opentelemetry-instrumentation-openai-v2 2.1b0

OpenTelemetry Official OpenAI instrumentation

This library allows tracing LLM requests and logging of messages made by the OpenAI Python API library. It also captures the duration of the operations and the number of tokens used as metrics.

Installation

If your application is already instrumented with OpenTelemetry, add this package to your requirements.

pip install opentelemetry-instrumentation-openai-v2

If you don’t have an OpenAI application, yet, try our examples which only need a valid OpenAI API key.

Check out zero-code example for a quick start.

Usage

This section describes how to set up OpenAI instrumentation if you’re setting OpenTelemetry up manually. Check out the manual example for more details.

Instrumenting all clients

When using the instrumentor, all clients will automatically trace OpenAI chat completion operations. You can also optionally capture prompts and completions as log events.

Make sure to configure OpenTelemetry tracing, logging, and events to capture all telemetry emitted by the instrumentation.

from opentelemetry.instrumentation.openai_v2 import OpenAIInstrumentor

OpenAIInstrumentor().instrument()

client = OpenAI()
response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[
        {"role": "user", "content": "Write a short poem on open telemetry."},
    ],
)

Enabling message content

Message content such as the contents of the prompt, completion, function arguments and return values are not captured by default. To capture message content as log events, set the environment variable OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT to true.

Uninstrument

To uninstrument clients, call the uninstrument method:

from opentelemetry.instrumentation.openai_v2 import OpenAIInstrumentor

OpenAIInstrumentor().instrument()
# ...

# Uninstrument all clients
OpenAIInstrumentor().uninstrument()

Bucket Boundaries

This section describes the explicit bucket boundaries for metrics such as token usage and operation duration, and guides users to create Views to implement them according to the semantic conventions.

The bucket boundaries are defined as follows:

• For gen_ai.client.token.usage: [1, 4, 16, 64, 256, 1024, 4096, 16384, 65536, 262144, 1048576, 4194304, 16777216, 67108864]

• For gen_ai.client.operation.duration: [0.01, 0.02, 0.04, 0.08, 0.16, 0.32, 0.64, 1.28, 2.56, 5.12, 10.24, 20.48, 40.96, 81.92]

To implement these bucket boundaries, you can create Views in your OpenTelemetry SDK setup. Here is an example:

from opentelemetry.sdk.metrics import MeterProvider, View
from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader
from opentelemetry.exporter.otlp.proto.grpc.metric_exporter import OTLPMetricExporter
from opentelemetry.sdk.metrics.aggregation import ExplicitBucketHistogramAggregation

views = [
    View(
        instrument_name="gen_ai.client.token.usage",
        aggregation=ExplicitBucketHistogramAggregation([1, 4, 16, 64, 256, 1024, 4096, 16384, 65536, 262144, 1048576, 4194304, 16777216, 67108864]),
    ),
    View(
        instrument_name="gen_ai.client.operation.duration",
        aggregation=ExplicitBucketHistogramAggregation([0.01, 0.02, 0.04, 0.08, 0.16, 0.32, 0.64, 1.28, 2.56, 5.12, 10.24, 20.48, 40.96, 81.92]),
    ),
]

metric_exporter = OTLPMetricExporter(endpoint="http://localhost:4317")
metric_reader = PeriodicExportingMetricReader(metric_exporter)
provider = MeterProvider(
    metric_readers=[metric_reader],
    views=views
)

from opentelemetry.sdk.metrics import set_meter_provider
set_meter_provider(provider)

For more details, refer to the OpenTelemetry GenAI Metrics documentation.

References

• OpenTelemetry OpenAI Instrumentation

• OpenTelemetry Project

• OpenTelemetry Python Examples