> ## Documentation Index
> Fetch the complete documentation index at: https://koreai.mintlify.app/llms.txt
> Use this file to discover all available pages before exploring further.

# Models Overview

Configure and manage AI models for your agents, tools, and workflows.

***

Agent Platform is model-agnostic, supporting a wide range of AI models from commercial providers, open-source repositories, and custom fine-tuned deployments. Access Model Hub from the top navigation to manage all your models in one place.

Model Hub provides three ways to work with models:

| Model Type             | Description                                                                         | Best For                                          |
| ---------------------- | ----------------------------------------------------------------------------------- | ------------------------------------------------- |
| **External Models**    | Commercial models from OpenAI, Anthropic, Google, Azure, Cohere, and Amazon Bedrock | Production workloads requiring proven reliability |
| **Open-Source Models** | 30+ curated models plus any Hugging Face text generation model                      | Cost control, customization, data privacy         |
| **Fine-Tuned Models**  | Custom models trained on your enterprise data                                       | Domain-specific tasks, consistent outputs         |

## External Models

Connect commercial models with minimal setup using Easy Integration or API Integration.

### Supported Providers

| Provider           | Popular Models                                | Tool Calling |
| ------------------ | --------------------------------------------- | ------------ |
| **OpenAI**         | GPT-4o, GPT-4 Turbo, GPT-3.5 Turbo, o1 series | ✓            |
| **Anthropic**      | Claude 3.5 Sonnet, Claude 3 Opus/Sonnet/Haiku | ✓            |
| **Google**         | Gemini 1.5 Pro, Gemini 1.5 Flash              | ✓            |
| **Azure OpenAI**   | GPT-4o, GPT-4, GPT-3.5 Turbo                  | ✓            |
| **Cohere**         | Command R+, Command R                         | ✓            |
| **Amazon Bedrock** | Claude, Titan, Llama models via AWS           | ✓            |

### Adding an External Model

**Easy Integration** (recommended):

1. Go to **Models** → **External Models** → **Add a model**
2. Select **Easy Integration** and choose your provider
3. Enter your API credentials
4. Select the model and confirm

**API Integration** (for custom endpoints):

1. Select **API Integration** when adding a model
2. Configure the endpoint URL and authentication
3. Map request/response parameters
4. Test and save

<Note>Custom models must support tool calling and follow OpenAI or Anthropic API structures to work with Agentic Apps.</Note>

For detailed setup instructions, see [Add a Model using Easy Integration](/agent-platform/models/external-models#add-a-model-via-easy-integration) or [Add a Model using API Integration](/agent-platform/models/external-models#add-a-model-via-api-integration).

## Open-Source Models

Deploy from 30+ curated models or import any text generation model from Hugging Face.

### Deployment Options

| Option                  | Description                                                                |
| ----------------------- | -------------------------------------------------------------------------- |
| **Kore-Hosted**         | Select from curated models, optimize, and deploy on managed infrastructure |
| **Hugging Face Import** | Bring any compatible model from Hugging Face (public or private)           |

### Optimization Techniques

Before deployment, optimize models for better performance:

* **vLLM**: High-throughput inference optimization
* **CTranslate2 (CT2)**: Efficient inference with reduced memory footprint
* **No Optimization**: Deploy as-is for maximum compatibility

### Quick Deploy Steps

1. Go to **Models** → **Open-source models** → **Deploy a model**
2. Select a Kore-hosted model or import from Hugging Face
3. Choose optimization technique (optional)
4. Configure parameters and hardware
5. Click **Deploy**

For the complete model list and detailed instructions, see [Deploy an Open-Source Model](/agent-platform/models/open-source-models).

## Fine-Tuned Models

Create custom models trained on your enterprise data for domain-specific tasks.

### When to Fine-Tune

* Consistent output format required across responses
* Domain-specific terminology or jargon
* Unique tone, style, or brand voice
* Improved accuracy for specialized tasks

### Fine-Tuning Process

1. **Prepare Data**: Format training data as JSONL with conversation examples
2. **Select Base Model**: Choose from supported Kore-hosted or Hugging Face models
3. **Configure Training**: Select fine-tuning type (Full, LoRA, or QLoRA)
4. **Monitor Progress**: Track metrics via Weights & Biases integration
5. **Deploy**: Make the model available across Agent Platform

### Training Data Format

```json theme={null}
{"messages": [{"role": "system", "content": "You are a support agent."}, {"role": "user", "content": "Order status?"}, {"role": "assistant", "content": "I'll check your order status right away."}]}
{"messages": [{"role": "system", "content": "You are a support agent."}, {"role": "user", "content": "Return request"}, {"role": "assistant", "content": "I can help process your return."}]}
```

For step-by-step instructions, see [Create a Fine-Tuned Model](/agent-platform/models/fine-tuned-models#create-a-fine-tuned-model).

## Model Parameters

Configure generation behavior when using models across Agent Platform:

| Parameter          | Description                                               | Typical Range          |
| ------------------ | --------------------------------------------------------- | ---------------------- |
| **Temperature**    | Controls randomness. Lower = focused, higher = creative   | 0.0–2.0 (default: 0.7) |
| **Top P**          | Nucleus sampling—considers tokens within probability mass | 0.0–1.0 (default: 0.9) |
| **Top K**          | Limits selection to top K tokens                          | 1–100 (default: 50)    |
| **Max Tokens**     | Maximum output length                                     | Varies by model        |
| **Stop Sequences** | Strings that stop generation                              | Custom list            |

## Tool Calling Support

Not all models support tool calling, which is required for Agentic Apps. Use models with tool calling support for agent orchestration.

**Supported for Tool Calling:**

* OpenAI: GPT-4o, GPT-4 Turbo, GPT-3.5 Turbo
* Anthropic: Claude 3.5 Sonnet, Claude 3 series
* Google: Gemini 1.5 Pro, Gemini 1.5 Flash
* Azure OpenAI: GPT-4o, GPT-4, GPT-3.5 Turbo
* Amazon Bedrock: Models via supported providers

**Not Supported:**

* Kore-hosted open-source models
* Most Hugging Face imports
* Models without function calling capabilities

## Model Selection Guide

Choose the right model based on your use case:

| Use Case          | Recommended Models                    | Why                   |
| ----------------- | ------------------------------------- | --------------------- |
| Complex reasoning | GPT-4o, Claude 3 Opus                 | Highest accuracy      |
| Fast responses    | GPT-3.5, Claude 3 Haiku, Gemini Flash | Low latency           |
| Code generation   | GPT-4o, Claude 3.5 Sonnet             | Best code quality     |
| Cost-sensitive    | GPT-3.5, Claude 3 Haiku               | Lower token cost      |
| Long context      | Claude 3, Gemini 1.5                  | 100K+ token windows   |
| Data privacy      | Open-source (Kore-hosted)             | No external API calls |
| Real-time voice   | GPT-4o Realtime Preview               | Native voice support  |

## Structured Output

Enable consistent, parsable responses using JSON schemas.

**Supported:**

* External models (OpenAI, Anthropic, Google)
* Kore-hosted open-source models with vLLM or no optimization

**Not Supported:**

* CT2-optimized models
* Fine-tuned models
* Hugging Face imports
* Locally imported models

## Model Endpoint & API Keys

After deployment, each model provides:

* **API Endpoint**: Use models externally via REST API
* **API Keys**: Secure access tokens for endpoint authentication
* **Deployment History**: Track version changes

Access these from the model's three-dot menu → **API Endpoint**.

## Monitoring

Track model performance across Agent Platform:

* **Model Analytics Dashboard**: Token usage, latency, error rates
* **Model Traces**: Detailed request/response logs
* **Usage Summary**: Cost tracking by model

Access via **Settings** → **Monitoring** → **Analytics**.