> ## Documentation Index
> Fetch the complete documentation index at: https://docs.chainlit.io/llms.txt
> Use this file to discover all available pages before exploring further.

# LiteLLM

In this tutorial, we will guide you through the steps to create a Chainlit application integrated with [LiteLLM Proxy](https://docs.litellm.ai/docs/simple_proxy)

The benefits of using LiteLLM Proxy with Chainlit is:

* You can [call 100+ LLMs in the OpenAI API format](https://docs.litellm.ai/docs/providers)
* Use Virtual Keys to set budget limits and track usage
* see LLM API calls in a step in the UI, and you can explore them in the prompt playground.

<Warning>
  You shouldn't configure this integration if you're already using another
  integration like Langchain or LlamaIndex. Both integrations would
  record the same generation and create duplicate steps in the UI.
</Warning>

## Prerequisites

Before getting started, make sure you have the following:

* A working installation of Chainlit
* The OpenAI package installed
* [LiteLLM Proxy Running](https://docs.litellm.ai/docs/proxy/deploy)
* [A LiteLLM Proxy API Key](https://docs.litellm.ai/docs/proxy/virtual_keys)
* Basic understanding of Python programming

## Step 1: Create a Python file

Create a new Python file named `app.py` in your project directory. This file will contain the main logic for your LLM application.

## Step 2: Write the Application Logic

In `app.py`, import the necessary packages and define one function to handle messages incoming from the UI.

```python theme={null}
from openai import AsyncOpenAI
import chainlit as cl
client = AsyncOpenAI(
    api_key="anything",            # litellm proxy virtual key
    base_url="http://0.0.0.0:4000" # litellm proxy base_url
)

# Instrument the OpenAI client
cl.instrument_openai()

settings = {
    "model": "gpt-3.5-turbo", # model you want to send litellm proxy
    "temperature": 0,
    # ... more settings
}

@cl.on_message
async def on_message(message: cl.Message):
    response = await client.chat.completions.create(
        messages=[
            {
                "content": "You are a helpful bot, you always reply in Spanish",
                "role": "system"
            },
            {
                "content": message.content,
                "role": "user"
            }
        ],
        **settings
    )
    await cl.Message(content=response.choices[0].message.content).send()
```

## Step 3: Run the Application

To start your app, open a terminal and navigate to the directory containing `app.py`. Then run the following command:

```bash theme={null}
chainlit run app.py -w
```

The `-w` flag tells Chainlit to enable auto-reloading, so you don't need to restart the server every time you make changes to your application. Your chatbot UI should now be accessible at [http://localhost:8000](http://localhost:8000).