Ollama

2

Install the OpenAI SDK

Required

Install the OpenAI SDK. The PostHog SDK instruments your LLM calls by wrapping the OpenAI client. The PostHog SDK does not proxy your calls.

PostHog AI

Python

pip install openai

Node

npm install openai

3

Initialize PostHog and OpenAI client

Required

We call Ollama through the OpenAI client and generate a response. We’ll use PostHog’s OpenAI provider to capture all the details of the call. Initialize PostHog with your PostHog project token and host from your project settings (opens in a new tab), then pass the PostHog client along with the Ollama config (the base URL) to our OpenAI wrapper.

PostHog AI

Python

from posthog.ai.openai import OpenAI
from posthog import Posthog
posthog = Posthog(
    "<ph_project_token>",
    host="https://us.i.posthog.com"
)
client = OpenAI(
    base_url="http://localhost:11434/v1",
    api_key="ollama",
    posthog_client=posthog
)

Node

import { OpenAI } from '@posthog/ai'
import { PostHog } from 'posthog-node'
const phClient = new PostHog(
  '<ph_project_token>',
  { host: 'https://us.i.posthog.com' }
);
const openai = new OpenAI({
  baseURL: 'http://localhost:11434/v1',
  apiKey: 'ollama'

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 (opens in a new tab) for more details.

4

Call Ollama

Required

Now, when you call Ollama with the OpenAI SDK, PostHog automatically captures an $ai_generation event. You can also capture or modify additional properties with the distinct ID, trace ID, properties, groups, and privacy mode parameters.

PostHog AI

Python

response = client.chat.completions.create(
    model="llama3.2",
    messages=[
        {"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)

Node

const completion = await openai.chat.completions.create({
    model: "llama3.2",
    messages: [{ role: "user", content: "Tell me a fun fact about hedgehogs" }],
    posthogDistinctId: "user_123", // optional
    posthogTraceId: "trace_123", // optional
    posthogProperties: { conversation_id: "abc123", paid: true }, // optional

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 (opens in a new tab) to learn more.

You can expect captured $ai_generation events to have the following 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 (opens in a new tab)

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.