awesome-copilot/plugins/phoenix/skills/phoenix-tracing/references/instrumentation-auto-python.md

Phoenix Tracing: Auto-Instrumentation (Python)

Automatically create spans for LLM calls without code changes.

Overview

Auto-instrumentation patches supported libraries at runtime to create spans automatically. Use for supported frameworks (LangChain, LlamaIndex, OpenAI SDK, etc.). For custom logic, manual-instrumentation-python.md.

Supported Frameworks

Python:

• LLM SDKs: OpenAI, Anthropic, Bedrock, Mistral, Vertex AI, Groq, Ollama
• Frameworks: LangChain, LlamaIndex, DSPy, CrewAI, Instructor, Haystack
• Install: pip install openinference-instrumentation-{name}

Setup

Install and enable:

pip install arize-phoenix-otel
pip install openinference-instrumentation-openai  # Add others as needed

from phoenix.otel import register

register(project_name="my-app", auto_instrument=True)  # Discovers all installed instrumentors

Example:

from phoenix.otel import register
from openai import OpenAI

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

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

Traces appear in Phoenix UI with model, input/output, tokens, timing automatically captured. See span kind files for full attribute schemas.

Selective instrumentation (explicit control):

from phoenix.otel import register
from openinference.instrumentation.openai import OpenAIInstrumentor

tracer_provider = register(project_name="my-app")  # No auto_instrument
OpenAIInstrumentor().instrument(tracer_provider=tracer_provider)

Limitations

Auto-instrumentation does NOT capture:

• Custom business logic
• Internal function calls

Example:

def my_custom_workflow(query: str) -> str:
    preprocessed = preprocess(query)  # Not traced
    response = client.chat.completions.create(...)  # Traced (auto)
    postprocessed = postprocess(response)  # Not traced
    return postprocessed

Solution: Add manual instrumentation:

@tracer.chain
def my_custom_workflow(query: str) -> str:
    preprocessed = preprocess(query)
    response = client.chat.completions.create(...)
    postprocessed = postprocess(response)
    return postprocessed