# Universe Search

[Roboflow Universe](https://universe.roboflow.com) is the public catalog of community-shared projects, datasets, and models. The Python SDK exposes a low-level adapter you can use to search Universe programmatically.

## Search

```python
from roboflow.adapters import rfapi

results = rfapi.search_universe(
    "vehicles",
    api_key="YOUR_API_KEY",
    project_type="object-detection",  # optional
    limit=12,
    page=1,
)

for project in results.get("results", []):
    print(project["workspace"], project["project"], project["name"])
```

### Parameters

* `query` (str, positional) — search term.
* `api_key` (str, kw-only) — your Roboflow API key. Required for Universe rate limits.
* `project_type` (str, kw-only, optional) — filter by project type. One of `object-detection`, `classification`, `instance-segmentation`, `semantic-segmentation`, `keypoint-detection`.
* `limit` (int, kw-only, default `12`) — page size.
* `page` (int, kw-only, default `1`) — 1-indexed page number.

### Response

The function returns the JSON response body verbatim. Each result entry includes the workspace slug, project slug, project name, type, image count, and (for trained models) version metadata you can plug straight into `Roboflow().workspace(...).project(...).version(...).model.predict()`.

## Run a Universe model directly

Once you've found a model with `search_universe`, run inference on it the same way you would your own model:

```python
import roboflow

rf = roboflow.Roboflow(api_key="YOUR_API_KEY")
project = rf.workspace("roboflow-100").project("poker-cards-cxcvz")
model = project.version(1).model

predictions = model.predict("hand.jpg", confidence=50, overlap=30).json()
```

See [Run a Model on an Image](/developer/python-sdk/run-a-model-on-an-image.md) for details on `predict()` across task types.

## CLI equivalent

```bash
roboflow universe search vehicles --type object-detection --limit 12
```

See [Universe Search (CLI)](/developer/command-line-interface/universe-search.md).


---

# Agent Instructions: 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/developer/python-sdk/universe-search.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.