Vercel AI SDK LLM analytics installation

  1. Install dependencies

    Required

    Install the PostHog AI package, the Vercel AI SDK, and the OpenTelemetry SDK.

    npm install @posthog/ai @ai-sdk/openai ai @opentelemetry/sdk-node @opentelemetry/resources
    No proxy

    These SDKs do not proxy your calls. They only send analytics data to PostHog in the background.

  2. Set up the OpenTelemetry exporter

    Required

    Initialize the OpenTelemetry SDK with PostHog's PostHogTraceExporter. This sends gen_ai.* spans directly to PostHog's OTLP ingestion endpoint. PostHog converts these into $ai_generation events automatically.

    import { NodeSDK } from '@opentelemetry/sdk-node'
    import { resourceFromAttributes } from '@opentelemetry/resources'
    import { PostHogTraceExporter } from '@posthog/ai/otel'
    

    const sdk = new NodeSDK({
      resource: resourceFromAttributes({
        'service.name': 'my-ai-app',
      }),
      traceExporter: new PostHogTraceExporter({
        apiKey: '<ph_project_token>',
        host: 'https://us.i.posthog.com',
      }),
    })
    sdk.start()

  3. Call Vercel AI with telemetry enabled

    Required

    Pass experimental_telemetry to your Vercel AI SDK calls. The posthog_distinct_id metadata field links events to a specific user in PostHog.

    import { generateText } from 'ai'
    import { openai } from '@ai-sdk/openai'
    

    const result = await generateText({
      model: openai('gpt-5-mini'),
      prompt: 'Tell me a fun fact about hedgehogs.',
      experimental_telemetry: {
        isEnabled: true,
        functionId: 'my-ai-function',
        metadata: {
          posthog_distinct_id: 'user_123', // optional
        },
      },
    })
    

    console.log(result.text)
    

    await sdk.shutdown()

    Note: If you want to capture LLM events anonymously, omit the posthog_distinct_id metadata field. 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_time_to_first_tokenTime to first token in seconds (streaming only)
    $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

  4. Verify traces and generations

    Recommended
    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

  5. Next steps

    Recommended

    Now that you're capturing AI conversations, continue with the resources below to learn what else LLM Analytics enables within the PostHog platform.

    ResourceDescription
    BasicsLearn the basics of how LLM calls become events in PostHog.
    GenerationsRead about the $ai_generation event and its properties.
    TracesExplore the trace hierarchy and how to use it to debug LLM calls.
    SpansReview spans and their role in representing individual operations.
    Anaylze LLM performanceLearn how to create dashboards to analyze LLM performance.

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