External Models - Kore.ai Docs

Connect commercial and custom models to Agent Platform.

Overview

External models are AI models hosted outside the platform. Once connected, they can be used across Agent Platform in Agentic Apps, Prompt Studio, Tools, and Evaluation Studio. Supported Providers (Easy Integration):

Provider	Authentication	Tool Calling
OpenAI	API Key	✓
Anthropic	API Key	✓
Google	API Key	✓
Cohere	API Key	✓
Azure OpenAI	API Key + Endpoint	✓
Amazon Bedrock	IAM Role ARN	✓
Vertex AI	API Key	✓
Microsoft Foundry	API Key / Service Principal	✓

Custom Models (API Integration): Connect any model via REST API endpoint. For the complete list of supported models, see Supported Models.

Manage Connected Models

View Models

Go to Models → External Models to see all connected models.

Manage Connections

Each model can have multiple connections with different API keys, enabling separate usage tracking and billing.

Action	Description
Inference Toggle	Enable/disable model availability across Platform
Edit	Update API key or credentials
Delete	Remove the connection

When adding multiple API keys for the same model, each connection must have a unique name and API key. In Agentic Apps, you can assign specific connections at the Agent or Supervisor level.

Add a Model via Easy Integration

Use Easy Integration for commercial providers with API keys or IAM roles.

Standard Providers (OpenAI, Anthropic, Google, Cohere)

Go to Models → External Models → Add a model.
Select Easy Integration → click Next.
Choose your provider → click Next.
Select a model from the supported list.
Enter a Connection name and your API key.
Click Confirm.

The model is now available across Agent Platform.

Amazon Bedrock

Bedrock uses IAM role-based authentication instead of API keys. Prerequisites: Create an IAM role in AWS with Bedrock permissions and a trust policy allowing Agent Platform to assume the role. See Configuring Amazon Bedrock for IAM setup. Steps:

Go to Models → External Models → Add a model.
Select Easy Integration → AWS Bedrock → Next.
Configure credentials and model details:

Field	Description
IAM Role ARN	Your IAM role with Bedrock permissions
Trusted Principal ARN	Platform’s AWS principal (pre-populated)
Model Name	Internal identifier
Model ID	Bedrock Model ID or Endpoint ID
Region	AWS region of the model
Headers	Optional custom headers

Configure model settings using Default or Existing Provider Structures.
Click Confirm.

Vertex AI

Vertex AI uses API key authentication to access Gemini models (2.5 and 3.0 families) from your Google Cloud account. Prerequisites: Create an API key in your Google Cloud account with Vertex AI API access.

New users: Use the express mode setup to generate an API key automatically, then manage keys under APIs & Services > Credentials.
Existing users: Enable the Vertex AI API, create a service account (vertex-ai-runner) with the Vertex AI Platform Express User role, create an API key linked to that service account under APIs & Services > Credentials, and store the key securely.

Steps:

Go to Models → External Models → Add a model.
Select Easy Integration → Vertex AI → Next.
Choose a configuration method:

Option A: Manual Setup

Field	Description
Model	Select a Gemini model from the dropdown
Connection name	Internal identifier for this connection
API key	Your Google Vertex AI API key
Project ID	(Optional) Your Google Cloud project identifier
Region	(Optional) Google Cloud region where models are deployed

Click Confirm to save.

Option B: Import from cURL Enter a Connection name, paste a cURL command in the text area, click Fetch to extract the configuration, then click Confirm.

The model is now available across Agent Platform.

Microsoft Foundry

Microsoft Foundry supports two authentication methods: entering credentials manually or using an Azure Active Directory Service Principal. Steps:

Go to Models → External Models → Add a model.
Select Easy Integration → Microsoft Foundry → Next.
Choose an authentication method:

Option A: Enter Manually Directly provide credentials from your model’s Details page in Microsoft Foundry.

Field	Description
Connection name	Unique name to identify this connection
Target URI	Endpoint URI from your model’s Details page
Key	API key from your model’s Details page
Deployment name	Deployment name as defined in Microsoft Foundry

Option B: Use Service Principal Authenticate through an Azure Active Directory Service Principal. Requires a pre-configured Microsoft Foundry connection. Enter a unique Connection name to identify this connection.

If no connection exists, click Configure Service Principal and complete these steps:

In the Azure Portal, go to App registrations → + New registration. Enter a name, select account type, and click Register.
Copy the Application (Client) ID and Directory (Tenant) ID from the Overview page.
Go to Certificates & secrets → + New client secret. Set an expiry and copy the Value immediately.
In your resource group, go to Access control (IAM) → Add role assignment. Assign a role (for example. Contributor) and select your registered app.
Click Configure Service Principal in the Platform, enter a Connection name, and fill in Tenant ID, Application (Client) ID, Client Secret, and Subscription ID. Click Test, then Save.
Configure model settings under Model configurations. Enable the features your model supports:

Feature	Description
Structured Response	JSON-formatted outputs for Prompts and Tools
Tool Calling	Function calling for Agentic Apps and AI nodes
Parallel Tool Calling	Multiple tool calls per request
Streaming	Real-time token generation
Data Generation	Synthetic data generation in Prompt Studio
Modalities	Text-to-Text, Text-to-Image, Image-to-Text, Audio-to-Text

You must enable Tool calling for the model to work in Agentic Apps.

Under Body, specify the model name and select a provider to set the API reference:

Template	Use When
OpenAI Chat Completions	Model follows OpenAI chat API format
Anthropic Messages	Model follows Anthropic messages API format

Click Save as draft to store without activating, or Confirm to finalize.

The model is now listed in the External Models tab and available in Prompts, Tools, and Agentic Apps.

Add a Model via API Integration

Use API Integration for custom endpoints or self-hosted models.

For Agentic Apps compatibility, custom models must support tool calling and follow OpenAI or Anthropic request/response structures.

Steps

Go to Models → External Models → Add a model.
Select Custom Integration → click Next.
Enter basic configuration:

Field	Description
Connection Name	Unique identifier
Model Endpoint URL	Full API endpoint URL
Authorization Profile	Select configured auth profile or None
Headers	Optional key-value pairs for requests

Configure model settings using Default or Existing Provider Structures.
Click Confirm

Model Configuration Modes

When using API Integration or advanced Bedrock setup, choose one of these configuration modes:

Default Mode

Manually configure request/response handling for complete control. 1. Define Variables

Variable Type	Description
Prompt	Primary input text (required)
System Prompt	System instructions (optional)
Examples	Few-shot examples (optional)
Custom Variables	Additional dynamic inputs with name, display name, and data type

2. Configure Request Body Create JSON payload using {{variable}} placeholders:

{
  "model": "your-model-name",
  "messages": [
    {"role": "system", "content": "{{system.prompt}}"},
    {"role": "user", "content": "{{prompt}}"}
  ],
  "max_tokens": 1000,
  "temperature": 0.7
}

3. Map Response JSON Paths Click Test to send a sample request, then configure extraction paths:

Field	Description	Example
Output Path	Location of generated text	`choices[0].message.content`
Input Tokens	Input token count	`usage.prompt_tokens`
Output Tokens	Output token count	`usage.completion_tokens`

Existing Provider Structures Mode

Automatically apply pre-defined schemas from known providers. Recommended when your model follows a standard API format. 1. Enable Model Features Enable only the features your model supports. Enabling unsupported features may cause unexpected behavior.

Feature	Description
Structured Response	JSON-formatted outputs for Prompts and Tools
Data Generation	Synthetic data generation in Prompt Studio
Streaming	Real-time token generation for Agentic Apps
Support tools	Function calling for Agentic Apps and AI nodes
Parallel tool calling	Multiple tool calls per request
Modalities	Text-to-Text, Text-to-Image, Image-to-Text, Audio-to-Text

2. Configure Model Request Settings (Body) a. Model Name: Specify the model identifier to include in the request body. b. API reference: Select a provider to set the API reference.

Template	Use When
OpenAI (Chat Completions)	Model follows OpenAI chat API format
Anthropic (Messages)	Model follows Anthropic messages API format
Google (Gemini)	Model follows Gemini API format

c. Model Parameters: Configure how the model generates responses.

Option	Description
Defaults	Configure common model parameters such as temperature, max tokens, frequency penalty, presence penalty, and top_p using the standard controls. Available parameters vary based on the selected API reference.
Custom	Use the custom parameters JSON editor to define model-specific hyperparameters or vendor-specific settings that are not available in the standard controls. The JSON configuration is saved with the model, and only the parameters defined in it are sent with each model request.

Example custom parameters

{
  "max_completion_tokens": 2000,
  "verbosity": "medium"
}

Troubleshooting

Issue	Solution
Test fails	Verify endpoint URL and authentication
Empty response	Check JSON path mapping matches response structure
Model not in dropdowns	Ensure Inference toggle is ON
Tool calling not working	Verify model supports it and feature is enabled
Bedrock connection fails	Check IAM role ARN and trust policy configuration
Vertex AI auth error	Ensure API key is valid and not an OAuth token; check that Vertex AI API is enabled for your project
Microsoft Foundry connection fails	Verify Target URI, API key, and deployment name; for Service Principal, confirm Tenant ID, Client ID, and Client Secret are correct

​Overview

​Manage Connected Models

​View Models

​Manage Connections

​Add a Model via Easy Integration

​Standard Providers (OpenAI, Anthropic, Google, Cohere)

​Amazon Bedrock

​Vertex AI

​Microsoft Foundry

​Add a Model via API Integration

​Steps

​Model Configuration Modes

​Default Mode

​Existing Provider Structures Mode

​Troubleshooting

Overview

Manage Connected Models

View Models

Manage Connections

Add a Model via Easy Integration

Standard Providers (OpenAI, Anthropic, Google, Cohere)

Amazon Bedrock

Vertex AI

Microsoft Foundry

Add a Model via API Integration

Steps

Model Configuration Modes

Default Mode

Existing Provider Structures Mode

Troubleshooting