Google LLM analytics installation

Setting up analytics starts with installing the PostHog SDK for your language. LLM analytics works best with our Python and Node SDKs.

pip install posthog

Install the Google Gen AI SDK:

pip install google-genai

In the spot where you initialize the Google Gen AI SDK, import PostHog and our Google Gen AI wrapper, initialize PostHog with your project API key and host from your project settings, and pass it to our wrapper.

from posthog.ai.gemini import Client
from posthog import Posthog

posthog = Posthog(
    "<ph_project_api_key>",
    host="https://us.i.posthog.com"
)

client = Client(
    api_key="...", # Replace with your Gemini API key
    posthog_client=posthog # This is an optional parameter. If it is not provided, a default client will be used.
)

Now, when you use the Google Gen AI SDK, it automatically captures many properties into PostHog including $ai_input, $ai_input_tokens, $ai_cache_read_input_tokens, $ai_cache_creation_input_tokens, $ai_latency, $ai_tools, $ai_model, $ai_model_parameters, $ai_output_choices, and $ai_output_tokens.

You can also capture or modify additional properties with the distinct ID, trace ID, properties, groups, and privacy mode parameters.

response = client.models.generate_content(
    model="gemini-2.5-flash",
    contents=["Tell me a fun fact about hedgehogs"],
    posthog_distinct_id="user_123", # optional
    posthog_trace_id="trace_123", # optional
    posthog_properties={"conversation_id": "abc123", "paid": True}, # optional
    posthog_groups={"company": "company_id_in_your_db"},  # optional 
    posthog_privacy_mode=False # optional
)

print(response.text)

Note: If you want to capture LLM events anonymously, don't pass a distinct ID to the request. See our docs on anonymous vs identified events to learn more.

Before proceeding, let's make sure LLM events are being captured and sent to PostHog. Under LLM analytics, you should see rows of data appear in the Traces and Generations tabs.