[Amazon SageMaker](https://aws.amazon.com/sagemaker/) is a fully managed machine learning service that provides infrastructure and tools for building, training, and deploying ML models at scale. The Strands Agents SDK implements a SageMaker provider, allowing you to run agents against models deployed on SageMaker inference endpoints, including both pre-trained models from SageMaker JumpStart and custom fine-tuned models. The provider is designed to work with models that support OpenAI-compatible chat completion APIs.

For example, you can expose models like [Mistral-Small-24B-Instruct-2501](https://aws.amazon.com/blogs/machine-learning/mistral-small-24b-instruct-2501-is-now-available-on-sagemaker-jumpstart-and-amazon-bedrock-marketplace/) on SageMaker, which has demonstrated reliable performance for conversational AI and tool calling scenarios.

## Installation

SageMaker is configured as an optional dependency in Strands Agents. To install, run:

```bash
pip install 'strands-agents[sagemaker]'
```

## Usage

After installing the SageMaker dependencies, you can import and initialize the Strands Agents’ SageMaker provider as follows:

```python
from strands import Agent
from strands.models.sagemaker import SageMakerAIModel
from strands.vended_tools import notebook

model = SageMakerAIModel(
    endpoint_config={
        "endpoint_name": "my-llm-endpoint",
        "region_name": "us-west-2",
    },
    payload_config={
        "max_tokens": 1000,
        "temperature": 0.7,
        "stream": True,
    }
)

agent = Agent(model=model, tools=[notebook])
response = agent('Create a notebook named "ideas" and add three project ideas.')
```

**Note**: Tool calling support varies by model. Models like [Mistral-Small-24B-Instruct-2501](https://aws.amazon.com/blogs/machine-learning/mistral-small-24b-instruct-2501-is-now-available-on-sagemaker-jumpstart-and-amazon-bedrock-marketplace/) have demonstrated reliable tool calling capabilities, but not all models deployed on SageMaker support this feature. Verify your model’s capabilities before implementing tool-based workflows.

## Configuration

### Endpoint Configuration

The `endpoint_config` configures the SageMaker endpoint connection:

| Parameter | Description | Required | Example |
| --- | --- | --- | --- |
| `endpoint_name` | Name of the SageMaker endpoint | Yes | `"my-llm-endpoint"` |
| `region_name` | AWS region where the endpoint is deployed | Yes | `"us-west-2"` |
| `inference_component_name` | Name of the inference component | No | `"my-component"` |
| `target_model` | Specific model to invoke (multi-model endpoints) | No | `"model-a.tar.gz"` |
| `target_variant` | Production variant to invoke | No | `"variant-1"` |

### Payload Configuration

The `payload_config` configures the model inference parameters:

| Parameter | Description | Default | Example |
| --- | --- | --- | --- |
| `max_tokens` | Maximum number of tokens to generate | Required | `1000` |
| `stream` | Enable streaming responses | `True` | `True` |
| `temperature` | Sampling temperature (0.0 to 2.0) | Optional | `0.7` |
| `top_p` | Nucleus sampling parameter (0.0 to 1.0) | Optional | `0.9` |
| `top_k` | Top-k sampling parameter | Optional | `50` |
| `stop` | List of stop sequences | Optional | `["Human:", "AI:"]` |

## Model Compatibility

The SageMaker provider is designed to work with models that support OpenAI-compatible chat completion APIs. During development and testing, the provider has been validated with [Mistral-Small-24B-Instruct-2501](https://aws.amazon.com/blogs/machine-learning/mistral-small-24b-instruct-2501-is-now-available-on-sagemaker-jumpstart-and-amazon-bedrock-marketplace/), which demonstrated reliable performance across various conversational AI tasks.

### Important Considerations

-   **Model Performance**: Results and capabilities vary significantly depending on the specific model deployed to your SageMaker endpoint
-   **Tool Calling Support**: Not all models deployed on SageMaker support tool calling. Verify your model’s capabilities before implementing tool-based workflows
-   **API Compatibility**: Ensure your deployed model accepts and returns data in the OpenAI chat completion format

Test your model deployment against your use case before deploying to production.

## Troubleshooting

### Module Not Found

If you encounter `ModuleNotFoundError: No module named 'boto3'` or similar, install the SageMaker dependencies:

```bash
pip install 'strands-agents[sagemaker]'
```

### Authentication

The SageMaker provider uses standard AWS authentication methods (credentials file, environment variables, IAM roles, or AWS SSO). Ensure your AWS credentials have the necessary SageMaker invoke permissions.

### Model Compatibility

Ensure your deployed model supports OpenAI-compatible chat completion APIs and verify tool calling capabilities if needed. Refer to the [Model Compatibility](#model-compatibility) section above for detailed requirements and testing recommendations.

## References

-   [Python API Reference](/docs/api/python/strands.models.model)
-   [Amazon SageMaker Documentation](https://docs.aws.amazon.com/sagemaker/)
-   [SageMaker Runtime API](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_runtime_InvokeEndpoint.html)

## Implementation

### Python

- [harness-sdk/strands-py/src/strands/models/sagemaker.py](https://github.com/strands-agents/harness-sdk/blob/main/strands-py/src/strands/models/sagemaker.py)