feat: Add docs for Sagemaker model provider

docs/user-guide/concepts/model-providers/sagemaker.md

@@ -0,0 +1,104 @@
+# Amazon SageMaker
+
+[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_tools import calculator
+
+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=[calculator])
+response = agent("What is the square root of 64?")
+```
+
+**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 function/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
+
+For optimal results, we recommend testing your specific model deployment with your use case requirements before production deployment.
+
+## 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
+
+- [API Reference](../../../api-reference/models.md)
+- [Amazon SageMaker Documentation](https://docs.aws.amazon.com/sagemaker/)
+- [SageMaker Runtime API](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_runtime_InvokeEndpoint.html)

mkdocs.yml

@@ -94,6 +94,7 @@ nav:
         - MistralAI: user-guide/concepts/model-providers/mistral.md
         - Ollama: user-guide/concepts/model-providers/ollama.md
         - OpenAI: user-guide/concepts/model-providers/openai.md
+        - SageMaker: user-guide/concepts/model-providers/sagemaker.md
         - Writer: user-guide/concepts/model-providers/writer.md
         - Cohere: user-guide/concepts/model-providers/cohere.md
         - Custom Providers: user-guide/concepts/model-providers/custom_model_provider.md