llm-tracekit-openai-agents 1.2.0

OpenTelemetry instrumentation for OpenAI Agents SDK.

LLM Tracekit - OpenAI Agents SDK

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

Installation

pip install "llm-tracekit-openai-agents"

Usage

This section describes how to set up instrumentation for the OpenAI Agents SDK.

Setting up tracing

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

from llm_tracekit.openai_agents 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.openai_agents import OpenAIAgentsInstrumentor

OpenAIAgentsInstrumentor().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:

OpenAIAgentsInstrumentor().uninstrument()

Full Example

from agents import Agent, Runner
from llm_tracekit.openai_agents import OpenAIAgentsInstrumentor, setup_export_to_coralogix

# 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
OpenAIAgentsInstrumentor().instrument()

# Example OpenAI Agents Usage
agent = Agent(name="Assistant", instructions="You are a helpful assistant.")
result = Runner.run_sync(agent, input="Write a short poem on open telemetry.")
print(result.final_output)

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 definition advertised to the model	function
gen_ai.request.tools.<tool_number>.function.name	string	Name of the tool/function exposed to the model	get_current_weather
gen_ai.request.tools.<tool_number>.function.description	string	Description of the tool/function when provided by the SDK response payload	Get current weather for a city.
gen_ai.request.tools.<tool_number>.function.parameters	string	JSON schema describing the tool/function parameters passed with the request	{"type": "object", "properties": {"city": {"type": "string"}}}

OpenAI Agents SDK specific attributes

Agent spans

These spans represent the execution of a single agent. They act as parents for LLM calls, guardrails, and handoffs initiated by that agent.

Attribute	Type	Description	Example
type	string	The type of the span, identifying it as an agent execution.	agent
agent_name	string	The name of the agent being executed.	Assistant
handoffs	string[]	A list of other agents that this agent is capable of handing off to.	["WeatherAgent"]
tools	string[]	A list of tool names available to the agent.	["get_current_weather"]
output_type	string	The expected data type of the agent's final output.	MessageOutput

Guardrail spans

These spans represent the execution of a guardrail check.

Attribute	Type	Description	Example
type	string	The type of the span, identifying it as a guardrail.	guardrail
name	string	The unique name of the guardrail being executed.	MathGuardrail
triggered	boolean	Indicates whether the guardrail condition was met (and triggered).	false

Handoff spans

These spans represent the moment an agent attempts to delegate a task to another agent.

Handling Multiple Handoffs: If the LLM attempts to hand off to multiple agents in a single turn, the to_agent attribute will only contain the name of the first agent in the list. The span will also be marked with an error status to indicate this ambiguity.

Attribute	Type	Description	Example
type	string	The type of the span, identifying it as a handoff.	handoff
from_agent	string	The name of the agent initiating the handoff.	Assistant
to_agent	string	The name of the agent intended to receive the handoff.	WeatherAgent

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