Import

Install the package and import the necessary functions from our React export:

import lunary, { useChatMonitor } from 'lunary/react';

Initialize lunary

Initialize the SDK with your application’s tracking ID:

lunary.init({
  publicKey: "0x0",
})

Use the `useChatMonitor` hook

The useChatMonitor hook exposes the following functions:

const { 
  restart, 
  trackFeedback, 
  trackMessage
} = useChatMonitor();

Here’s an example of how you would it into your Chat component.

import { useState, useEffect } from "react";

const App = () => {
  const [input, setInput] = useState("");
  const [messages, setMessages] = useState([]);

  const { restart: restartMonitor, trackFeedback, trackMessage } = useChatMonitor();
  
  // Step 4: Use Effects for Initialization
  useEffect(() => {
    restartMonitor();
  }, []);

  // Step 5: Define Your Message Handlers
  const askBot = async (query) => { 

    // Track the user message and keep the message ID in reference
    const messageId = trackMessage({
      role: 'user',
      content: query,
    });

    setMessages([...messages, { id: messageId, role: "user", content: query }]);

    const answer = await fetchBotResponse(query, messages);

    setMessages([...messages, { role: "assistant", content: answer }]);

    // Track the bot answer
    trackMessage({
      role: 'assistant',
      content: answer,
    });
  }

  // Your message logic
  const fetchBotResponse = async (query, messages) => {
    const response = await fetch("https://...", {
      method: "POST",
      body: JSON.stringify({ messages }),
    });
    return await response.text();
  };

  const restartChat = () => { 
    setMessages([]);
    restartMonitor();
  }

  // Step 6: Render UI
  return (
    <>
      <div>
        {messages.map((message) => (
          <div key={message.id}>
            <div>{message.role}</div>
            <div>{message.text}</div>
          </div>
        ))}
      </div>
      <input
        value={input}
        onChange={(e) => setInput(e.target.value)}
        onKeyDown={(e) => {
          if (e.key === "Enter") {
            askBot(input);
            setInput("");
          }
        }}
      />
    </>
  );
}

Reconcile calls on your backend

Make sure to pass the message IDs to your backend to reconcile with the backend calls and agents.

Vercel AI SDK Integration

Effortlessly integrate the Vercel AI SDK into your Next.js app using lunary We’ve built a custom hook that makes tracking your AI-driven chats a breeze.

Other frameworks

This assumes you are using Next.js. If you are using another framework, contact us and we’ll help you integrate.

Import and Initialize

Import lunary and the AI SDK helper hook, then initialize the monitor with your app ID.

import lunary, { useMonitorVercelAI } from "lunary/react"

lunary.init({
  publicKey: "0x0"
})

Wrap the useChat hook

export default function Chat() {
  const ai = useChat({
    // This is necessary to reconcile LLM calls made on the backend
    sendExtraMessageFields: true
  })

  // Use the hook to wrap and track the AI SDK
  const {
    trackFeedback, // this a new function you can use to track feedback
    messages,
    input,
    handleInputChange,
    handleSubmit
  } = useMonitorVercelAI(ai)

  // Optional: Identify the user
  useEffect(() => {
    lunary.identify("elon", {
      name: "Elon Musk",
      email: "elon@tesla.com",
    })
  }, [])

  return (
    // ... your chat UI ...
  )
}

Setup the monitor on the backend

We’ll need to reconcile the OpenAI calls made in the backend, with messages sent from the frontend. To do this, we’ll need to use the backend version of the lunary.

import lunary from "lunary";
import { monitorOpenAI } from "lunary/openai";

lunary.init({
  publicKey: "0x0",
})

// Create an OpenAI API client and monitor it
const openai = monitorOpenAI(
  new OpenAI({
    apiKey: process.env.OPENAI_API_KEY
  })
);

Reconcile messages with OpenAI calls

Once your openai client is monitored, you can use the setParent method to reconcile the frontend message IDs with the backend call:

const response = await openai.chat.completions
  .create({
    model: "gpt-4",
    stream: true,
    messages: messages,
  })
  // The setParent method reconcilates the frontend call with the backend call
  .setParent(lastMessageId);

Full API Function Example

Make sure you’ve enabled sendExtraMessageFields on the useChat hook so that message IDs are also sent.

// ./app/api/chat/route.ts
import OpenAI from "openai";
import { OpenAIStream, StreamingTextResponse } from "ai";

// Import the backend version of the monitor
import lunary, { monitorOpenAI } from "lunary/openai";

lunary.init({
  publicKey: "0x0",
})

// Create an OpenAI API client and monitor it
const openai = monitorOpenAI(
  new OpenAI({
    apiKey: process.env.OPENAI_API_KEY
  })
);

export const runtime = "edge";

export async function POST(req: Request) {
  const data = await req.json()
  const { messages: rawMessages } = data

  // Keep only the content and role of each message, otherwise OpenAI throws an error
  const messages = rawMessages.map(({ content, role }) => ({ role, content }))

  // Get the last message's run ID
  const lastMessageId = rawMessages[rawMessages.length - 1].id

  // Ask OpenAI for a streaming chat completion given the prompt
  const response = await openai.chat.completions
    .create({
      model: "gpt-4",
      stream: true,
      messages: messages,
    })
    // The setParent method reconcilates the frontend call with the backend call
    .setParent(lastMessageId);


  const stream = OpenAIStream(response);


  return new StreamingTextResponse(stream);
}

Get started

Integrations

Features

More

Usage with React

Vercel AI SDK Integration

Other frameworks

Full API Function Example

​Vercel AI SDK Integration

​Other frameworks

​Full API Function Example

Vercel AI SDK Integration

Other frameworks

Full API Function Example