> ## 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.

# Message

A Message is a piece of information that is sent from the user to an assistant and vice versa.
Coupled with life cycle hooks, they are the building blocks of a chat.

A message has a content, a timestamp and cannot be nested.

## Example: Reply to a user message

Lets create a simple assistant that replies to a user message with a greeting.

```py theme={null}
import chainlit as cl

@cl.on_message
async def on_message(message: cl.Message):
    response = f"Hello, you just sent: {message.content}!"
    await cl.Message(response).send()
```

<Card title="Message API" icon="message" color="#F80061" href="/api-reference/message">
  Learn more about the Message API.
</Card>

## Chat Context

Since LLMs are stateless, you will often have to accumulate the messages of the current conversation in a list to provide the full context to LLM with each query.

You could do that manually with the [user\_session](/concepts/user-session). However, Chainlit provides a built-in way to do this:

```py chat_context theme={null}
import chainlit as cl

@cl.on_message
async def on_message(message: cl.Message):
    # Get all the messages in the conversation in the OpenAI format
    print(cl.chat_context.to_openai())

    # Send the response
    response = f"Hello, you just sent: {message.content}!"
    await cl.Message(response).send()
```

Every message sent or received will be automatically accumulated in `cl.chat_context`.
You can then use `cl.chat_context.to_openai()` to get the conversation in the OpenAI format and feed it to the LLM.