Gemini

Coralogix's AI Observability integrations provide deep visibility into applications that rely on Google Gemini models. With a dedicated integration for the Google Generative AI SDK, Coralogix delivers consolidated insight into synchronous, streaming, and async calls, enabling teams to monitor performance, cost, and reliability across every Gemini workload.

Overview

This library offers customized OpenTelemetry instrumentation for Google Gemini, optimized to support large language model (LLM) application development with streamlined integration, detailed production tracing, and effective debugging capabilities.

Supported operations

Text generation: client.models.generate_content() and generate_content_stream() (sync and async).
Embeddings: client.models.embed_content() (sync and async).

Requirements

Python 3.10–3.13.
Coralogix API keys.

Installation

Run the following command.

pip install "llm-tracekit-gemini"

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.gemini 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 Gemini.

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.

from llm_tracekit.gemini import GeminiInstrumentor

GeminiInstrumentor().instrument()

Uninstrument

To uninstrument clients, call the uninstrument method.

GeminiInstrumentor().uninstrument()

Full example: text generation

from google import genai
from llm_tracekit.gemini import GeminiInstrumentor, 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
GeminiInstrumentor().instrument()

# Gemini usage example
client = genai.Client()
response = client.models.generate_content(
    model="gemini-2.0-flash",
    contents=[{"role": "user", "parts": [{"text": "Write a short poem on open telemetry."}]}],
)

Full example: embeddings

from google import genai
from google.genai import types
from llm_tracekit.gemini import GeminiInstrumentor, setup_export_to_coralogix

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

GeminiInstrumentor().instrument()

client = genai.Client()

# Single content embedding
response = client.models.embed_content(
    model="gemini-embedding-001",
    contents="What is machine learning?",
)
print(f"Embedding dimensions: {len(response.embeddings[0].values)}")

# Batch embedding
response = client.models.embed_content(
    model="gemini-embedding-001",
    contents=["First text", "Second text", "Third text"],
)
print(f"Number of embeddings: {len(response.embeddings)}")

# With dimensionality reduction
response = client.models.embed_content(
    model="gemini-embedding-001",
    contents="What is quantum computing?",
    config=types.EmbedContentConfig(output_dimensionality=256),
)
print(f"Reduced dimensions: {len(response.embeddings[0].values)}")

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=True when calling setup_export_to_coralogix.
Set the environment variable OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT to true.

Most Coralogix AI evaluations require message contents to function properly, so enabling message capture is strongly recommended.

Semantic conventions

Text generation attributes

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 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	`Get the current weather in a given location`
`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"}}}`

Embeddings attributes

Attribute	Type	Description	Example
`gen_ai.embeddings.dimension.count`	int	Requested output dimensionality	`256`
`gen_ai.embeddings.<n>.vector`	array	The embedding vector values (when content capture is enabled)	`[0.1, 0.2, ...]`

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.

Need help? Contact Support.

What's new? Find out here.

LLM? Read llms.txt.

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