`strands.models.model` ¶

Abstract base class for Agent model providers.

`Messages = List[Message]` `module-attribute` ¶

A list of messages representing a conversation.

`T = TypeVar('T', bound=BaseModel)` `module-attribute` ¶

`ToolChoice = Union[ToolChoiceAutoDict, ToolChoiceAnyDict, ToolChoiceToolDict]` `module-attribute` ¶

Configuration for how the model should choose tools.

"auto": The model decides whether to use tools based on the context
"any": The model must use at least one tool (any tool)
"tool": The model must use the specified tool

`logger = logging.getLogger(name)` `module-attribute` ¶

`Model` ¶

Bases: ABC

Abstract base class for Agent model providers.

This class defines the interface for all model implementations in the Strands Agents SDK. It provides a standardized way to configure and process requests for different AI model providers.

Source code in strands/models/model.py

class Model(abc.ABC):
    """Abstract base class for Agent model providers.

    This class defines the interface for all model implementations in the Strands Agents SDK. It provides a
    standardized way to configure and process requests for different AI model providers.
    """

    @abc.abstractmethod
    # pragma: no cover
    def update_config(self, **model_config: Any) -> None:
        """Update the model configuration with the provided arguments.

        Args:
            **model_config: Configuration overrides.
        """
        pass

    @abc.abstractmethod
    # pragma: no cover
    def get_config(self) -> Any:
        """Return the model configuration.

        Returns:
            The model's configuration.
        """
        pass

    @abc.abstractmethod
    # pragma: no cover
    def structured_output(
        self, output_model: Type[T], prompt: Messages, system_prompt: Optional[str] = None, **kwargs: Any
    ) -> AsyncGenerator[dict[str, Union[T, Any]], None]:
        """Get structured output from the model.

        Args:
            output_model: The output model to use for the agent.
            prompt: The prompt messages to use for the agent.
            system_prompt: System prompt to provide context to the model.
            **kwargs: Additional keyword arguments for future extensibility.

        Yields:
            Model events with the last being the structured output.

        Raises:
            ValidationException: The response format from the model does not match the output_model
        """
        pass

    @abc.abstractmethod
    # pragma: no cover
    def stream(
        self,
        messages: Messages,
        tool_specs: Optional[list[ToolSpec]] = None,
        system_prompt: Optional[str] = None,
        *,
        tool_choice: ToolChoice | None = None,
        system_prompt_content: list[SystemContentBlock] | None = None,
        **kwargs: Any,
    ) -> AsyncIterable[StreamEvent]:
        """Stream conversation with the model.

        This method handles the full lifecycle of conversing with the model:

        1. Format the messages, tool specs, and configuration into a streaming request
        2. Send the request to the model
        3. Yield the formatted message chunks

        Args:
            messages: List of message objects to be processed by the model.
            tool_specs: List of tool specifications to make available to the model.
            system_prompt: System prompt to provide context to the model.
            tool_choice: Selection strategy for tool invocation.
            system_prompt_content: System prompt content blocks for advanced features like caching.
            **kwargs: Additional keyword arguments for future extensibility.

        Yields:
            Formatted message chunks from the model.

        Raises:
            ModelThrottledException: When the model service is throttling requests from the client.
        """
        pass

`get_config()` `abstractmethod` ¶

Return the model configuration.

Returns:

Type	Description
`Any`	The model's configuration.

Source code in strands/models/model.py

@abc.abstractmethod
# pragma: no cover
def get_config(self) -> Any:
    """Return the model configuration.

    Returns:
        The model's configuration.
    """
    pass

`stream(messages, tool_specs=None, system_prompt=None, *, tool_choice=None, system_prompt_content=None, **kwargs)` `abstractmethod` ¶

Stream conversation with the model.

This method handles the full lifecycle of conversing with the model:

Format the messages, tool specs, and configuration into a streaming request
Send the request to the model
Yield the formatted message chunks

Parameters:

Name	Type	Description	Default
`messages`	`Messages`	List of message objects to be processed by the model.	required
`tool_specs`	`Optional[list[ToolSpec]]`	List of tool specifications to make available to the model.	`None`
`system_prompt`	`Optional[str]`	System prompt to provide context to the model.	`None`
`tool_choice`	`ToolChoice \| None`	Selection strategy for tool invocation.	`None`
`system_prompt_content`	`list[SystemContentBlock] \| None`	System prompt content blocks for advanced features like caching.	`None`
`**kwargs`	`Any`	Additional keyword arguments for future extensibility.	`{}`

Yields:

Type	Description
`AsyncIterable[StreamEvent]`	Formatted message chunks from the model.

Raises:

Type	Description
`ModelThrottledException`	When the model service is throttling requests from the client.

Source code in strands/models/model.py

@abc.abstractmethod
# pragma: no cover
def stream(
    self,
    messages: Messages,
    tool_specs: Optional[list[ToolSpec]] = None,
    system_prompt: Optional[str] = None,
    *,
    tool_choice: ToolChoice | None = None,
    system_prompt_content: list[SystemContentBlock] | None = None,
    **kwargs: Any,
) -> AsyncIterable[StreamEvent]:
    """Stream conversation with the model.

    This method handles the full lifecycle of conversing with the model:

    1. Format the messages, tool specs, and configuration into a streaming request
    2. Send the request to the model
    3. Yield the formatted message chunks

    Args:
        messages: List of message objects to be processed by the model.
        tool_specs: List of tool specifications to make available to the model.
        system_prompt: System prompt to provide context to the model.
        tool_choice: Selection strategy for tool invocation.
        system_prompt_content: System prompt content blocks for advanced features like caching.
        **kwargs: Additional keyword arguments for future extensibility.

    Yields:
        Formatted message chunks from the model.

    Raises:
        ModelThrottledException: When the model service is throttling requests from the client.
    """
    pass

`structured_output(output_model, prompt, system_prompt=None, **kwargs)` `abstractmethod` ¶

Get structured output from the model.

Parameters:

Name	Type	Description	Default
`output_model`	`Type[T]`	The output model to use for the agent.	required
`prompt`	`Messages`	The prompt messages to use for the agent.	required
`system_prompt`	`Optional[str]`	System prompt to provide context to the model.	`None`
`**kwargs`	`Any`	Additional keyword arguments for future extensibility.	`{}`

Yields:

Type	Description
`AsyncGenerator[dict[str, Union[T, Any]], None]`	Model events with the last being the structured output.

Raises:

Type	Description
`ValidationException`	The response format from the model does not match the output_model

Source code in strands/models/model.py

@abc.abstractmethod
# pragma: no cover
def structured_output(
    self, output_model: Type[T], prompt: Messages, system_prompt: Optional[str] = None, **kwargs: Any
) -> AsyncGenerator[dict[str, Union[T, Any]], None]:
    """Get structured output from the model.

    Args:
        output_model: The output model to use for the agent.
        prompt: The prompt messages to use for the agent.
        system_prompt: System prompt to provide context to the model.
        **kwargs: Additional keyword arguments for future extensibility.

    Yields:
        Model events with the last being the structured output.

    Raises:
        ValidationException: The response format from the model does not match the output_model
    """
    pass

`update_config(**model_config)` `abstractmethod` ¶

Update the model configuration with the provided arguments.

Parameters:

Name	Type	Description	Default
`**model_config`	`Any`	Configuration overrides.	`{}`

Source code in strands/models/model.py

@abc.abstractmethod
# pragma: no cover
def update_config(self, **model_config: Any) -> None:
    """Update the model configuration with the provided arguments.

    Args:
        **model_config: Configuration overrides.
    """
    pass

`StreamEvent` ¶

Bases: TypedDict

The messages output stream.

Attributes:

Name	Type	Description
`contentBlockDelta`	`ContentBlockDeltaEvent`	Delta content for a content block.
`contentBlockStart`	`ContentBlockStartEvent`	Start of a content block.
`contentBlockStop`	`ContentBlockStopEvent`	End of a content block.
`internalServerException`	`ExceptionEvent`	Internal server error information.
`messageStart`	`MessageStartEvent`	Start of a message.
`messageStop`	`MessageStopEvent`	End of a message.
`metadata`	`MetadataEvent`	Metadata about the streaming response.
`modelStreamErrorException`	`ModelStreamErrorEvent`	Model streaming error information.
`serviceUnavailableException`	`ExceptionEvent`	Service unavailable error information.
`throttlingException`	`ExceptionEvent`	Throttling error information.
`validationException`	`ExceptionEvent`	Validation error information.

Source code in strands/types/streaming.py

class StreamEvent(TypedDict, total=False):
    """The messages output stream.

    Attributes:
        contentBlockDelta: Delta content for a content block.
        contentBlockStart: Start of a content block.
        contentBlockStop: End of a content block.
        internalServerException: Internal server error information.
        messageStart: Start of a message.
        messageStop: End of a message.
        metadata: Metadata about the streaming response.
        modelStreamErrorException: Model streaming error information.
        serviceUnavailableException: Service unavailable error information.
        throttlingException: Throttling error information.
        validationException: Validation error information.
    """

    contentBlockDelta: ContentBlockDeltaEvent
    contentBlockStart: ContentBlockStartEvent
    contentBlockStop: ContentBlockStopEvent
    internalServerException: ExceptionEvent
    messageStart: MessageStartEvent
    messageStop: MessageStopEvent
    metadata: MetadataEvent
    redactContent: RedactContentEvent
    modelStreamErrorException: ModelStreamErrorEvent
    serviceUnavailableException: ExceptionEvent
    throttlingException: ExceptionEvent
    validationException: ExceptionEvent

`SystemContentBlock` ¶

Bases: TypedDict

Contains configurations for instructions to provide the model for how to handle input.

Attributes:

Name	Type	Description
`cachePoint`	`CachePoint`	A cache point configuration to optimize conversation history.
`text`	`str`	A system prompt for the model.

Source code in strands/types/content.py

class SystemContentBlock(TypedDict, total=False):
    """Contains configurations for instructions to provide the model for how to handle input.

    Attributes:
        cachePoint: A cache point configuration to optimize conversation history.
        text: A system prompt for the model.
    """

    cachePoint: CachePoint
    text: str

`ToolSpec` ¶

Bases: TypedDict

Specification for a tool that can be used by an agent.

Attributes:

Name	Type	Description
`description`	`str`	A human-readable description of what the tool does.
`inputSchema`	`JSONSchema`	JSON Schema defining the expected input parameters.
`name`	`str`	The unique name of the tool.
`outputSchema`	`NotRequired[JSONSchema]`	Optional JSON Schema defining the expected output format. Note: Not all model providers support this field. Providers that don't support it should filter it out before sending to their API.

Source code in strands/types/tools.py

class ToolSpec(TypedDict):
    """Specification for a tool that can be used by an agent.

    Attributes:
        description: A human-readable description of what the tool does.
        inputSchema: JSON Schema defining the expected input parameters.
        name: The unique name of the tool.
        outputSchema: Optional JSON Schema defining the expected output format.
            Note: Not all model providers support this field. Providers that don't
            support it should filter it out before sending to their API.
    """

    description: str
    inputSchema: JSONSchema
    name: str
    outputSchema: NotRequired[JSONSchema]

strands.models.model ¶

Messages = List[Message] module-attribute ¶

T = TypeVar('T', bound=BaseModel) module-attribute ¶

ToolChoice = Union[ToolChoiceAutoDict, ToolChoiceAnyDict, ToolChoiceToolDict] module-attribute ¶

logger = logging.getLogger(__name__) module-attribute ¶

Model ¶

get_config() abstractmethod ¶

stream(messages, tool_specs=None, system_prompt=None, *, tool_choice=None, system_prompt_content=None, **kwargs) abstractmethod ¶

structured_output(output_model, prompt, system_prompt=None, **kwargs) abstractmethod ¶

update_config(**model_config) abstractmethod ¶

StreamEvent ¶

SystemContentBlock ¶

ToolSpec ¶

`strands.models.model` ¶

`Messages = List[Message]` `module-attribute` ¶

`T = TypeVar('T', bound=BaseModel)` `module-attribute` ¶

`ToolChoice = Union[ToolChoiceAutoDict, ToolChoiceAnyDict, ToolChoiceToolDict]` `module-attribute` ¶

`logger = logging.getLogger(name)` `module-attribute` ¶

`Model` ¶

`get_config()` `abstractmethod` ¶

`stream(messages, tool_specs=None, system_prompt=None, *, tool_choice=None, system_prompt_content=None, **kwargs)` `abstractmethod` ¶

`structured_output(output_model, prompt, system_prompt=None, **kwargs)` `abstractmethod` ¶

`update_config(**model_config)` `abstractmethod` ¶

`StreamEvent` ¶

`SystemContentBlock` ¶

`ToolSpec` ¶