# Messages Format

## Plain text

By default when you send a message using the SDK, the UI shows it in plain text.

Plain text messages are great for chatting or sending messages that don't contain formatting to the user.

<figure><img src="/files/hnxPwcHiTja8tEWGTy9z" alt=""><figcaption><p>Plain text response example</p></figcaption></figure>

Sending plain text is the default behavior of the SDK, so you just have to run the following code:

{% tabs %}
{% tab title="Python" %}

```python
agent.send({
    conversation_id: 'your-conversation-id',
    text: 'Your plain text value',
});
```

{% endtab %}

{% tab title="TypeScript" %}

```
agent.send({
    conversationId: 'your-conversation-id',
    text: 'Your plain text value',
});
```

{% endtab %}
{% endtabs %}

## Markdown

Often, you may want your agent to return more than just a simple text. In that situation, you most likely want to use the markdown format.

Markdown is great for applying **some** *styling,* [or to display a link](https://agentlabs.dev), generating a table, displaying `code snippet` , images, and more.

You can see a [full markdown cheatsheet here.](https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet)

{% embed url="<https://youtu.be/r6PK--cEdjw>" %}
Example of a message containing markdown
{% endembed %}

Send indicate the interpreter you are sending text in Markdown, you just have to use the `MARKDOWN` format option.

{% tabs %}
{% tab title="Python" %}

```python
from agentlabs.chat importMessageFormat

agent.send(
  text="**My Text**",
  format=MessageFormat.MARKDOWN,
  conversation_id=self.conversation_id,
)
```

{% endtab %}

{% tab title="TypeScript" %}

```
agent.send({
    conversationId: 'your-conversation-id',
    text: 'Your plain text value',
    format: 'Markdown'
});
```

{% endtab %}
{% endtabs %}


---

# Agent Instructions: Querying This Documentation

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Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.agentlabs.dev/core-concepts/messages/messages-format.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.