OpenAI LLM analytics installation

Last updated:

|

On this page

  1. Install the PostHog SDK

    Required

    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

  2. Install the OpenAI SDK

    Required

    Install the OpenAI SDK:

    pip install openai

  3. Initialize PostHog and OpenAI client

    Required

    Initialize PostHog with your project API key and host from your project settings, then pass it to our OpenAI wrapper. 

    from posthog.ai.openai import OpenAI
    from posthog import Posthog
    

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

    client = OpenAI(
        api_key="your_openai_api_key",
        posthog_client=posthog # This is an optional parameter. If it is not provided, a default client will be used.
    )

    Note: This also works with the AsyncOpenAI client.

    Proxy note

    These 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 in the right format. See the schema in the manual capture section for more details.

  4. Call OpenAI LLMs

    Required

    Now, when you use the OpenAI SDK to call LLMs, PostHog automatically captures an $ai_generation event.

    You can enrich the event with additional data such as the trace ID, distinct ID, custom properties, groups, and privacy mode options.

    response = client.responses.create(
        model="gpt-4o-mini",
        input=[
            {"role": "user", "content": "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.choices[0].message.content)

    Notes:

    • We also support the old chat.completions API.
    • This works with responses where stream=True.
    • 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.

    You can expect captured $ai_generation events to have the following properties:

    PropertyDescription
    $ai_modelThe specific model, like gpt-5-mini or claude-4-sonnet
    $ai_latencyThe latency of the LLM call in seconds
    $ai_toolsTools and functions available to the LLM
    $ai_inputList of messages sent to the LLM
    $ai_input_tokensThe number of tokens in the input (often found in response.usage)
    $ai_output_choicesList of response choices from the LLM
    $ai_output_tokensThe number of tokens in the output (often found in response.usage)
    $ai_total_cost_usdThe total cost in USD (input + output)
    ...See full list of properties

  5. Verify traces and generations

    Checkpoint
    Confirm LLM events are being sent to PostHog

    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.


    LLM generations in PostHog
    Check for LLM events in PostHog

  6. Capture embeddings

    Optional

    PostHog can also capture embedding generations as $ai_embedding events. Just make sure to use the same posthog.ai.openai client to do so:

    Python
    response = client.embeddings.create(
        input="The quick brown fox",
        model="text-embedding-3-small",
        posthog_distinct_id="user_123", # optional
        posthog_trace_id="trace_123",   # optional
        posthog_properties={"key": "value"} # optional
        posthog_groups={"company": "company_id_in_your_db"}  # optional 
        posthog_privacy_mode=False # optional
    )

Questions? Ask Max AI.

It's easier than reading through 814 pages of documentation

Community questions

Was this page useful?

Next article

Anthropic 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. Install the Anthropic SDK: Initialize PostHog with your project API key and host from your project settings , then pass it to our Anthropic wrapper. Note: This also works with the AsyncAnthropic client as well as AnthropicBedrock , AnthropicVertex , and the async versions of those. Now, when you use the Anthropic SDK to call LLMs, PostHog automatically…

Read next article