Chapter 48 · Instrument LLM Analytics
Subchapter 48.38
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Required
Full working examples
See the complete Node.js (opens in a new tab) and Python (opens in a new tab) examples on GitHub. If you’re using the PostHog SDK wrapper instead of OpenTelemetry, see the Node.js wrapper (opens in a new tab) and Python wrapper (opens in a new tab) examples.
Install the OpenTelemetry SDK, the OpenAI instrumentation, and the OpenAI SDK.
PostHog AI
pip install openai opentelemetry-sdk "posthog[otel]" opentelemetry-instrumentation-openai-v2npm install openai @posthog/ai @opentelemetry/sdk-node @opentelemetry/resources @opentelemetry/instrumentation-openai2
Required
Configure OpenTelemetry to auto-instrument OpenAI SDK calls and export traces to PostHog. PostHog converts gen_ai.* spans into $ai_generation events automatically.
PostHog AI
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.resources import Resource, SERVICE_NAME
from posthog.ai.otel import PostHogSpanProcessor
from opentelemetry.instrumentation.openai_v2 import OpenAIInstrumentor
resource = Resource(attributes={
SERVICE_NAME: "my-app",
"posthog.distinct_id": "user_123", # optional: identifies the user in PostHog
"foo": "bar", # custom properties are passed through
})
provider = TracerProvider(resource=resource)
provider.add_span_processor(
PostHogSpanProcessor(
api_key="<ph_project_token>",
host="https://us.i.posthog.com",
)
)
trace.set_tracer_provider(provider)
OpenAIInstrumentor().instrument()import { NodeSDK } from '@opentelemetry/sdk-node'
import { resourceFromAttributes } from '@opentelemetry/resources'
import { PostHogSpanProcessor } from '@posthog/ai/otel'
import { OpenAIInstrumentation } from '@opentelemetry/instrumentation-openai'
const sdk = new NodeSDK({
resource: resourceFromAttributes({
'service.name'
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Required
Now, when you use the OpenAI SDK to call xAI, PostHog automatically captures $ai_generation events via the OpenTelemetry instrumentation.
PostHog AI
import openai
client = openai.OpenAI(
base_url="https://api.x.ai/v1",
api_key="<xai_api_key>",
)
response = client.chat.completions.create(
model="grok-3",
max_completion_tokens=1024,
messages=[
{"role": "user", "content": "Tell me a fun fact about hedgehogs"}
],
)
print(response.choices[0].message.content)import OpenAI from 'openai'
const client = new OpenAI({
baseURL: 'https://api.x.ai/v1',
apiKey: '<xai_api_key>',
})
const response = await client.chat.completions.create({
model: 'grok-3',
max_completion_tokens: 1024,
messages: [{ role: 'user'
Note: If you want to capture LLM events anonymously, omit the
posthog.distinct_idresource attribute. See our docs on anonymous vs identified events (opens in a new tab) 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) |
| […] (opens in a new tab) | See full list (opens in a new tab) of properties |
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.


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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 (opens in a new tab) | Learn the basics of how LLM calls become events in PostHog. |
| Generations (opens in a new tab) | Read about the $ai_generation event and its properties. |
| Traces (opens in a new tab) | Explore the trace hierarchy and how to use it to debug LLM calls. |
| Spans (opens in a new tab) | Review spans and their role in representing individual operations. |
| Anaylze LLM performance (opens in a new tab) | Learn how to create dashboards to analyze LLM performance. |
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