> 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/data-sources/business-apps/exa.md).

# Exa

Learn how to connect Exa to JetAdmin to perform AI-powered web search, retrieve webpage content, and provide AI Agents with up-to-date information.

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.

![Exa connection form in Jet Admin](https://3448227606-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-LQ08RFAKZvFADEiXKFy%2Fuploads%2FPaVbjsTxvhtDQO4L0gtS%2Fexa-connection.jpg?alt=media)

The current connection form shows account options, including Use Jet-managed and Connect Custom. Choose the option appropriate for your setup.

{% @arcade/embed url="<https://app.arcade.software/share/SOubhnZ6703uUEweoFkX>" flowId="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 url="<https://app.arcade.software/share/5hzAPQ4I6g3SVgfxKKDk>" flowId="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 by asking a question.

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

```
GET https://docs.jetadmin.io/user-guide/data-sources/business-apps/exa.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.