> For the complete documentation index, see [llms.txt](https://cortex-docs.paloaltonetworks.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://cortex-docs.paloaltonetworks.com/cortex-agentix/reference-and-developer-docs/cortex-agentix-xql/get-started-with-xql/xql-language-features.md).

# XQL language features

Explore Cortex Query Language features for security data in Cortex AgentiX.

The Cortex Query Language (XQL) enables you to query for information contained in a wide variety of data sources in Cortex AgentiX for rigorous endpoint and network event analysis. Queries require a dataset, or data source, to run against. In a dataset query, unless otherwise specified, the query runs against the `xdr_data` dataset, which contains all raw log information that Cortex AgentiX collects from all Cortex product agents, including EDR data, and PAN NGFW data. In XDM queries, you must specify the dataset mapped to the XDM that you want to run your query against. For both types of queries, you can also import data from third parties and then query against those datasets as well.

You submit XQL queries to Cortex AgentiX using the **Investigation & Response** → **Search** → **Query Builder** user interface.

XQL is similar to other query languages, and it uses some of the same functions as can be found in many SQL implementations, but it is not SQL. XQL forms queries in stages. Each stage performs a specific query operation and is separated by a pipe (`|`) character. To help you create an effective XQL query with the proper syntax, the query field in the user interface provides suggestions and definitions as you type. For example, the following dataset query uses three stages to identify the dataset to query, identify the field to be retrieved from the dataset, and then set a filter that identifies which records should be retrieved as part of the query:

```programlisting
dataset = xdr_data 
| fields os_actor_process_file_size as osapfs 
| filter to_string(osapfs) = "12345"
```

{% hint style="info" %}

### Tip

When creating XQL queries, you can:

* Use the up and down arrow keys to navigate through the auto-suggestion commands and definitions.
* Select an auto-suggestion command by pressing either the **Enter** or **Tab** key.
* Press **Shift**+**Enter** to add a new line, and easily ignore the auto-suggestion output.
* Close the auto-suggestion output by pressing the **Esc** key.
  {% endhint %}

XQL supports:

* Simple queries.
* Filters that identify a subset of records to return in the result set.
* Joins and Unions.
* Aggregations.
* Queries against standard datasets.
* Queries against presets, which are collections of information that are specific to a given type of network or endpoint activity, such as authentication or file transfers.
* Queries against custom imported datasets.
* Queries against the XDM.


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation by asking a question.

Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:

```
GET https://cortex-docs.paloaltonetworks.com/cortex-agentix/reference-and-developer-docs/cortex-agentix-xql/get-started-with-xql/xql-language-features.md?ask=<question>&goal=<user_goal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.

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.