrument=True)

Phoenix discovers and instruments all installed OpenInference packages automatically.

Batch Processing (Production)

Enabled by default. Configure via environment variables:

export OTEL_BSP_SCHEDULE_DELAY=5000           # Batch every 5s
export OTEL_BSP_MAX_QUEUE_SIZE=2048           # Queue 2048 spans
export OTEL_BSP_MAX_EXPORT_BATCH_SIZE=512     # Send 512 spans/batch

Link: https://opentelemetry.io/docs/specs/otel/configuration/sdk-environment-variables/

Verification

  1. Open Phoenix UI: http://localhost:6006
  2. Navigate to your project
  3. Run your application
  4. Check for traces (appear within batch delay)

Troubleshooting

No traces:

  • Verify PHOENIX_COLLECTOR_ENDPOINT matches Phoenix server
  • Set PHOENIX_API_KEY for Phoenix Cloud
  • Confirm instrumentors installed

Missing attributes:

  • Check span kind (see rules/ directory)
  • Verify attribute names (see rules/ directory)

Example

from phoenix.otel import register
from openai import OpenAI

# Enable tracing with auto-instrumentation
register(project_name="my-chatbot", auto_instrument=True)

# OpenAI automatically instrumented
client = OpenAI()
response = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Hello!"}]
)

API Reference

Source: SKILL.md on GitHub

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  • Gen Agent Trust Hub9d

    The phoenix-tracing skill provides comprehensive documentation and implementation guides for instrumenting LLM applications with Phoenix and OpenInference semantic conventions. It covers setup, manual and auto-instrumentation, and production best practices for both Python and TypeScript, with a strong focus on secure data handling and PII masking.

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Signed by skilld at 4214189. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub yesterday.

Activeupdated 2 months ago
Other metadata
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"
}
  • Python
  • TypeScript
  • phoenix
  • tracing
  • openinference
  • llm-observability
  • instrumentation
  • otel
  • arize

README badge

README badge for github/awesome-copilot/phoenix-tracing

Instruments LLM applications with OpenInference tracing in Phoenix, supporting auto-instrumentation of frameworks like OpenAI and LangChain or custom manual spans. Covers setup, span types (LLM calls, retrievers, agents, evaluators), production deployment with PII masking, and feedback annotation in Python and TypeScript.

Generated from the current SKILL.md.

Does this skill work with both Python and TypeScript?
Yes. The skill provides separate setup, instrumentation, and production guides for both Python (arize-phoenix-otel) and TypeScript (@arizeai/phoenix-otel).
What do I need to install to use this skill?
For Python: arize-phoenix-otel package. For TypeScript: @arizeai/phoenix-otel package. Both require a running Phoenix server endpoint.
Can I auto-instrument frameworks like OpenAI or LangChain?
Yes. The skill includes instrumentation-auto-{lang} references that cover auto-instrumentation for supported frameworks in both languages.
How do I track custom spans for my own operations?
Use the instrumentation-manual-{lang} references to create custom spans with decorators (Python) or wrappers (TypeScript), then refer to span-{type} files for the appropriate attribute schema.
Does this skill cover production deployment concerns?
Yes. The production-{lang} references address batch processing, PII masking, and deployment patterns for production environments.

Generated from the current SKILL.md. These answers refresh after source changes.