Google ADK
Coralogix's AI Observability integration for Google ADK (Agent Development Kit) provides production-ready tracing for agent workflows built on Gemini. Instrument your ADK agents with a single call to gain full visibility into prompts, completions, tool calls, and performance metrics.
Requirements
- Python 3.10–3.13.
- Coralogix API keys.
Installation
Run the following command.
Authentication
Authentication data is passed during OTel Span Exporter definition:
- Choose the ingress.:443 endpoint that corresponds to your Coralogix domain using the domain selector at the top of the page.
- Use your customized API key in the authorization request header.
- Provide the application and subsystem names.
from llm_tracekit.google_adk import setup_export_to_coralogix
setup_export_to_coralogix(
coralogix_token=<your_coralogix_token>,
coralogix_endpoint="ingress.:443",
service_name="ai-service",
application_name="ai-application",
subsystem_name="ai-subsystem",
capture_content=True,
)
Note
All of the authentication parameters can also be provided through environment variables (CX_TOKEN, CX_ENDPOINT, etc.).
Usage
This section describes how to set up instrumentation for Google ADK.
Set up tracing
Automatic
Use the setup_export_to_coralogix function to set up tracing and export traces to Coralogix. See the code snippet in the Authentication section.
Manual
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)
Instrument
To instrument all clients, call the instrument method.
Uninstrument
To uninstrument clients, call the uninstrument method.
Full example
import asyncio
from google.adk import Agent, Runner
from google.adk.sessions import InMemorySessionService
from google.genai import types
from llm_tracekit.google_adk import GoogleADKInstrumentor, 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
GoogleADKInstrumentor().instrument()
async def main():
agent = Agent(
name="MyAgent",
model="gemini-2.0-flash",
instruction="You are a helpful assistant.",
)
session_service = InMemorySessionService()
runner = Runner(agent=agent, app_name="my_app", session_service=session_service)
session = await session_service.create_session(app_name="my_app", user_id="user_1")
async for event in runner.run_async(
user_id="user_1",
session_id=session.id,
new_message=types.Content(role="user", parts=[types.Part(text="Hello!")]),
):
if event.content and event.content.parts:
for part in event.content.parts:
if part.text:
print(part.text, end="")
if __name__ == "__main__":
asyncio.run(main())
Enable message content capture
By default, message content — prompt contents, completions, function arguments, and return values — is not captured. To capture message content as span attributes:
- Pass
capture_content=Truewhen callingsetup_export_to_coralogix. - Set the environment variable
OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENTtotrue.
Most Coralogix AI evaluations require message contents to function properly, so enabling message capture is strongly recommended.
Semantic conventions
| Attribute | Type | Description | Example |
|---|---|---|---|
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.user | string | A unique identifier representing the end user (from the user_id passed to session_service.create_session(...)) | [email protected] |
Next steps
Once your integration is set up, explore the AI Center Overview to monitor performance, costs, quality issues, and security across all your AI applications — and to set up Guardrails for real-time policy enforcement.