> For the complete documentation index, see [llms.txt](https://docs.roboflow.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.roboflow.com/universe/find-a-model-on-universe.md).

# Find a Model on Universe

Over 50,000 datasets on Universe also have trained models associated with the dataset. These models can be used in your computer vision project with [Workflows](/workflows/what-is-workflows.md).

To find a dataset on Universe, type in what you want to identify into the search bar. On the search page, check the "Has a Model" box:

<figure><img src="/files/081nalnGWmCpHzn6rQmj" alt=""><figcaption></figcaption></figure>

This will refine your search to only datasets that contain trained models.

Click on a result to find out more about the project.

The dataset home page will show metrics. These metrics will depend on the model type. For object detection, for example, you will see mAP, precision, and recall; for classification you will see accuracy.

<figure><img src="/files/YfGnnTZoIdBqdUP6Oob3" alt=""><figcaption></figcaption></figure>

You can upload an image into the "Try This Model" section to try the most recent model version.

You can also click "Models" in the sidebar to see all trained models and choose one to try:

<figure><img src="/files/KYhFosCQIMvcZTm0qrkx" alt=""><figcaption></figcaption></figure>

When you open the Models page, you will see the results from an example image in the project test set plotted onto the image:

<figure><img src="/files/wrwYsvcrHC6p1qEkvLTF" alt=""><figcaption><p>The class "occupied forklift" is identified in the image.</p></figcaption></figure>

## Deploy a Universe Model

Each Universe model page has a "Deploy Model" button that opens a modal with several deployment options:

* Code Snippet: ready-to-use code in Python, JavaScript, or cURL for calling the model via the [Serverless API](/deploy/serverless-hosted-api-v2.md). Each snippet includes a "Copy prompt" button that copies a structured prompt you can paste into an AI coding agent.
* MCP: setup instructions for connecting the [Roboflow MCP Server](/agents/agents.md) to Claude Code, Cursor, or Codex. Once connected, your agent can run inference on Roboflow models directly.
* Custom Workflow: pre-built [Workflow](/workflows/what-is-workflows.md) templates for common tasks like detection, counting, or visualization.

You can also type a prompt directly in the Roboflow Agent input at the top of the modal to start a guided conversation about building with the model.

### Use a Universe Model in a Workflow

You can use Universe models in Workflows.

To use a model, open Roboflow Workflows, [create a Workflow](/workflows/create-a-workflow.md), then add a model block:

<figure><img src="/files/Kcefisqh4VHsxYlPlpD9" alt=""><figcaption></figcaption></figure>

Click "Model" in the block editor to select a model:

<figure><img src="/files/vEe3NmuWH6wasadsq4rH" alt=""><figcaption></figcaption></figure>

Then, click the Public Models tab:

<figure><img src="/files/CLx1X78nLtb11azacYxJ" alt=""><figcaption></figcaption></figure>

Go back to the Universe model page that corresponds with the model you want to use and click the copy icon to copy the model ID. This appears at the top of the model page:

<figure><img src="/files/c8Mu8Y59qHLyYqoK4dha" alt=""><figcaption></figcaption></figure>

Then, paste the model ID into the Public Models search bar:

<figure><img src="/files/hlNnGm8yeUR9VbNFYSVK" alt=""><figcaption></figcaption></figure>

Click "Use model ID" to use the model.

Your Workflow block will now use the Universe model.


---

# Agent Instructions
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## 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:

```
GET https://docs.roboflow.com/universe/find-a-model-on-universe.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.
