llm-tracekit-openai 1.3.3

OpenTelemetry instrumentation for OpenAI.

LLM Tracekit - OpenAI

OpenTelemetry instrumentation for OpenAI, designed to simplify LLM application development and production tracing and debugging.

Installation

OpenAI

pip install "llm-tracekit-openai"

Usage

This section describes how to set up instrumentation. The examples will use the OpenAI instrumentation, but the usage is similar for all instrumentations.

Setting up tracing

You can use the setup_export_to_coralogix function to setup tracing and export traces to Coralogix

from llm_tracekit.openai import setup_export_to_coralogix

setup_export_to_coralogix(
    service_name="ai-service",
    application_name="ai-application",
    subsystem_name="ai-subsystem",
    capture_content=True,
)

Alternatively, you can set up tracing manually:

from opentelemetry import trace
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.resources import SERVICE_NAME, Resource
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import SimpleSpanProcessor

tracer_provider = TracerProvider(
    resource=Resource.create({SERVICE_NAME: "ai-service"}),
)
exporter = OTLPSpanExporter()
span_processor = SimpleSpanProcessor(exporter)
tracer_provider.add_span_processor(span_processor)
trace.set_tracer_provider(tracer_provider)

Activation

To instrument all clients, call the instrument method

from llm_tracekit import OpenAIInstrumentor

OpenAIInstrumentor().instrument()

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 span attributes, do one of the following:

• Pass capture_content=True when calling setup_export_to_coralogix
• Set the environment variable OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT to true

Most Coralogix AI evaluations will not work without message contents, so it is highly recommended to enable capturing.

Uninstrument

To uninstrument clients, call the uninstrument method:

OpenAIInstrumentor().uninstrument()

Full Example

from llm_tracekit.openai import OpenAIInstrumentor, setup_export_to_coralogix
from openai import OpenAI

# Optional: Configure sending spans to Coralogix
# Reads Coralogix connection details from the following environment variables:
# - CX_TOKEN
# - CX_ENDPOINT
setup_export_to_coralogix(
    service_name="ai-service",
    application_name="ai-application",
    subsystem_name="ai-subsystem",
    capture_content=True,
)

# Activate instrumentation
OpenAIInstrumentor().instrument()

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

Responses API

Example Response API call

from openai import OpenAI

client = OpenAI()
response = client.responses.create(
    model="gpt-4o-mini",
    input="Write a haiku about OpenTelemetry.",
)

from openai import OpenAI

client = OpenAI()
with client.responses.stream(
    model="gpt-4o-mini",
    input="Write a haiku about OpenTelemetry.",
) as stream:
    for event in stream:
        pass
    response = stream.get_final_response()

Embeddings Example

from openai import OpenAI

client = OpenAI()
response = client.embeddings.create(
    model="text-embedding-3-small",
    input="What is machine learning?",
)
print(f"Embedding dimensions: {len(response.data[0].embedding)}")

Changes from OpenTelemetry

General

• The user parameter in the OpenAI Chat Completions API is now recorded in the span as the gen_ai.request.user attribute
• User prompts and model responses are captured as span attributes instead of log events (see Semantic Conventions below)

Semantic Conventions

Attribute	Type	Description	Examples
gen_ai.prompt.<message_number>.role	string	Role of message author for user message <message_number>	system, user, assistant, tool
gen_ai.prompt.<message_number>.content	string	Contents of user message <message_number>	What's the weather in Paris?
gen_ai.prompt.<message_number>.tool_calls.<tool_call_number>.id	string	ID of tool call in user message <message_number>	call_O8NOz8VlxosSASEsOY7LDUcP
gen_ai.prompt.<message_number>.tool_calls.<tool_call_number>.type	string	Type of tool call in user message <message_number>	function
gen_ai.prompt.<message_number>.tool_calls.<tool_call_number>.function.name	string	The name of the function used in tool call within user message <message_number>	get_current_weather
gen_ai.prompt.<message_number>.tool_calls.<tool_call_number>.function.arguments	string	Arguments passed to the function used in tool call within user message <message_number>	{"location": "Seattle, WA"}
gen_ai.prompt.<message_number>.tool_call_id	string	Tool call ID in user message <message_number>	call_mszuSIzqtI65i1wAUOE8w5H4
gen_ai.completion.<choice_number>.role	string	Role of message author for choice <choice_number> in model response	assistant
gen_ai.completion.<choice_number>.finish_reason	string	Finish reason for choice <choice_number> in model response	stop, tool_calls, error
gen_ai.completion.<choice_number>.content	string	Contents of choice <choice_number> in model response	The weather in Paris is rainy and overcast, with temperatures around 57°F
gen_ai.completion.<choice_number>.tool_calls.<tool_call_number >.id	string	ID of tool call in choice <choice_number>	call_O8NOz8VlxosSASEsOY7LDUcP
gen_ai.completion.<choice_number>.tool_calls.<tool_call_number >.type	string	Type of tool call in choice <choice_number>	function
gen_ai.completion.<choice_number>.tool_calls.<tool_call_number >.function.name	string	The name of the function used in tool call within choice <choice_number>	get_current_weather
gen_ai.completion.<choice_number>.tool_calls.<tool_call_number >.function.arguments	string	Arguments passed to the function used in tool call within choice <choice_number>	{"location": "Seattle, WA"}
gen_ai.request.tools.<tool_number>.type	string	Type of tool entry in tools list	function
gen_ai.request.tools.<tool_number>.function.name	string	The name of the function to use in tool calls	get_current_weather
gen_ai.request.tools.<tool_number>.function.description	string	Description of the function	Get the current weather in a given location
gen_ai.request.tools.<tool_number>.function.parameters	string	JSON describing the schema of the function parameters	{"type": "object", "properties": {"location": {"type": "string", "description": "The city and state, e.g. San Francisco, CA"}, "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}}, "required": ["location"]}
gen_ai.request.user	string	A unique identifier representing the end-user	[e-mail]
gen_ai.embeddings.dimension.count	int	Requested output dimensionality for embeddings	256
gen_ai.embeddings.<n>.vector	array	The embedding vector values (when content capture enabled)	[0.1, 0.2, ...]

Function spans

These spans represent the execution of a tool (a Python function).

Attribute	Type	Description	Example
type	string	The type of the span, identifying it as a function.	function
name	string	The name of the function that was called.	get_current_weather
input	string	The JSON string of arguments passed to the function.	{"city":"Tel Aviv"}
output	string	The string representation of the function's return value.	The weather in Tel Aviv is 30°C and sunny.

Enriched LLM call spans

These attributes are added to the existing span to link LLM calls back to the responsible agent.

Attribute	Type	Description	Example
gen_ai.agent.name	string	The name of the agent that initiated this LLM call.	Assistant, WeatherAgent