> This page is for version v5.0 (default).
> For other versions, use one of these documentation indexes:
> - v5.0 (default): https://humanloop.com/docs/v5/llms.txt
> - v4.0: https://humanloop.com/docs/v4/llms.txt

> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://humanloop.com/docs/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://humanloop.com/docs/_mcp/server.

> Getting up and running with Humanloop is quick and easy. This guide will explain how to set up evaluations through the Humanloop UI.

For this quickstart, we're going to evaluate **Outreach Agent**, which is designed to compose personalized outbound messages to potential customers. The Agent uses a [Tool](/docs/explanation/tools) to research information about the lead before writing a message.

We will assess the quality of the Agent using a [Dataset](https://humanloop.com/docs/evaluation/guides/create-dataset) and [Evaluators](/docs/explanation/evaluators).

#### Account setup

Create a Humanloop Account

If you haven't already, [create an account](https://app.humanloop.com/signup) or [log in](https://app.humanloop.com/login) to Humanloop

Add an OpenAI API Key

If you're the first person in your organization, you'll need to add an API key to a model provider.

1. Go to OpenAI and [grab an API key](https://platform.openai.com/api-keys).
2. In Humanloop [Organization Settings](https://app.humanloop.com/account/api-keys) set up OpenAI as a model provider.

> **Info**
>
> Using the Prompt Editor will use your OpenAI credits in the same way that the
> OpenAI playground does. Keep your API keys for Humanloop and the model
> providers private.

## Running an Evaluation

### Clone an Agent

In this quickstart, we will use a pre-configured Agent from the Humanloop Library.

Navigate to the Library by clicking the Library button in the upper-left corner. Select the **Outreach Agent** and click the **Clone to Workspace** button in the upper-right corner.

![](/docs/_fern-img/5eb455c9af00590e41d229c45693483b8e327f19e2bb51c36f39b10e5d244506.webp)

This will create an **Outreach Agent** folder in your workspace. Inside the folder, you'll find:

* The **Outreach Agent**.
* **Hacker News Search** Tool used by the Agent to research potential customers.
* A **Customer Outreach** Dataset and three **Evaluators** that we will use to assess the Agent.

### Try out the Agent

The **Outreach Agent** looks up information about the lead on Hacker News and composes an outbound message to them.

Before we kick off the first evaluation, run the Agent in the Editor to get a feel for how it works:

1. Click on the **Outreach Agent** file.
2. On the left-hand side, you can configure your Agent's Parameters, Instructions, and add Tools.
3. On the right-hand side, you can specify Inputs and run the Agent on demand.
4. Enter *Coca-Cola* as the organization and *Humanloop* as the lead in the Inputs section in the top right-hand side.
5. Click the Run button.

![](/docs/_fern-img/7192532e25b53f5d9b6cae719e4edbd0e15d22ffb1d6af24f03aebbd0b5bdaa5.webp)

### Run Eval

Evaluations are an efficient way to improve your Agent iteratively. You can test versions of the Agent against a [Dataset](https://humanloop.com/docs/evaluation/guides/create-dataset) and see how changing the Agent's configuration impacts the performance.

To test the **Outreach Agent**, navigate to the **Evals** tab and click on the **+ Evaluation** button.

Create a new Run by clicking on the **+ Run** button. Then, follow these steps:

1. Click on the **Dataset** button and select **Customer Outreach** Dateset.
2. Click on the **Agent** button and select the "v1" version.
3. Click on **Evaluators** and add the three Evaluators included in the Output Agent folder: **Friendly Tone**, **Tool Call** and **Message Length**

The first two Evaluators will check if the message is friendly and if the Tool was used.
The **Message Length** Evaluator will show the number of words in the output, providing a baseline value for all further evaluations.

Click **Save**. Humanloop will start generating Logs for the Evaluation.

![](/docs/_fern-img/48c64f972b5458324bd9771da5d7389d7ae290687e389b094cb663b47eb362ed.webp)

### Review results

After the Run is completed, you can review the Logs produced and corresponding judgments in the **Review** tab.

![](/docs/_fern-img/904856454a7f1bbd57496f2c110c1b05e7b2451e685749e3aa2712f7c8a78bed.webp)

The summary of all Evaluators is displayed in the **Stats** tab.

![](/docs/_fern-img/c11e3447a6bb8530c31b4a4569d9384f7c985ccf4784cf9a8df6e6b6153dd4cc.webp)

> **Note**
>
> The evaluation is most useful when you iteratively improve the Agent, as it allows you to compare several versions
> side by side. Follow our [tutorial](/docs/v5/tutorials/evaluate-agent-in-ui) where we add the **Google Search Tool**
> to this Agent and compare the results with this version.

In this quick guide, you've created an Agent that can help your organization compose personalized messages to your leads. You've evaluated the initial version to see how this Agent performs across multiple inputs.

## Next steps

Now that you've successfully run your first Eval, you can explore how you can make your Agent more powerful:

* Add **Google Search** Tool to the Outbound Agent and compare two versions side by side. We explain the steps in our [Evaluate an Agent tutorial](/docs/v5/tutorials/evaluate-agent-in-ui)
* Learn how your subject-matter experts (such as your sales team) can [review and evaluate model outputs](/docs/evaluation/guides/manage-multiple-reviewers) in Humanloop to help improve your AI product.