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

# List Ml Models

GET https://platform.labric.co/api/v1/tools/ml-models

List the organization's ML models and the inputs each expects.

Returns each non-archived model with its serving status and prediction
interface: feature_columns (plus image_columns for image models) are the
fields each data row passed to the predict tool should contain, and
target_column is what the model predicts. Only models with status 'ready'
can serve predictions.

Reference: https://docs.labric.co/api-reference/labric-api/tools/list-ml-models

## OpenAPI Specification

```yaml
openapi: 3.1.0
info:
  title: openapi-tools
  version: 1.0.0
paths:
  /api/v1/tools/ml-models:
    get:
      operationId: list_ml_models
      summary: List Ml Models
      description: >-
        List the organization's ML models and the inputs each expects.


        Returns each non-archived model with its serving status and prediction

        interface: feature_columns (plus image_columns for image models) are the

        fields each data row passed to the predict tool should contain, and

        target_column is what the model predicts. Only models with status
        'ready'

        can serve predictions.
      tags:
        - tools
      parameters:
        - name: Authorization
          in: header
          description: Bearer authentication
          required: true
          schema:
            type: string
      responses:
        '200':
          description: OK
          content:
            application/json:
              schema:
                type: array
                items:
                  $ref: '#/components/schemas/ToolsMLModelSchema'
        '400':
          description: Bad Request
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/ErrorSchema'
        '401':
          description: Unauthorized
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/ErrorSchema'
        '403':
          description: Forbidden
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/ErrorSchema'
        '404':
          description: Not Found
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/ErrorSchema'
        '422':
          description: Unprocessable Content
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/ValidationErrorSchema'
        '500':
          description: Internal Server Error
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/ErrorSchema'
servers:
  - url: https://platform.labric.co
    description: https://platform.labric.co
components:
  schemas:
    ToolsMLModelSchema:
      type: object
      properties:
        id:
          type: string
          format: uuid
        name:
          type: string
        description:
          type:
            - string
            - 'null'
        task_type:
          type: string
        status:
          type:
            - string
            - 'null'
        target_column:
          type:
            - string
            - 'null'
        feature_columns:
          type:
            - array
            - 'null'
          items:
            type: string
        image_columns:
          type:
            - array
            - 'null'
          items:
            type: string
        problem_type:
          type:
            - string
            - 'null'
      required:
        - id
        - name
        - task_type
      description: >-
        Flat model summary for the SDK/MCP tools surface: everything a caller

        needs to pick a model and build a predict payload, without nested
        versions.
      title: ToolsMLModelSchema
    ErrorSchema:
      type: object
      properties:
        detail:
          type: string
      required:
        - detail
      title: ErrorSchema
    ValidationErrorSchema:
      type: object
      properties:
        detail:
          type: array
          items:
            type: object
            additionalProperties:
              description: Any type
      required:
        - detail
      description: Shape of Ninja's built-in 422 request-validation error response.
      title: ValidationErrorSchema
  securitySchemes:
    ApiKeyAuth:
      type: http
      scheme: bearer

```

## Examples



**Request**

```json
{}
```

**Response**

```json
[
  {
    "id": "a3f1c9d2-4b7e-4f9a-9c3d-2e5b7f8a1d6c",
    "name": "Customer Churn Predictor",
    "task_type": "classification",
    "description": "Predicts whether a customer will churn based on usage patterns and demographics.",
    "status": "ready",
    "target_column": "churned",
    "feature_columns": [
      "monthly_spend",
      "account_age_days",
      "num_support_tickets",
      "contract_type"
    ],
    "image_columns": null,
    "problem_type": "binary_classification"
  },
  {
    "id": "d9b7e3f4-2a1c-4e8b-9f7d-3c6a5b2e8f1d",
    "name": "Product Image Quality Classifier",
    "task_type": "image_classification",
    "description": "Classifies product images as high or low quality for catalog optimization.",
    "status": "ready",
    "target_column": "quality_label",
    "feature_columns": [
      "product_category",
      "upload_date"
    ],
    "image_columns": [
      "product_image"
    ],
    "problem_type": "multiclass_classification"
  },
  {
    "id": "f2c4a7b9-8d3e-4f1a-9b6c-7e5d2a1f3c4b",
    "name": "Sales Forecasting Model",
    "task_type": "regression",
    "description": "Forecasts monthly sales volume based on historical sales and marketing spend.",
    "status": "training",
    "target_column": "monthly_sales",
    "feature_columns": [
      "historical_sales",
      "marketing_budget",
      "seasonality_index"
    ],
    "image_columns": null,
    "problem_type": "regression"
  }
]
```

**SDK Code**

```python
import requests

url = "https://platform.labric.co/api/v1/tools/ml-models"

payload = {}
headers = {
    "Authorization": "Bearer <api_key>",
    "Content-Type": "application/json"
}

response = requests.get(url, json=payload, headers=headers)

print(response.json())
```

```javascript
const url = 'https://platform.labric.co/api/v1/tools/ml-models';
const options = {
  method: 'GET',
  headers: {Authorization: 'Bearer <api_key>', 'Content-Type': 'application/json'},
  body: '{}'
};

try {
  const response = await fetch(url, options);
  const data = await response.json();
  console.log(data);
} catch (error) {
  console.error(error);
}
```

```go
package main

import (
	"fmt"
	"strings"
	"net/http"
	"io"
)

func main() {

	url := "https://platform.labric.co/api/v1/tools/ml-models"

	payload := strings.NewReader("{}")

	req, _ := http.NewRequest("GET", url, payload)

	req.Header.Add("Authorization", "Bearer <api_key>")
	req.Header.Add("Content-Type", "application/json")

	res, _ := http.DefaultClient.Do(req)

	defer res.Body.Close()
	body, _ := io.ReadAll(res.Body)

	fmt.Println(res)
	fmt.Println(string(body))

}
```

```ruby
require 'uri'
require 'net/http'

url = URI("https://platform.labric.co/api/v1/tools/ml-models")

http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true

request = Net::HTTP::Get.new(url)
request["Authorization"] = 'Bearer <api_key>'
request["Content-Type"] = 'application/json'
request.body = "{}"

response = http.request(request)
puts response.read_body
```

```java
import com.mashape.unirest.http.HttpResponse;
import com.mashape.unirest.http.Unirest;

HttpResponse<String> response = Unirest.get("https://platform.labric.co/api/v1/tools/ml-models")
  .header("Authorization", "Bearer <api_key>")
  .header("Content-Type", "application/json")
  .body("{}")
  .asString();
```

```php
<?php
require_once('vendor/autoload.php');

$client = new \GuzzleHttp\Client();

$response = $client->request('GET', 'https://platform.labric.co/api/v1/tools/ml-models', [
  'body' => '{}',
  'headers' => [
    'Authorization' => 'Bearer <api_key>',
    'Content-Type' => 'application/json',
  ],
]);

echo $response->getBody();
```

```csharp
using RestSharp;

var client = new RestClient("https://platform.labric.co/api/v1/tools/ml-models");
var request = new RestRequest(Method.GET);
request.AddHeader("Authorization", "Bearer <api_key>");
request.AddHeader("Content-Type", "application/json");
request.AddParameter("application/json", "{}", ParameterType.RequestBody);
IRestResponse response = client.Execute(request);
```

```swift
import Foundation

let headers = [
  "Authorization": "Bearer <api_key>",
  "Content-Type": "application/json"
]
let parameters = [] as [String : Any]

let postData = JSONSerialization.data(withJSONObject: parameters, options: [])

let request = NSMutableURLRequest(url: NSURL(string: "https://platform.labric.co/api/v1/tools/ml-models")! as URL,
                                        cachePolicy: .useProtocolCachePolicy,
                                    timeoutInterval: 10.0)
request.httpMethod = "GET"
request.allHTTPHeaderFields = headers
request.httpBody = postData as Data

let session = URLSession.shared
let dataTask = session.dataTask(with: request as URLRequest, completionHandler: { (data, response, error) -> Void in
  if (error != nil) {
    print(error as Any)
  } else {
    let httpResponse = response as? HTTPURLResponse
    print(httpResponse)
  }
})

dataTask.resume()
```