Metadata-Version: 2.4
Name: llm-tracekit-google-adk
Version: 1.3.0
Summary: OpenTelemetry instrumentation for Google ADK (Agent Development Kit).
Project-URL: homepage, https://coralogix.com
Project-URL: repository, https://github.com/coralogix/llm-tracekit.git
Author-email: "Coralogix Ltd." <info@coralogix.com>
License-Expression: Apache-2.0
License-File: LICENSE
Classifier: Development Status :: 5 - Production/Stable
Classifier: Framework :: OpenTelemetry :: Instrumentations
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Requires-Python: >=3.10
Requires-Dist: google-adk>=1.0.0
Requires-Dist: llm-tracekit-core>=1.0.0
Requires-Dist: opentelemetry-instrumentation>=0.60b0
Description-Content-Type: text/markdown

# LLM Tracekit - Google ADK
OpenTelemetry instrumentation for [Google ADK (Agent Development Kit)](https://github.com/google/adk-python), designed to simplify LLM application development and production tracing and debugging.

## Installation
```bash
pip install "llm-tracekit-google-adk"
```


## Usage
This section describes how to set up instrumentation for Google ADK.

### Setting up tracing
You can use the `setup_export_to_coralogix` function to setup tracing and export traces to Coralogix
```python
from llm_tracekit.google_adk 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:
```python
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
```python
from llm_tracekit.google_adk import GoogleADKInstrumentor

GoogleADKInstrumentor().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:
```python
GoogleADKInstrumentor().uninstrument()
```

### Full Example
```python
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!")]),
    ):
        # print any streamed text chunks
        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())
```

## 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.user` | string | A unique identifier representing the end-user (from the `user_id` passed to `session_service.create_session(...)`) | `user@company.com`
