Overview

You can monitor individual users’ activity in your AI products. Get insights into their usage patterns, preferences, and behavior, and take actions to improve the user experience.

Why user analytics?

Track user LLM usage: Understand which users are most active and contribute to your product’s success.
Identify high-value users: Understand which users are most active and contribute to your product’s success.
Optimize user experience: Tailor your product to meet the specific needs and preferences of different user groups.
Improve retention: Retain high-value users by addressing their pain points and providing a seamless experience.

Quick start

You can pass a customer_params to the request to track the user’s behavior on your LLM applications.

Here is an example of how to send a user’s data with the parameter customer_params to Keywords AI in the OpenAI Python SDK.

from openai import OpenAI

client = OpenAI(
    base_url="https://api.keywordsai.co/api/",
    api_key="YOUR_KEYWORDSAI_API_KEY",
)

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[
        {"role": "user", "content": "Tell me a long story"}
    ],
    extra_body={
        "customer_params": {
            "customer_identifier": "customer_1",
            "name": "Hendrix Liu", # optional parameter 
            "email": "hendrix@keywordsai.co" # optional parameter
        }
    }
)

Here is an example of how to send a user’s data with the parameter customer_params to Keywords AI in the OpenAI Python SDK.

from openai import OpenAI

client = OpenAI(
    base_url="https://api.keywordsai.co/api/",
    api_key="YOUR_KEYWORDSAI_API_KEY",
)

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[
        {"role": "user", "content": "Tell me a long story"}
    ],
    extra_body={
        "customer_params": {
            "customer_identifier": "customer_1",
            "name": "Hendrix Liu", # optional parameter 
            "email": "hendrix@keywordsai.co" # optional parameter
        }
    }
)

Here is an example of how to send a user’s data with the parameter customer_params to Keywords AI in the OpenAI TypeScript SDK. In OpenAI TypeScript SDK, you should add a // @ts-expect-error before the metadata field.

import { OpenAI } from "openai";

const client = new OpenAI({
  baseURL: "https://api.keywordsai.co/api",
  apiKey: "YOUR_KEYWORDSAI_API_KEY",
});

const response = await client.chat.completions
  .create({
    messages: [{ role: "user", content: "Say this is a test" }],
    model: "gpt-4o-mini",
    // @ts-expect-error
    customer_params: {
        "customer_identifier": "customer_1",
        "name": "Hendrix Liu", // optional parameter
        "email": "hendrix@keywordsai.co" // optional parameter
    }
  })
  .asResponse();

console.log(await response.json());

import requests
def demo_call(input, 
              model="gpt-4o-mini",
              token="YOUR_KEYWORDS_AI_API_KEY",
              ):
    headers = {
        'Content-Type': 'application/json',
        'Authorization': f'Bearer {token}',
    }

    data = {
        'model': model,
        'messages': [{'role': 'user', 'content': input}],
        'customer_params': {
            "customer_identifier": "customer_1",
            "name": "Hendrix Liu", # optional parameter
            "email": "hendrix@keywordsai.co" # optional parameter
        }
    }

    response = requests.post('https://api.keywordsai.co/api/chat/completions', headers=headers, json=data)
    return response

messages = "Say 'Hello World'"
print(demo_call(messages).json())

We also support adding credentials in other SDKs or languages, please check out our integration section for more information.

Get started

AI observability

Prompt engineering

Evaluations

AI gateway

Organization management

Resources

Why user analytics?

Quick start

Get started

AI observability

Prompt engineering

Evaluations

AI gateway

Organization management

Resources

​Why user analytics?

​Quick start

Why user analytics?

Quick start