Convex LLM analytics installation

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

Set environment variables

Required

Set your PostHog project API key and host as Convex environment variables. You can find these in your project settings.

npx convex env set POSTHOG_API_KEY "<ph_project_token>"
npx convex env set POSTHOG_HOST "https://us.i.posthog.com"

You also need your AI provider's API key (e.g. OPENAI_API_KEY):

npx convex env set OPENAI_API_KEY "your_openai_api_key"

Capture LLM events with OpenTelemetry

Required

Convex actions run in a Node.js-compatible environment when you add the "use node" directive. Create an action that initializes the OpenTelemetry SDK with PostHog's trace exporter and enables telemetry on your AI SDK calls.

"use node"

import { NodeSDK } from '@opentelemetry/sdk-node'
import { resourceFromAttributes } from '@opentelemetry/resources'
import { generateText } from 'ai'
import { openai } from '@ai-sdk/openai'
import { PostHogTraceExporter } from '@posthog/ai/otel'
import { action } from './_generated/server'
import { v } from 'convex/values'

export const generate = action({
  args: {
    prompt: v.string(),
    distinctId: v.optional(v.string()),
  },
  handler: async (_ctx, args) => {
    const distinctId = args.distinctId ?? 'anonymous'

    const sdk = new NodeSDK({
      resource: resourceFromAttributes({
        'service.name': 'my-convex-app',
        'user.id': distinctId,
      }),
      traceExporter: new PostHogTraceExporter({
        apiKey: process.env.POSTHOG_API_KEY!,
        host: process.env.POSTHOG_HOST,
      }),
    })
    sdk.start()

    const result = await generateText({
      model: openai('gpt-5-mini'),
      prompt: args.prompt,
      experimental_telemetry: {
        isEnabled: true,
        functionId: 'my-convex-action',
        metadata: {
          posthog_distinct_id: distinctId,
        },
      },
    })

    await sdk.shutdown()

    return { text: result.text, usage: result.usage }
  },
})

How this works

The PostHogTraceExporter sends OpenTelemetry gen_ai.* spans to PostHog's OTLP ingestion endpoint. PostHog converts these into $ai_generation events automatically. The posthog_distinct_id metadata field links events to a specific user.

Using Convex Agent

Optional

If you're using @convex-dev/agent, pass experimental_telemetry to the agent's generateText call:

"use node"

import { NodeSDK } from '@opentelemetry/sdk-node'
import { resourceFromAttributes } from '@opentelemetry/resources'
import { Agent } from '@convex-dev/agent'
import { openai } from '@ai-sdk/openai'
import { PostHogTraceExporter } from '@posthog/ai/otel'
import { components } from './_generated/api'
import { action } from './_generated/server'
import { v } from 'convex/values'

export const generate = action({
  args: {
    prompt: v.string(),
    distinctId: v.optional(v.string()),
  },
  handler: async (ctx, args) => {
    const distinctId = args.distinctId ?? 'anonymous'

    const sdk = new NodeSDK({
      resource: resourceFromAttributes({
        'service.name': 'my-convex-app',
        'user.id': distinctId,
      }),
      traceExporter: new PostHogTraceExporter({
        apiKey: process.env.POSTHOG_API_KEY!,
        host: process.env.POSTHOG_HOST,
      }),
    })
    sdk.start()

    const supportAgent = new Agent(components.agent, {
      name: 'support-agent',
      languageModel: openai('gpt-5-mini'),
      instructions: 'You are a helpful support agent.',
    })

    const { thread } = await supportAgent.createThread(ctx, {})

    const result = await thread.generateText({
      prompt: args.prompt,
      experimental_telemetry: {
        isEnabled: true,
        functionId: 'convex-agent',
        metadata: {
          posthog_distinct_id: distinctId,
        },
      },
    })

    await sdk.shutdown()

    return { text: result.text, usage: result.totalUsage }
  },
})

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

Property	Description
`$ai_model`	The specific model, like `gpt-5-mini` or `claude-4-sonnet`
`$ai_latency`	The latency of the LLM call in seconds
`$ai_time_to_first_token`	Time to first token in seconds (streaming only)
`$ai_tools`	Tools and functions available to the LLM
`$ai_input`	List of messages sent to the LLM
`$ai_input_tokens`	The number of tokens in the input (often found in response.usage)
`$ai_output_choices`	List of response choices from the LLM
`$ai_output_tokens`	The number of tokens in the output (often found in `response.usage`)
`$ai_total_cost_usd`	The total cost in USD (input + output)
[...]	See full list of properties

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.

Check for LLM events in PostHog

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.

Resource	Description
Basics	Learn the basics of how LLM calls become events in PostHog.
Generations	Read about the `$ai_generation` event and its properties.
Traces	Explore the trace hierarchy and how to use it to debug LLM calls.
Spans	Review spans and their role in representing individual operations.
Anaylze LLM performance	Learn how to create dashboards to analyze LLM performance.

Convex LLM analytics installation

Contents

Install dependencies

Set environment variables

Capture LLM events with OpenTelemetry

Using Convex Agent

Verify traces and generations

Next steps

Community questions

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