> For the complete documentation index, see [llms.txt](https://docs.jetadmin.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.jetadmin.io/user-guide/integrations/exa.md).

# Exa

Exa is an AI-powered search engine designed for applications and AI systems. It enables semantic web search and content retrieval, helping AI Agents find relevant information beyond traditional keyword-based search.

## Connecting Exa

To connect your Exa account:

1. Open the **Data** tab from the left sidebar.
2. Click **Add Resource**.
3. Select **Exa** from the list of available resources.
4. Authenticate your Exa account.

{% @arcade/embed flowId="SOubhnZ6703uUEweoFkX" url="<https://app.arcade.software/share/SOubhnZ6703uUEweoFkX>" %}

{% hint style="info" %}
Once connected, the Exa resource becomes available anywhere JetAdmin supports Data resources.
{% endhint %}

## What can it do?

The Exa integration gives JetAdmin access to AI-powered web search and content retrieval.

From JetAdmin, you can:

* Search the web using semantic search
* Retrieve the contents of webpages
* Find relevant sources for research
* Provide AI Agents with live web information
* Use search results inside workflows and applications

## Where to use it?

Exa is most useful whenever your application needs web research, live information, or Retrieval-Augmented Generation (RAG).

You can use it in:

| JetAdmin feature   | Common use cases                                                                                          |
| ------------------ | --------------------------------------------------------------------------------------------------------- |
| **AI Agents**      | Answer questions using current web data, research topics, find reliable sources                           |
| **Workflows**      | Automate web research, collect online information, enrich business data                                   |
| **Internal tools** | Build research assistants, search portals, knowledge discovery tools, or content aggregation applications |

Typical business use cases include:

* AI-powered research
* Market and competitor analysis
* Content discovery
* Knowledge enrichment
* News monitoring
* Source retrieval for AI responses
* RAG applications

## Available tools (Actions)

| Action                    | Description                                                                     |
| ------------------------- | ------------------------------------------------------------------------------- |
| **Web Search (Advanced)** | Perform semantic web searches to find the most relevant webpages and sources.   |
| **Web Fetch**             | Retrieve and process the content of a webpage for analysis or use by AI Agents. |

> 💡 **Web Search (Advanced)** supports natural language queries, making it ideal for research tasks where keyword matching alone isn't enough.

## Example prompts for AI Agents

#### Research a topic

```
Research the latest trends in AI coding assistants and summarize the most important findings from reliable sources.
```

#### Find official documentation

```
Search for the official Stripe documentation about webhooks and summarize the implementation process.
```

#### Retrieve webpage content

```
Fetch the content from https://openai.com and summarize the latest product announcements.
```

#### Compare competitors

```
Search for the top project management platforms and compare their key features and pricing.
```

#### Find recent information

```
Search for recent news about vector databases and provide a summary with the original sources.
```

#### Enrich an AI response

```
Search the web for the latest information about the Model Context Protocol (MCP) and answer the user's question using the retrieved sources.
```

## Example

This example demonstrates how an AI Agent can use Exa to research a topic, retrieve relevant webpages, and generate an answer using live web information.

{% @arcade/embed flowId="5hzAPQ4I6g3SVgfxKKDk" url="<https://app.arcade.software/share/5hzAPQ4I6g3SVgfxKKDk>" %}


---

# 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 dynamically by asking a question.

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

```
GET https://docs.jetadmin.io/user-guide/integrations/exa.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

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.