Google GenAI (tracing)

The official Google Gen AI SDK (google-genai) is the Python client for Gemini models, supporting text, multimodal prompts, streaming, tool use, and structured outputs. Respan gives you full observability over every Gemini generation, streamed response, token count, and model tool call — and gateway routing through the Respan endpoint.

Set up Respan

Create an account at platform.respan.ai and grab an API key.

Run npx @respan/cli setup to set up with your coding agent.

Use Respan Gateway

See Google Gen AI gateway setup to route this integration through the Respan gateway.

Example projects

Example project repository

Setup

Install packages

$ pip install respan-ai respan-instrumentation-google-genai google-genai

Set environment variables

$ export RESPAN_API_KEY="YOUR_RESPAN_API_KEY"
$ # Optional for direct Google calls while still exporting traces to Respan
$ export GOOGLE_API_KEY="YOUR_GOOGLE_API_KEY"

If GOOGLE_API_KEY is not set, route through the Respan Gemini gateway with RESPAN_API_KEY. Gateway calls require Gemini provider credentials or managed credits configured on your Respan account.

Initialize and run

1 import os
2 from google import genai
3 from respan import Respan
4 from respan_instrumentation_google_genai import GoogleGenAIInstrumentor
5 
6 respan = Respan(instrumentations=[GoogleGenAIInstrumentor()])
7 
8 client = genai.Client(
9     api_key=os.environ.get("GOOGLE_API_KEY") or os.environ["RESPAN_API_KEY"],
10     http_options=None if os.environ.get("GOOGLE_API_KEY") else {
11         "base_url": "https://api.respan.ai/api/google/gemini"
12     },
13 )
14 
15 response = client.models.generate_content(
16     model="gemini-2.5-flash",
17     contents="Say hello in three languages.",
18 )
19 print(response.text)
20 respan.flush()

View your trace

Open the Traces page to see your Gemini span with prompt content, completion content, model name, token usage, and tool-call attributes.

Configuration

Parameter	Type	Default	Description
`api_key`	`str \| None`	`None`	Falls back to `RESPAN_API_KEY` env var.
`base_url`	`str \| None`	`None`	Falls back to `RESPAN_BASE_URL` env var.
`instrumentations`	`list`	`[]`	Plugin instrumentations to activate, such as `GoogleGenAIInstrumentor()`.
`customer_identifier`	`str \| None`	`None`	Default customer identifier for all spans.
`metadata`	`dict \| None`	`None`	Default metadata attached to all spans.
`environment`	`str \| None`	`None`	Environment tag, such as `"production"`.

Attributes

In Respan()

Set defaults at initialization — these apply to all generated spans.

1 from respan import Respan
2 from respan_instrumentation_google_genai import GoogleGenAIInstrumentor
3 
4 respan = Respan(
5     instrumentations=[GoogleGenAIInstrumentor()],
6     customer_identifier="user_123",
7     metadata={"service": "gemini-api", "version": "1.0.0"},
8 )

With propagate_attributes

Override per-request using a context scope.

1 from google import genai
2 from respan import Respan, propagate_attributes
3 from respan_instrumentation_google_genai import GoogleGenAIInstrumentor
4 
5 respan = Respan(instrumentations=[GoogleGenAIInstrumentor()])
6 client = genai.Client(
7     api_key=os.environ.get("GOOGLE_API_KEY") or os.environ["RESPAN_API_KEY"],
8     http_options=None if os.environ.get("GOOGLE_API_KEY") else {
9         "base_url": "https://api.respan.ai/api/google/gemini"
10     },
11 )
12 
13 def handle_request(user_id: str, question: str):
14     with propagate_attributes(
15         customer_identifier=user_id,
16         thread_identifier="conv_abc_123",
17         metadata={"plan": "pro"},
18     ):
19         response = client.models.generate_content(
20             model="gemini-2.5-flash",
21             contents=question,
22         )
23         print(response.text)

Attribute	Type	Description
`customer_identifier`	`str`	Identifies the end user in Respan analytics.
`thread_identifier`	`str`	Groups related messages into a conversation.
`metadata`	`dict`	Custom key-value pairs. Merged with default metadata.