LLM analytics is currently considered in beta
. To access it, enable the feature preview in your PostHog account.
- 1
Install the PostHog SDK
RequiredSetting up analytics starts with installing the PostHog SDK for your language. LLM analytics works best with our Python and Node SDKs.
pip install posthog - 2
Install the Anthropic SDK
RequiredInstall the Anthropic SDK:
pip install anthropicProxy noteThese SDKs do not proxy your calls, they only fire off an async call to PostHog in the background to send the data.
You can also use LLM analytics with other SDKs or our API, but you will need to capture the data manually via the capture method. See schema in the manual capture section for more details.
- 3
Initialize PostHog and the Anthropic wrapper
RequiredIn the spot where you initialize the Anthropic SDK, import PostHog and our Anthropic wrapper, initialize PostHog with your project API key and host from your project settings, and pass it to our Anthropic wrapper.
from posthog.ai.anthropic import Anthropicfrom posthog import Posthogposthog = Posthog("<ph_project_api_key>",host="https://us.i.posthog.com")client = Anthropic(api_key="sk-ant-api...", # Replace with your Anthropic API keyposthog_client=posthog # This is an optional parameter. If it is not provided, a default client will be used.)Note: This also works with the
AsyncAnthropic
client as well asAnthropicBedrock
,AnthropicVertex
, and the async versions of those. - 4
Call Anthropic LLMs
RequiredNow, when you use the Anthropic 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.messages.create(model="claude-3-opus-20240229",messages=[{"role": "user","content": "Tell me a fun fact about hedgehogs"}],posthog_distinct_id="user_123", # optionalposthog_trace_id="trace_123", # optionalposthog_properties={"conversation_id": "abc123", "paid": True}, # optionalposthog_groups={"company": "company_id_in_your_db"}, # optionalposthog_privacy_mode=False # optional)print(response.content[0].text)Notes:
- This also works when message streams are used (e.g.
stream=True
orclient.messages.stream(...)
). - 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.
- This also works when message streams are used (e.g.