OpenInference OpenLit Instrumentation

Python auto-instrumentation library for OpenLIT. This library allows you to convert OpenLIT traces to OpenInference, which is OpenTelemetry compatible, and view those traces in Arize Phoenix or Arize AX.

Installation

pip install openinference-instrumentation-openlit

Quickstart

This quickstart shows you how to view your OpenLIT traces in Phoenix.

Install required packages.

pip install arize-phoenix opentelemetry-sdk opentelemetry-exporter-otlp openlit semantic-kernel

Start Phoenix in the background as a collector. By default, it listens on http://localhost:6006. You can visit the app via a browser at the same address.

phoenix serve

Here's a simple example that demonstrates how to convert OpenLIT traces into OpenInference and view those traces in Phoenix:

import os
import grpc
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter
from phoenix.otel import register
from openinference.instrumentation.openlit import OpenInferenceSpanProcessor
from semantic_kernel import Kernel
from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion
import openlit

# Set your OpenAI API key
os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"

# Set up the tracer provider
tracer_provider = register(
    project_name="default" #Phoenix project name
)

tracer_provider.add_span_processor(OpenInferenceSpanProcessor())
    
tracer_provider.add_span_processor(
    BatchSpanProcessor(
        OTLPSpanExporter(
            endpoint="http://localhost:4317", #if using phoenix cloud, change to phoenix cloud endpoint (phoenix cloud space -> settings -> endpoint/hostname)
            headers={},
            compression=grpc.Compression.Gzip,  # use enum instead of string
        )
    )
)

# Initialize OpenLit tracer
tracer = tracer_provider.get_tracer(__name__)
openlit.init(tracer=tracer)

# Set up Semantic Kernel with OpenLIT
kernel = Kernel()
kernel.add_service(
    OpenAIChatCompletion(
        service_id="default",
        ai_model_id="gpt-4o-mini",
    ),
)

# Define and invoke your model
result = await kernel.invoke_prompt(
    prompt="What is the national food of Yemen?",
    arguments={},
)

# Now view your converted OpenLIT traces in Phoenix!

This example:

1. Uses OpenLIT Instrumentor to instrument the application.
2. Defines a simple Semantic Kernel model and runs a query
3. Queries are exported to Phoenix using a span processor.

The traces will be visible in the Phoenix UI at http://localhost:6006.

More Info

• More info on OpenInference and Phoenix
• More info on OpenInference and Arize AX
• How to customize spans to track sessions, metadata, etc.
• How to account for private information and span payload customization