Groq AI Observability installation

Let AI instrument your LLM calls for you

Skip the manual setup — run this in your project and the wizard installs the SDK and wires up AI Observability for you.

Learn more

Install dependencies

Required

Full working examples

See the complete Node.js and Python examples on GitHub.

Install the PostHog SDK and the OpenAI SDK.

pip install posthog openai
npm install @posthog/ai posthog-node openai

Configure PostHog

Required

Create a PostHog client, then swap in PostHog's OpenAI wrapper, pointed at Groq.

from posthog import Posthog
from posthog.ai.openai import OpenAI
import time, uuid, json

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

client = OpenAI(
    base_url="https://api.groq.com/openai/v1",
    api_key="<groq_api_key>",
    posthog_client=posthog,
)
import { OpenAI } from '@posthog/ai/openai'
import { PostHog } from 'posthog-node'

const posthog = new PostHog('<ph_project_token>', { host: 'https://us.i.posthog.com' })

const client = new OpenAI({
  baseURL: 'https://api.groq.com/openai/v1',
  apiKey: '<groq_api_key>',
  posthog,
})

Call Groq

Required

When you use the wrapped client to call Groq, PostHog automatically captures an $ai_generation event.

trace_id = str(uuid.uuid4())

response = client.chat.completions.create(
    model="llama-3.3-70b-versatile",
    messages=[{"role": "user", "content": "What's the weather in Paris?"}],
    tools=tools,
    posthog_distinct_id="user_123",
    posthog_trace_id=trace_id,
    posthog_properties={
        "$ai_session_id": "conversation-abc",
        "$ai_provider": "groq",
    },
)
const traceId = crypto.randomUUID()

const response = await client.chat.completions.create({
  model: 'llama-3.3-70b-versatile',
  messages: [{ role: 'user', content: "What's the weather in Paris?" }],
  tools,
  posthogDistinctId: 'user_123',
  posthogTraceId: traceId,
  posthogProperties: {
    $ai_session_id: 'conversation-abc',
    $ai_provider: 'groq',
  },
})

Note: If you want to capture LLM events anonymously, omit posthog_distinct_id from the call. See our docs on anonymous vs identified events to learn more.

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

Capture tool calls as spans

Optional

For standard responses, the posthog client captures it as a generation. For all tool calls, you must manually capture them as $ai_span events.

for call in response.choices[0].message.tool_calls or []:
    start = time.time()
    result = run_tool(call.function.name, json.loads(call.function.arguments))

    posthog.capture(
        distinct_id="user_123",
        event="$ai_span",
        properties={
            "$ai_trace_id": trace_id,
            "$ai_session_id": "conversation-abc",
            "$ai_span_id": str(uuid.uuid4()),
            "$ai_span_name": call.function.name,
            "$ai_input_state": call.function.arguments,
            "$ai_output_state": result,
            "$ai_latency": time.time() - start,
        },
    )
for (const call of response.choices[0].message.tool_calls ?? []) {
  const start = Date.now()
  const result = await runTool(call.function.name, JSON.parse(call.function.arguments))

  posthog.capture({
    distinctId: 'user_123',
    event: '$ai_span',
    properties: {
      $ai_trace_id: traceId,
      $ai_session_id: 'conversation-abc',
      $ai_span_id: crypto.randomUUID(),
      $ai_span_name: call.function.name,
      $ai_input_state: call.function.arguments,
      $ai_output_state: result,
      $ai_latency: (Date.now() - start) / 1000,
    },
  })
}

See spans for the full list of span 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 AI Observability, 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 AI Observability 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.

Groq AI Observability installation

Contents

Install dependencies

Configure PostHog

Call Groq

Capture tool calls as spans

Verify traces and generations

Next steps

Community questions

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