Getting Started

GitHub

Integrations

JavaScript

Python

LangChain

LiteLLM

Flowise

Custom

Features

Observability

Conversations

Prompts

Classification

Users

Feedback

More

Security

Concepts

Self-hosting

User Tracking

Identify your users, track their cost, conversations and more.

User tracking

The strict minimum to enable user tracking is to report a userId, however you can report any property you'd like such as an email or name using an userProps object.

Tracking users with the backend SDK

Python

Learn how to install the Python SDK

JavaScript

Learn how to install the JS SDk

Identify OpenAI calls

The easiest way to get started tracking users is to send user data with every OpenAI API call.

const res = await openai.chat.completions.create({
  model: "gpt-4o",
  messages: [{ role: "user", content: "Hello" }],
  user: "user123",
  userProps: { name: "John" },
})

chat_completion = client.chat.completions.create(
  model="gpt-4o", 
  messages=[{"role": "user", "content": "Hello"}],
  user_id="user123",
  user_props={ "name": "John" }
)

If you're using LangChain, you can similarly pass user data as metadata.

const chat = new ChatOpenAI({
  callbacks: [new LunaryHandler()],
});

const res = await chat.call([new HumanMessage("Hello!")], {
  metadata: {
    userId: "123",
    userProps: { name: "John" },
  },
});

handler = LunaryCallbackHandler()

chat = ChatOpenAI(
  callbacks=[handler],
  metadata={
   "user_id": "user123"
  },  # Assigning user ids to models in the metadata
)

Advanced: Inject user into context

When tracking traces, you can inject user data into the context using the identify methods. This will cascade down to all the child runs.

async function TranslatorAgent(input) {
  // Some AI queries
  // Everything done in this context will be tracked with the user
}

// Wrap the agent with the monitor
const translate = lunary.wrapAgent(TranslatorAgent)

// Using identify to inject the user into the context
const res = await translate(`Hello, what's your name?`)
                  .identify("user123", { email: "email@example.org" })

import lunary

def my_agent():
  # Some AI queries
  # Everything done in this context will be tracked with the user

def main():
  # Using identify to inject the user into the context
  with lunary.identify('user123', user_props={"email": "email@example.org"}):
    my_agent()

Identifying users on the frontend

If you are tracking chat messages or feedback on the frontend, you can use the identify method to identify the user there.

lunary.identify("user123", { 
  email: 'test@example.org'
})

Identifying Threads

If you are using threads to track conversations, you can pass userId and userProps to the openThread method.

const thread = await lunary.openThread({
  userId: "user123",
  userProps: { name: "John" },
})

User Properties

While you can track any property you'd like, we recommend using the following ones:

Property	Description
`name`	Name of the user
`email`	Email of the user
`avatar`	URL to an avatar
`group`	Group or company ID the user belongs to

Questions? We're here to help.

Email