`strands.agent` ¶

This package provides the core Agent interface and supporting components for building AI agents with the SDK.

It includes:

Agent: The main interface for interacting with AI models and tools
ConversationManager: Classes for managing conversation history and context windows

`strands.agent.agent` ¶

Agent Interface.

This module implements the core Agent class that serves as the primary entry point for interacting with foundation models and tools in the SDK.

The Agent interface supports two complementary interaction patterns:

Natural language for conversation: agent("Analyze this data")
Method-style for direct tool access: agent.tool.tool_name(param1="value")

`Agent` ¶

Core Agent interface.

An agent orchestrates the following workflow:

Receives user input
Processes the input using a language model
Decides whether to use tools to gather information or perform actions
Executes those tools and receives results
Continues reasoning with the new information
Produces a final response

Source code in strands/agent/agent.py

class Agent:
    """Core Agent interface.

    An agent orchestrates the following workflow:

    1. Receives user input
    2. Processes the input using a language model
    3. Decides whether to use tools to gather information or perform actions
    4. Executes those tools and receives results
    5. Continues reasoning with the new information
    6. Produces a final response
    """

    # For backwards compatibility
    ToolCaller = _ToolCaller

    def __init__(
        self,
        model: Union[Model, str, None] = None,
        messages: Optional[Messages] = None,
        tools: Optional[list[Union[str, dict[str, str], "ToolProvider", Any]]] = None,
        system_prompt: Optional[str | list[SystemContentBlock]] = None,
        structured_output_model: Optional[Type[BaseModel]] = None,
        callback_handler: Optional[
            Union[Callable[..., Any], _DefaultCallbackHandlerSentinel]
        ] = _DEFAULT_CALLBACK_HANDLER,
        conversation_manager: Optional[ConversationManager] = None,
        record_direct_tool_call: bool = True,
        load_tools_from_directory: bool = False,
        trace_attributes: Optional[Mapping[str, AttributeValue]] = None,
        *,
        agent_id: Optional[str] = None,
        name: Optional[str] = None,
        description: Optional[str] = None,
        state: Optional[Union[AgentState, dict]] = None,
        hooks: Optional[list[HookProvider]] = None,
        session_manager: Optional[SessionManager] = None,
        tool_executor: Optional[ToolExecutor] = None,
    ):
        """Initialize the Agent with the specified configuration.

        Args:
            model: Provider for running inference or a string representing the model-id for Bedrock to use.
                Defaults to strands.models.BedrockModel if None.
            messages: List of initial messages to pre-load into the conversation.
                Defaults to an empty list if None.
            tools: List of tools to make available to the agent.
                Can be specified as:

                - String tool names (e.g., "retrieve")
                - File paths (e.g., "/path/to/tool.py")
                - Imported Python modules (e.g., from strands_tools import current_time)
                - Dictionaries with name/path keys (e.g., {"name": "tool_name", "path": "/path/to/tool.py"})
                - ToolProvider instances for managed tool collections
                - Functions decorated with `@strands.tool` decorator.

                If provided, only these tools will be available. If None, all tools will be available.
            system_prompt: System prompt to guide model behavior.
                Can be a string or a list of SystemContentBlock objects for advanced features like caching.
                If None, the model will behave according to its default settings.
            structured_output_model: Pydantic model type(s) for structured output.
                When specified, all agent calls will attempt to return structured output of this type.
                This can be overridden on the agent invocation.
                Defaults to None (no structured output).
            callback_handler: Callback for processing events as they happen during agent execution.
                If not provided (using the default), a new PrintingCallbackHandler instance is created.
                If explicitly set to None, null_callback_handler is used.
            conversation_manager: Manager for conversation history and context window.
                Defaults to strands.agent.conversation_manager.SlidingWindowConversationManager if None.
            record_direct_tool_call: Whether to record direct tool calls in message history.
                Defaults to True.
            load_tools_from_directory: Whether to load and automatically reload tools in the `./tools/` directory.
                Defaults to False.
            trace_attributes: Custom trace attributes to apply to the agent's trace span.
            agent_id: Optional ID for the agent, useful for session management and multi-agent scenarios.
                Defaults to "default".
            name: name of the Agent
                Defaults to "Strands Agents".
            description: description of what the Agent does
                Defaults to None.
            state: stateful information for the agent. Can be either an AgentState object, or a json serializable dict.
                Defaults to an empty AgentState object.
            hooks: hooks to be added to the agent hook registry
                Defaults to None.
            session_manager: Manager for handling agent sessions including conversation history and state.
                If provided, enables session-based persistence and state management.
            tool_executor: Definition of tool execution strategy (e.g., sequential, concurrent, etc.).

        Raises:
            ValueError: If agent id contains path separators.
        """
        self.model = BedrockModel() if not model else BedrockModel(model_id=model) if isinstance(model, str) else model
        self.messages = messages if messages is not None else []
        # initializing self._system_prompt for backwards compatibility
        self._system_prompt, self._system_prompt_content = self._initialize_system_prompt(system_prompt)
        self._default_structured_output_model = structured_output_model
        self.agent_id = _identifier.validate(agent_id or _DEFAULT_AGENT_ID, _identifier.Identifier.AGENT)
        self.name = name or _DEFAULT_AGENT_NAME
        self.description = description

        # If not provided, create a new PrintingCallbackHandler instance
        # If explicitly set to None, use null_callback_handler
        # Otherwise use the passed callback_handler
        self.callback_handler: Union[Callable[..., Any], PrintingCallbackHandler]
        if isinstance(callback_handler, _DefaultCallbackHandlerSentinel):
            self.callback_handler = PrintingCallbackHandler()
        elif callback_handler is None:
            self.callback_handler = null_callback_handler
        else:
            self.callback_handler = callback_handler

        self.conversation_manager = conversation_manager if conversation_manager else SlidingWindowConversationManager()

        # Process trace attributes to ensure they're of compatible types
        self.trace_attributes: dict[str, AttributeValue] = {}
        if trace_attributes:
            for k, v in trace_attributes.items():
                if isinstance(v, (str, int, float, bool)) or (
                    isinstance(v, list) and all(isinstance(x, (str, int, float, bool)) for x in v)
                ):
                    self.trace_attributes[k] = v

        self.record_direct_tool_call = record_direct_tool_call
        self.load_tools_from_directory = load_tools_from_directory

        self.tool_registry = ToolRegistry()

        # Process tool list if provided
        if tools is not None:
            self.tool_registry.process_tools(tools)

        # Initialize tools and configuration
        self.tool_registry.initialize_tools(self.load_tools_from_directory)
        if load_tools_from_directory:
            self.tool_watcher = ToolWatcher(tool_registry=self.tool_registry)

        self.event_loop_metrics = EventLoopMetrics()

        # Initialize tracer instance (no-op if not configured)
        self.tracer = get_tracer()
        self.trace_span: Optional[trace_api.Span] = None

        # Initialize agent state management
        if state is not None:
            if isinstance(state, dict):
                self.state = AgentState(state)
            elif isinstance(state, AgentState):
                self.state = state
            else:
                raise ValueError("state must be an AgentState object or a dict")
        else:
            self.state = AgentState()

        self.tool_caller = _ToolCaller(self)

        self.hooks = HookRegistry()

        self._interrupt_state = _InterruptState()

        # Initialize session management functionality
        self._session_manager = session_manager
        if self._session_manager:
            self.hooks.add_hook(self._session_manager)

        self.tool_executor = tool_executor or ConcurrentToolExecutor()

        if hooks:
            for hook in hooks:
                self.hooks.add_hook(hook)
        self.hooks.invoke_callbacks(AgentInitializedEvent(agent=self))

    @property
    def system_prompt(self) -> str | None:
        """Get the system prompt as a string for backwards compatibility.

        Returns the system prompt as a concatenated string when it contains text content,
        or None if no text content is present. This maintains backwards compatibility
        with existing code that expects system_prompt to be a string.

        Returns:
            The system prompt as a string, or None if no text content exists.
        """
        return self._system_prompt

    @system_prompt.setter
    def system_prompt(self, value: str | list[SystemContentBlock] | None) -> None:
        """Set the system prompt and update internal content representation.

        Accepts either a string or list of SystemContentBlock objects.
        When set, both the backwards-compatible string representation and the internal
        content block representation are updated to maintain consistency.

        Args:
            value: System prompt as string, list of SystemContentBlock objects, or None.
                  - str: Simple text prompt (most common use case)
                  - list[SystemContentBlock]: Content blocks with features like caching
                  - None: Clear the system prompt
        """
        self._system_prompt, self._system_prompt_content = self._initialize_system_prompt(value)

    @property
    def tool(self) -> _ToolCaller:
        """Call tool as a function.

        Returns:
            Tool caller through which user can invoke tool as a function.

        Example:
            ```
            agent = Agent(tools=[calculator])
            agent.tool.calculator(...)
            ```
        """
        return self.tool_caller

    @property
    def tool_names(self) -> list[str]:
        """Get a list of all registered tool names.

        Returns:
            Names of all tools available to this agent.
        """
        all_tools = self.tool_registry.get_all_tools_config()
        return list(all_tools.keys())

    def __call__(
        self,
        prompt: AgentInput = None,
        *,
        invocation_state: dict[str, Any] | None = None,
        structured_output_model: Type[BaseModel] | None = None,
        **kwargs: Any,
    ) -> AgentResult:
        """Process a natural language prompt through the agent's event loop.

        This method implements the conversational interface with multiple input patterns:
        - String input: `agent("hello!")`
        - ContentBlock list: `agent([{"text": "hello"}, {"image": {...}}])`
        - Message list: `agent([{"role": "user", "content": [{"text": "hello"}]}])`
        - No input: `agent()` - uses existing conversation history

        Args:
            prompt: User input in various formats:
                - str: Simple text input
                - list[ContentBlock]: Multi-modal content blocks
                - list[Message]: Complete messages with roles
                - None: Use existing conversation history
            invocation_state: Additional parameters to pass through the event loop.
            structured_output_model: Pydantic model type(s) for structured output (overrides agent default).
            **kwargs: Additional parameters to pass through the event loop.[Deprecating]

        Returns:
            Result object containing:

                - stop_reason: Why the event loop stopped (e.g., "end_turn", "max_tokens")
                - message: The final message from the model
                - metrics: Performance metrics from the event loop
                - state: The final state of the event loop
                - structured_output: Parsed structured output when structured_output_model was specified
        """
        return run_async(
            lambda: self.invoke_async(
                prompt, invocation_state=invocation_state, structured_output_model=structured_output_model, **kwargs
            )
        )

    async def invoke_async(
        self,
        prompt: AgentInput = None,
        *,
        invocation_state: dict[str, Any] | None = None,
        structured_output_model: Type[BaseModel] | None = None,
        **kwargs: Any,
    ) -> AgentResult:
        """Process a natural language prompt through the agent's event loop.

        This method implements the conversational interface with multiple input patterns:
        - String input: Simple text input
        - ContentBlock list: Multi-modal content blocks
        - Message list: Complete messages with roles
        - No input: Use existing conversation history

        Args:
            prompt: User input in various formats:
                - str: Simple text input
                - list[ContentBlock]: Multi-modal content blocks
                - list[Message]: Complete messages with roles
                - None: Use existing conversation history
            invocation_state: Additional parameters to pass through the event loop.
            structured_output_model: Pydantic model type(s) for structured output (overrides agent default).
            **kwargs: Additional parameters to pass through the event loop.[Deprecating]

        Returns:
            Result: object containing:

                - stop_reason: Why the event loop stopped (e.g., "end_turn", "max_tokens")
                - message: The final message from the model
                - metrics: Performance metrics from the event loop
                - state: The final state of the event loop
        """
        events = self.stream_async(
            prompt, invocation_state=invocation_state, structured_output_model=structured_output_model, **kwargs
        )
        async for event in events:
            _ = event

        return cast(AgentResult, event["result"])

    def structured_output(self, output_model: Type[T], prompt: AgentInput = None) -> T:
        """This method allows you to get structured output from the agent.

        If you pass in a prompt, it will be used temporarily without adding it to the conversation history.
        If you don't pass in a prompt, it will use only the existing conversation history to respond.

        For smaller models, you may want to use the optional prompt to add additional instructions to explicitly
        instruct the model to output the structured data.

        Args:
            output_model: The output model (a JSON schema written as a Pydantic BaseModel)
                that the agent will use when responding.
            prompt: The prompt to use for the agent in various formats:
                - str: Simple text input
                - list[ContentBlock]: Multi-modal content blocks
                - list[Message]: Complete messages with roles
                - None: Use existing conversation history

        Raises:
            ValueError: If no conversation history or prompt is provided.
        """
        warnings.warn(
            "Agent.structured_output method is deprecated."
            " You should pass in `structured_output_model` directly into the agent invocation."
            " see: https://strandsagents.com/latest/documentation/docs/user-guide/concepts/agents/structured-output/",
            category=DeprecationWarning,
            stacklevel=2,
        )

        return run_async(lambda: self.structured_output_async(output_model, prompt))

    async def structured_output_async(self, output_model: Type[T], prompt: AgentInput = None) -> T:
        """This method allows you to get structured output from the agent.

        If you pass in a prompt, it will be used temporarily without adding it to the conversation history.
        If you don't pass in a prompt, it will use only the existing conversation history to respond.

        For smaller models, you may want to use the optional prompt to add additional instructions to explicitly
        instruct the model to output the structured data.

        Args:
            output_model: The output model (a JSON schema written as a Pydantic BaseModel)
                that the agent will use when responding.
            prompt: The prompt to use for the agent (will not be added to conversation history).

        Raises:
            ValueError: If no conversation history or prompt is provided.
        -
        """
        if self._interrupt_state.activated:
            raise RuntimeError("cannot call structured output during interrupt")

        warnings.warn(
            "Agent.structured_output_async method is deprecated."
            " You should pass in `structured_output_model` directly into the agent invocation."
            " see: https://strandsagents.com/latest/documentation/docs/user-guide/concepts/agents/structured-output/",
            category=DeprecationWarning,
            stacklevel=2,
        )
        await self.hooks.invoke_callbacks_async(BeforeInvocationEvent(agent=self))
        with self.tracer.tracer.start_as_current_span(
            "execute_structured_output", kind=trace_api.SpanKind.CLIENT
        ) as structured_output_span:
            try:
                if not self.messages and not prompt:
                    raise ValueError("No conversation history or prompt provided")

                temp_messages: Messages = self.messages + await self._convert_prompt_to_messages(prompt)

                structured_output_span.set_attributes(
                    {
                        "gen_ai.system": "strands-agents",
                        "gen_ai.agent.name": self.name,
                        "gen_ai.agent.id": self.agent_id,
                        "gen_ai.operation.name": "execute_structured_output",
                    }
                )
                if self.system_prompt:
                    structured_output_span.add_event(
                        "gen_ai.system.message",
                        attributes={"role": "system", "content": serialize([{"text": self.system_prompt}])},
                    )
                for message in temp_messages:
                    structured_output_span.add_event(
                        f"gen_ai.{message['role']}.message",
                        attributes={"role": message["role"], "content": serialize(message["content"])},
                    )
                events = self.model.structured_output(output_model, temp_messages, system_prompt=self.system_prompt)
                async for event in events:
                    if isinstance(event, TypedEvent):
                        event.prepare(invocation_state={})
                        if event.is_callback_event:
                            self.callback_handler(**event.as_dict())

                structured_output_span.add_event(
                    "gen_ai.choice", attributes={"message": serialize(event["output"].model_dump())}
                )
                return event["output"]

            finally:
                await self.hooks.invoke_callbacks_async(AfterInvocationEvent(agent=self))

    def cleanup(self) -> None:
        """Clean up resources used by the agent.

        This method cleans up all tool providers that require explicit cleanup,
        such as MCP clients. It should be called when the agent is no longer needed
        to ensure proper resource cleanup.

        Note: This method uses a "belt and braces" approach with automatic cleanup
        through finalizers as a fallback, but explicit cleanup is recommended.
        """
        self.tool_registry.cleanup()

    def __del__(self) -> None:
        """Clean up resources when agent is garbage collected."""
        # __del__ is called even when an exception is thrown in the constructor,
        # so there is no guarantee tool_registry was set..
        if hasattr(self, "tool_registry"):
            self.tool_registry.cleanup()

    async def stream_async(
        self,
        prompt: AgentInput = None,
        *,
        invocation_state: dict[str, Any] | None = None,
        structured_output_model: Type[BaseModel] | None = None,
        **kwargs: Any,
    ) -> AsyncIterator[Any]:
        """Process a natural language prompt and yield events as an async iterator.

        This method provides an asynchronous interface for streaming agent events with multiple input patterns:
        - String input: Simple text input
        - ContentBlock list: Multi-modal content blocks
        - Message list: Complete messages with roles
        - No input: Use existing conversation history

        Args:
            prompt: User input in various formats:
                - str: Simple text input
                - list[ContentBlock]: Multi-modal content blocks
                - list[Message]: Complete messages with roles
                - None: Use existing conversation history
            invocation_state: Additional parameters to pass through the event loop.
            structured_output_model: Pydantic model type(s) for structured output (overrides agent default).
            **kwargs: Additional parameters to pass to the event loop.[Deprecating]

        Yields:
            An async iterator that yields events. Each event is a dictionary containing
               information about the current state of processing, such as:

                - data: Text content being generated
                - complete: Whether this is the final chunk
                - current_tool_use: Information about tools being executed
                - And other event data provided by the callback handler

        Raises:
            Exception: Any exceptions from the agent invocation will be propagated to the caller.

        Example:
            ```python
            async for event in agent.stream_async("Analyze this data"):
                if "data" in event:
                    yield event["data"]
            ```
        """
        self._interrupt_state.resume(prompt)

        merged_state = {}
        if kwargs:
            warnings.warn("`**kwargs` parameter is deprecating, use `invocation_state` instead.", stacklevel=2)
            merged_state.update(kwargs)
            if invocation_state is not None:
                merged_state["invocation_state"] = invocation_state
        else:
            if invocation_state is not None:
                merged_state = invocation_state

        callback_handler = self.callback_handler
        if kwargs:
            callback_handler = kwargs.get("callback_handler", self.callback_handler)

        # Process input and get message to add (if any)
        messages = await self._convert_prompt_to_messages(prompt)

        self.trace_span = self._start_agent_trace_span(messages)

        with trace_api.use_span(self.trace_span):
            try:
                events = self._run_loop(messages, merged_state, structured_output_model)

                async for event in events:
                    event.prepare(invocation_state=merged_state)

                    if event.is_callback_event:
                        as_dict = event.as_dict()
                        callback_handler(**as_dict)
                        yield as_dict

                result = AgentResult(*event["stop"])
                callback_handler(result=result)
                yield AgentResultEvent(result=result).as_dict()

                self._end_agent_trace_span(response=result)

            except Exception as e:
                self._end_agent_trace_span(error=e)
                raise

    async def _run_loop(
        self,
        messages: Messages,
        invocation_state: dict[str, Any],
        structured_output_model: Type[BaseModel] | None = None,
    ) -> AsyncGenerator[TypedEvent, None]:
        """Execute the agent's event loop with the given message and parameters.

        Args:
            messages: The input messages to add to the conversation.
            invocation_state: Additional parameters to pass to the event loop.
            structured_output_model: Optional Pydantic model type for structured output.

        Yields:
            Events from the event loop cycle.
        """
        await self.hooks.invoke_callbacks_async(BeforeInvocationEvent(agent=self))

        try:
            yield InitEventLoopEvent()

            for message in messages:
                await self._append_message(message)

            structured_output_context = StructuredOutputContext(
                structured_output_model or self._default_structured_output_model
            )

            # Execute the event loop cycle with retry logic for context limits
            events = self._execute_event_loop_cycle(invocation_state, structured_output_context)
            async for event in events:
                # Signal from the model provider that the message sent by the user should be redacted,
                # likely due to a guardrail.
                if (
                    isinstance(event, ModelStreamChunkEvent)
                    and event.chunk
                    and event.chunk.get("redactContent")
                    and event.chunk["redactContent"].get("redactUserContentMessage")
                ):
                    self.messages[-1]["content"] = self._redact_user_content(
                        self.messages[-1]["content"], str(event.chunk["redactContent"]["redactUserContentMessage"])
                    )
                    if self._session_manager:
                        self._session_manager.redact_latest_message(self.messages[-1], self)
                yield event

        finally:
            self.conversation_manager.apply_management(self)
            await self.hooks.invoke_callbacks_async(AfterInvocationEvent(agent=self))

    async def _execute_event_loop_cycle(
        self, invocation_state: dict[str, Any], structured_output_context: StructuredOutputContext | None = None
    ) -> AsyncGenerator[TypedEvent, None]:
        """Execute the event loop cycle with retry logic for context window limits.

        This internal method handles the execution of the event loop cycle and implements
        retry logic for handling context window overflow exceptions by reducing the
        conversation context and retrying.

        Args:
            invocation_state: Additional parameters to pass to the event loop.
            structured_output_context: Optional structured output context for this invocation.

        Yields:
            Events of the loop cycle.
        """
        # Add `Agent` to invocation_state to keep backwards-compatibility
        invocation_state["agent"] = self

        if structured_output_context:
            structured_output_context.register_tool(self.tool_registry)

        try:
            events = event_loop_cycle(
                agent=self,
                invocation_state=invocation_state,
                structured_output_context=structured_output_context,
            )
            async for event in events:
                yield event

        except ContextWindowOverflowException as e:
            # Try reducing the context size and retrying
            self.conversation_manager.reduce_context(self, e=e)

            # Sync agent after reduce_context to keep conversation_manager_state up to date in the session
            if self._session_manager:
                self._session_manager.sync_agent(self)

            events = self._execute_event_loop_cycle(invocation_state, structured_output_context)
            async for event in events:
                yield event

        finally:
            if structured_output_context:
                structured_output_context.cleanup(self.tool_registry)

    async def _convert_prompt_to_messages(self, prompt: AgentInput) -> Messages:
        if self._interrupt_state.activated:
            return []

        messages: Messages | None = None
        if prompt is not None:
            # Check if the latest message is toolUse
            if len(self.messages) > 0 and any("toolUse" in content for content in self.messages[-1]["content"]):
                # Add toolResult message after to have a valid conversation
                logger.info(
                    "Agents latest message is toolUse, appending a toolResult message to have valid conversation."
                )
                tool_use_ids = [
                    content["toolUse"]["toolUseId"] for content in self.messages[-1]["content"] if "toolUse" in content
                ]
                await self._append_message(
                    {
                        "role": "user",
                        "content": generate_missing_tool_result_content(tool_use_ids),
                    }
                )
            if isinstance(prompt, str):
                # String input - convert to user message
                messages = [{"role": "user", "content": [{"text": prompt}]}]
            elif isinstance(prompt, list):
                if len(prompt) == 0:
                    # Empty list
                    messages = []
                # Check if all item in input list are dictionaries
                elif all(isinstance(item, dict) for item in prompt):
                    # Check if all items are messages
                    if all(all(key in item for key in Message.__annotations__.keys()) for item in prompt):
                        # Messages input - add all messages to conversation
                        messages = cast(Messages, prompt)

                    # Check if all items are content blocks
                    elif all(any(key in ContentBlock.__annotations__.keys() for key in item) for item in prompt):
                        # Treat as List[ContentBlock] input - convert to user message
                        # This allows invalid structures to be passed through to the model
                        messages = [{"role": "user", "content": cast(list[ContentBlock], prompt)}]
        else:
            messages = []
        if messages is None:
            raise ValueError("Input prompt must be of type: `str | list[Contentblock] | Messages | None`.")
        return messages

    def _start_agent_trace_span(self, messages: Messages) -> trace_api.Span:
        """Starts a trace span for the agent.

        Args:
            messages: The input messages.
        """
        model_id = self.model.config.get("model_id") if hasattr(self.model, "config") else None
        return self.tracer.start_agent_span(
            messages=messages,
            agent_name=self.name,
            model_id=model_id,
            tools=self.tool_names,
            system_prompt=self.system_prompt,
            custom_trace_attributes=self.trace_attributes,
            tools_config=self.tool_registry.get_all_tools_config(),
        )

    def _end_agent_trace_span(
        self,
        response: Optional[AgentResult] = None,
        error: Optional[Exception] = None,
    ) -> None:
        """Ends a trace span for the agent.

        Args:
            span: The span to end.
            response: Response to record as a trace attribute.
            error: Error to record as a trace attribute.
        """
        if self.trace_span:
            trace_attributes: dict[str, Any] = {
                "span": self.trace_span,
            }

            if response:
                trace_attributes["response"] = response
            if error:
                trace_attributes["error"] = error

            self.tracer.end_agent_span(**trace_attributes)

    def _initialize_system_prompt(
        self, system_prompt: str | list[SystemContentBlock] | None
    ) -> tuple[str | None, list[SystemContentBlock] | None]:
        """Initialize system prompt fields from constructor input.

        Maintains backwards compatibility by keeping system_prompt as str when string input
        provided, avoiding breaking existing consumers.

        Maps system_prompt input to both string and content block representations:
        - If string: system_prompt=string, _system_prompt_content=[{text: string}]
        - If list with text elements: system_prompt=concatenated_text, _system_prompt_content=list
        - If list without text elements: system_prompt=None, _system_prompt_content=list
        - If None: system_prompt=None, _system_prompt_content=None
        """
        if isinstance(system_prompt, str):
            return system_prompt, [{"text": system_prompt}]
        elif isinstance(system_prompt, list):
            # Concatenate all text elements for backwards compatibility, None if no text found
            text_parts = [block["text"] for block in system_prompt if "text" in block]
            system_prompt_str = "\n".join(text_parts) if text_parts else None
            return system_prompt_str, system_prompt
        else:
            return None, None

    async def _append_message(self, message: Message) -> None:
        """Appends a message to the agent's list of messages and invokes the callbacks for the MessageCreatedEvent."""
        self.messages.append(message)
        await self.hooks.invoke_callbacks_async(MessageAddedEvent(agent=self, message=message))

    def _redact_user_content(self, content: list[ContentBlock], redact_message: str) -> list[ContentBlock]:
        """Redact user content preserving toolResult blocks.

        Args:
            content: content blocks to be redacted
            redact_message: redact message to be replaced

        Returns:
            Redacted content, as follows:
            - if the message contains at least a toolResult block,
                all toolResult blocks(s) are kept, redacting only the result content;
            - otherwise, the entire content of the message is replaced
                with a single text block with the redact message.
        """
        redacted_content = []
        for block in content:
            if "toolResult" in block:
                block["toolResult"]["content"] = [{"text": redact_message}]
                redacted_content.append(block)

        if not redacted_content:
            # Text content is added only if no toolResult blocks were found
            redacted_content = [{"text": redact_message}]

        return redacted_content

`system_prompt` `property` `writable` ¶

Get the system prompt as a string for backwards compatibility.

Returns the system prompt as a concatenated string when it contains text content, or None if no text content is present. This maintains backwards compatibility with existing code that expects system_prompt to be a string.

Returns:

Type	Description
`str \| None`	The system prompt as a string, or None if no text content exists.

`tool` `property` ¶

Call tool as a function.

Returns:

Type	Description
`_ToolCaller`	Tool caller through which user can invoke tool as a function.

Example

agent = Agent(tools=[calculator])
agent.tool.calculator(...)

`tool_names` `property` ¶

Get a list of all registered tool names.

Returns:

Type	Description
`list[str]`	Names of all tools available to this agent.

`call(prompt=None, *, invocation_state=None, structured_output_model=None, **kwargs)` ¶

Process a natural language prompt through the agent's event loop.

This method implements the conversational interface with multiple input patterns: - String input: agent("hello!") - ContentBlock list: agent([{"text": "hello"}, {"image": {...}}]) - Message list: agent([{"role": "user", "content": [{"text": "hello"}]}]) - No input: agent() - uses existing conversation history

Parameters:

Name	Type	Description	Default
`prompt`	`AgentInput`	User input in various formats: - str: Simple text input - list[ContentBlock]: Multi-modal content blocks - list[Message]: Complete messages with roles - None: Use existing conversation history	`None`
`invocation_state`	`dict[str, Any] \| None`	Additional parameters to pass through the event loop.	`None`
`structured_output_model`	`Type[BaseModel] \| None`	Pydantic model type(s) for structured output (overrides agent default).	`None`
`**kwargs`	`Any`	Additional parameters to pass through the event loop.[Deprecating]	`{}`

Returns:

Type	Description
`AgentResult`	Result object containing: stop_reason: Why the event loop stopped (e.g., "end_turn", "max_tokens") message: The final message from the model metrics: Performance metrics from the event loop state: The final state of the event loop structured_output: Parsed structured output when structured_output_model was specified

Source code in strands/agent/agent.py

def __call__(
    self,
    prompt: AgentInput = None,
    *,
    invocation_state: dict[str, Any] | None = None,
    structured_output_model: Type[BaseModel] | None = None,
    **kwargs: Any,
) -> AgentResult:
    """Process a natural language prompt through the agent's event loop.

    This method implements the conversational interface with multiple input patterns:
    - String input: `agent("hello!")`
    - ContentBlock list: `agent([{"text": "hello"}, {"image": {...}}])`
    - Message list: `agent([{"role": "user", "content": [{"text": "hello"}]}])`
    - No input: `agent()` - uses existing conversation history

    Args:
        prompt: User input in various formats:
            - str: Simple text input
            - list[ContentBlock]: Multi-modal content blocks
            - list[Message]: Complete messages with roles
            - None: Use existing conversation history
        invocation_state: Additional parameters to pass through the event loop.
        structured_output_model: Pydantic model type(s) for structured output (overrides agent default).
        **kwargs: Additional parameters to pass through the event loop.[Deprecating]

    Returns:
        Result object containing:

            - stop_reason: Why the event loop stopped (e.g., "end_turn", "max_tokens")
            - message: The final message from the model
            - metrics: Performance metrics from the event loop
            - state: The final state of the event loop
            - structured_output: Parsed structured output when structured_output_model was specified
    """
    return run_async(
        lambda: self.invoke_async(
            prompt, invocation_state=invocation_state, structured_output_model=structured_output_model, **kwargs
        )
    )

`del()` ¶

Clean up resources when agent is garbage collected.

Source code in strands/agent/agent.py

def __del__(self) -> None:
    """Clean up resources when agent is garbage collected."""
    # __del__ is called even when an exception is thrown in the constructor,
    # so there is no guarantee tool_registry was set..
    if hasattr(self, "tool_registry"):
        self.tool_registry.cleanup()

`init(model=None, messages=None, tools=None, system_prompt=None, structured_output_model=None, callback_handler=_DEFAULT_CALLBACK_HANDLER, conversation_manager=None, record_direct_tool_call=True, load_tools_from_directory=False, trace_attributes=None, *, agent_id=None, name=None, description=None, state=None, hooks=None, session_manager=None, tool_executor=None)` ¶

Initialize the Agent with the specified configuration.

Parameters:

Name	Type	Description	Default
`model`	`Union[Model, str, None]`	Provider for running inference or a string representing the model-id for Bedrock to use. Defaults to strands.models.BedrockModel if None.	`None`
`messages`	`Optional[Messages]`	List of initial messages to pre-load into the conversation. Defaults to an empty list if None.	`None`
`tools`	`Optional[list[Union[str, dict[str, str], ToolProvider, Any]]]`	List of tools to make available to the agent. Can be specified as: String tool names (e.g., "retrieve") File paths (e.g., "/path/to/tool.py") Imported Python modules (e.g., from strands_tools import current_time) Dictionaries with name/path keys (e.g., {"name": "tool_name", "path": "/path/to/tool.py"}) ToolProvider instances for managed tool collections Functions decorated with `@strands.tool` decorator. If provided, only these tools will be available. If None, all tools will be available.	`None`
`system_prompt`	`Optional[str \| list[SystemContentBlock]]`	System prompt to guide model behavior. Can be a string or a list of SystemContentBlock objects for advanced features like caching. If None, the model will behave according to its default settings.	`None`
`structured_output_model`	`Optional[Type[BaseModel]]`	Pydantic model type(s) for structured output. When specified, all agent calls will attempt to return structured output of this type. This can be overridden on the agent invocation. Defaults to None (no structured output).	`None`
`callback_handler`	`Optional[Union[Callable[..., Any], _DefaultCallbackHandlerSentinel]]`	Callback for processing events as they happen during agent execution. If not provided (using the default), a new PrintingCallbackHandler instance is created. If explicitly set to None, null_callback_handler is used.	`_DEFAULT_CALLBACK_HANDLER`
`conversation_manager`	`Optional[ConversationManager]`	Manager for conversation history and context window. Defaults to strands.agent.conversation_manager.SlidingWindowConversationManager if None.	`None`
`record_direct_tool_call`	`bool`	Whether to record direct tool calls in message history. Defaults to True.	`True`
`load_tools_from_directory`	`bool`	Whether to load and automatically reload tools in the `./tools/` directory. Defaults to False.	`False`
`trace_attributes`	`Optional[Mapping[str, AttributeValue]]`	Custom trace attributes to apply to the agent's trace span.	`None`
`agent_id`	`Optional[str]`	Optional ID for the agent, useful for session management and multi-agent scenarios. Defaults to "default".	`None`
`name`	`Optional[str]`	name of the Agent Defaults to "Strands Agents".	`None`
`description`	`Optional[str]`	description of what the Agent does Defaults to None.	`None`
`state`	`Optional[Union[AgentState, dict]]`	stateful information for the agent. Can be either an AgentState object, or a json serializable dict. Defaults to an empty AgentState object.	`None`
`hooks`	`Optional[list[HookProvider]]`	hooks to be added to the agent hook registry Defaults to None.	`None`
`session_manager`	`Optional[SessionManager]`	Manager for handling agent sessions including conversation history and state. If provided, enables session-based persistence and state management.	`None`
`tool_executor`	`Optional[ToolExecutor]`	Definition of tool execution strategy (e.g., sequential, concurrent, etc.).	`None`

Raises:

Type	Description
`ValueError`	If agent id contains path separators.

Source code in strands/agent/agent.py

def __init__(
    self,
    model: Union[Model, str, None] = None,
    messages: Optional[Messages] = None,
    tools: Optional[list[Union[str, dict[str, str], "ToolProvider", Any]]] = None,
    system_prompt: Optional[str | list[SystemContentBlock]] = None,
    structured_output_model: Optional[Type[BaseModel]] = None,
    callback_handler: Optional[
        Union[Callable[..., Any], _DefaultCallbackHandlerSentinel]
    ] = _DEFAULT_CALLBACK_HANDLER,
    conversation_manager: Optional[ConversationManager] = None,
    record_direct_tool_call: bool = True,
    load_tools_from_directory: bool = False,
    trace_attributes: Optional[Mapping[str, AttributeValue]] = None,
    *,
    agent_id: Optional[str] = None,
    name: Optional[str] = None,
    description: Optional[str] = None,
    state: Optional[Union[AgentState, dict]] = None,
    hooks: Optional[list[HookProvider]] = None,
    session_manager: Optional[SessionManager] = None,
    tool_executor: Optional[ToolExecutor] = None,
):
    """Initialize the Agent with the specified configuration.

    Args:
        model: Provider for running inference or a string representing the model-id for Bedrock to use.
            Defaults to strands.models.BedrockModel if None.
        messages: List of initial messages to pre-load into the conversation.
            Defaults to an empty list if None.
        tools: List of tools to make available to the agent.
            Can be specified as:

            - String tool names (e.g., "retrieve")
            - File paths (e.g., "/path/to/tool.py")
            - Imported Python modules (e.g., from strands_tools import current_time)
            - Dictionaries with name/path keys (e.g., {"name": "tool_name", "path": "/path/to/tool.py"})
            - ToolProvider instances for managed tool collections
            - Functions decorated with `@strands.tool` decorator.

            If provided, only these tools will be available. If None, all tools will be available.
        system_prompt: System prompt to guide model behavior.
            Can be a string or a list of SystemContentBlock objects for advanced features like caching.
            If None, the model will behave according to its default settings.
        structured_output_model: Pydantic model type(s) for structured output.
            When specified, all agent calls will attempt to return structured output of this type.
            This can be overridden on the agent invocation.
            Defaults to None (no structured output).
        callback_handler: Callback for processing events as they happen during agent execution.
            If not provided (using the default), a new PrintingCallbackHandler instance is created.
            If explicitly set to None, null_callback_handler is used.
        conversation_manager: Manager for conversation history and context window.
            Defaults to strands.agent.conversation_manager.SlidingWindowConversationManager if None.
        record_direct_tool_call: Whether to record direct tool calls in message history.
            Defaults to True.
        load_tools_from_directory: Whether to load and automatically reload tools in the `./tools/` directory.
            Defaults to False.
        trace_attributes: Custom trace attributes to apply to the agent's trace span.
        agent_id: Optional ID for the agent, useful for session management and multi-agent scenarios.
            Defaults to "default".
        name: name of the Agent
            Defaults to "Strands Agents".
        description: description of what the Agent does
            Defaults to None.
        state: stateful information for the agent. Can be either an AgentState object, or a json serializable dict.
            Defaults to an empty AgentState object.
        hooks: hooks to be added to the agent hook registry
            Defaults to None.
        session_manager: Manager for handling agent sessions including conversation history and state.
            If provided, enables session-based persistence and state management.
        tool_executor: Definition of tool execution strategy (e.g., sequential, concurrent, etc.).

    Raises:
        ValueError: If agent id contains path separators.
    """
    self.model = BedrockModel() if not model else BedrockModel(model_id=model) if isinstance(model, str) else model
    self.messages = messages if messages is not None else []
    # initializing self._system_prompt for backwards compatibility
    self._system_prompt, self._system_prompt_content = self._initialize_system_prompt(system_prompt)
    self._default_structured_output_model = structured_output_model
    self.agent_id = _identifier.validate(agent_id or _DEFAULT_AGENT_ID, _identifier.Identifier.AGENT)
    self.name = name or _DEFAULT_AGENT_NAME
    self.description = description

    # If not provided, create a new PrintingCallbackHandler instance
    # If explicitly set to None, use null_callback_handler
    # Otherwise use the passed callback_handler
    self.callback_handler: Union[Callable[..., Any], PrintingCallbackHandler]
    if isinstance(callback_handler, _DefaultCallbackHandlerSentinel):
        self.callback_handler = PrintingCallbackHandler()
    elif callback_handler is None:
        self.callback_handler = null_callback_handler
    else:
        self.callback_handler = callback_handler

    self.conversation_manager = conversation_manager if conversation_manager else SlidingWindowConversationManager()

    # Process trace attributes to ensure they're of compatible types
    self.trace_attributes: dict[str, AttributeValue] = {}
    if trace_attributes:
        for k, v in trace_attributes.items():
            if isinstance(v, (str, int, float, bool)) or (
                isinstance(v, list) and all(isinstance(x, (str, int, float, bool)) for x in v)
            ):
                self.trace_attributes[k] = v

    self.record_direct_tool_call = record_direct_tool_call
    self.load_tools_from_directory = load_tools_from_directory

    self.tool_registry = ToolRegistry()

    # Process tool list if provided
    if tools is not None:
        self.tool_registry.process_tools(tools)

    # Initialize tools and configuration
    self.tool_registry.initialize_tools(self.load_tools_from_directory)
    if load_tools_from_directory:
        self.tool_watcher = ToolWatcher(tool_registry=self.tool_registry)

    self.event_loop_metrics = EventLoopMetrics()

    # Initialize tracer instance (no-op if not configured)
    self.tracer = get_tracer()
    self.trace_span: Optional[trace_api.Span] = None

    # Initialize agent state management
    if state is not None:
        if isinstance(state, dict):
            self.state = AgentState(state)
        elif isinstance(state, AgentState):
            self.state = state
        else:
            raise ValueError("state must be an AgentState object or a dict")
    else:
        self.state = AgentState()

    self.tool_caller = _ToolCaller(self)

    self.hooks = HookRegistry()

    self._interrupt_state = _InterruptState()

    # Initialize session management functionality
    self._session_manager = session_manager
    if self._session_manager:
        self.hooks.add_hook(self._session_manager)

    self.tool_executor = tool_executor or ConcurrentToolExecutor()

    if hooks:
        for hook in hooks:
            self.hooks.add_hook(hook)
    self.hooks.invoke_callbacks(AgentInitializedEvent(agent=self))

`cleanup()` ¶

Clean up resources used by the agent.

This method cleans up all tool providers that require explicit cleanup, such as MCP clients. It should be called when the agent is no longer needed to ensure proper resource cleanup.

Note: This method uses a "belt and braces" approach with automatic cleanup through finalizers as a fallback, but explicit cleanup is recommended.

Source code in strands/agent/agent.py

def cleanup(self) -> None:
    """Clean up resources used by the agent.

    This method cleans up all tool providers that require explicit cleanup,
    such as MCP clients. It should be called when the agent is no longer needed
    to ensure proper resource cleanup.

    Note: This method uses a "belt and braces" approach with automatic cleanup
    through finalizers as a fallback, but explicit cleanup is recommended.
    """
    self.tool_registry.cleanup()

`invoke_async(prompt=None, *, invocation_state=None, structured_output_model=None, **kwargs)` `async` ¶

Process a natural language prompt through the agent's event loop.

This method implements the conversational interface with multiple input patterns: - String input: Simple text input - ContentBlock list: Multi-modal content blocks - Message list: Complete messages with roles - No input: Use existing conversation history

Parameters:

Name	Type	Description	Default
`prompt`	`AgentInput`	User input in various formats: - str: Simple text input - list[ContentBlock]: Multi-modal content blocks - list[Message]: Complete messages with roles - None: Use existing conversation history	`None`
`invocation_state`	`dict[str, Any] \| None`	Additional parameters to pass through the event loop.	`None`
`structured_output_model`	`Type[BaseModel] \| None`	Pydantic model type(s) for structured output (overrides agent default).	`None`
`**kwargs`	`Any`	Additional parameters to pass through the event loop.[Deprecating]	`{}`

Returns:

Name	Type	Description
`Result`	`AgentResult`	object containing: stop_reason: Why the event loop stopped (e.g., "end_turn", "max_tokens") message: The final message from the model metrics: Performance metrics from the event loop state: The final state of the event loop

Source code in strands/agent/agent.py

async def invoke_async(
    self,
    prompt: AgentInput = None,
    *,
    invocation_state: dict[str, Any] | None = None,
    structured_output_model: Type[BaseModel] | None = None,
    **kwargs: Any,
) -> AgentResult:
    """Process a natural language prompt through the agent's event loop.

    This method implements the conversational interface with multiple input patterns:
    - String input: Simple text input
    - ContentBlock list: Multi-modal content blocks
    - Message list: Complete messages with roles
    - No input: Use existing conversation history

    Args:
        prompt: User input in various formats:
            - str: Simple text input
            - list[ContentBlock]: Multi-modal content blocks
            - list[Message]: Complete messages with roles
            - None: Use existing conversation history
        invocation_state: Additional parameters to pass through the event loop.
        structured_output_model: Pydantic model type(s) for structured output (overrides agent default).
        **kwargs: Additional parameters to pass through the event loop.[Deprecating]

    Returns:
        Result: object containing:

            - stop_reason: Why the event loop stopped (e.g., "end_turn", "max_tokens")
            - message: The final message from the model
            - metrics: Performance metrics from the event loop
            - state: The final state of the event loop
    """
    events = self.stream_async(
        prompt, invocation_state=invocation_state, structured_output_model=structured_output_model, **kwargs
    )
    async for event in events:
        _ = event

    return cast(AgentResult, event["result"])

`stream_async(prompt=None, *, invocation_state=None, structured_output_model=None, **kwargs)` `async` ¶

Process a natural language prompt and yield events as an async iterator.

This method provides an asynchronous interface for streaming agent events with multiple input patterns: - String input: Simple text input - ContentBlock list: Multi-modal content blocks - Message list: Complete messages with roles - No input: Use existing conversation history

Parameters:

Name	Type	Description	Default
`prompt`	`AgentInput`	User input in various formats: - str: Simple text input - list[ContentBlock]: Multi-modal content blocks - list[Message]: Complete messages with roles - None: Use existing conversation history	`None`
`invocation_state`	`dict[str, Any] \| None`	Additional parameters to pass through the event loop.	`None`
`structured_output_model`	`Type[BaseModel] \| None`	Pydantic model type(s) for structured output (overrides agent default).	`None`
`**kwargs`	`Any`	Additional parameters to pass to the event loop.[Deprecating]	`{}`

Yields:

Type	Description
`AsyncIterator[Any]`	An async iterator that yields events. Each event is a dictionary containing information about the current state of processing, such as: data: Text content being generated complete: Whether this is the final chunk current_tool_use: Information about tools being executed And other event data provided by the callback handler

Raises:

Type	Description
`Exception`	Any exceptions from the agent invocation will be propagated to the caller.

Example

async for event in agent.stream_async("Analyze this data"):
    if "data" in event:
        yield event["data"]

Source code in strands/agent/agent.py

async def stream_async(
    self,
    prompt: AgentInput = None,
    *,
    invocation_state: dict[str, Any] | None = None,
    structured_output_model: Type[BaseModel] | None = None,
    **kwargs: Any,
) -> AsyncIterator[Any]:
    """Process a natural language prompt and yield events as an async iterator.

    This method provides an asynchronous interface for streaming agent events with multiple input patterns:
    - String input: Simple text input
    - ContentBlock list: Multi-modal content blocks
    - Message list: Complete messages with roles
    - No input: Use existing conversation history

    Args:
        prompt: User input in various formats:
            - str: Simple text input
            - list[ContentBlock]: Multi-modal content blocks
            - list[Message]: Complete messages with roles
            - None: Use existing conversation history
        invocation_state: Additional parameters to pass through the event loop.
        structured_output_model: Pydantic model type(s) for structured output (overrides agent default).
        **kwargs: Additional parameters to pass to the event loop.[Deprecating]

    Yields:
        An async iterator that yields events. Each event is a dictionary containing
           information about the current state of processing, such as:

            - data: Text content being generated
            - complete: Whether this is the final chunk
            - current_tool_use: Information about tools being executed
            - And other event data provided by the callback handler

    Raises:
        Exception: Any exceptions from the agent invocation will be propagated to the caller.

    Example:
        ```python
        async for event in agent.stream_async("Analyze this data"):
            if "data" in event:
                yield event["data"]
        ```
    """
    self._interrupt_state.resume(prompt)

    merged_state = {}
    if kwargs:
        warnings.warn("`**kwargs` parameter is deprecating, use `invocation_state` instead.", stacklevel=2)
        merged_state.update(kwargs)
        if invocation_state is not None:
            merged_state["invocation_state"] = invocation_state
    else:
        if invocation_state is not None:
            merged_state = invocation_state

    callback_handler = self.callback_handler
    if kwargs:
        callback_handler = kwargs.get("callback_handler", self.callback_handler)

    # Process input and get message to add (if any)
    messages = await self._convert_prompt_to_messages(prompt)

    self.trace_span = self._start_agent_trace_span(messages)

    with trace_api.use_span(self.trace_span):
        try:
            events = self._run_loop(messages, merged_state, structured_output_model)

            async for event in events:
                event.prepare(invocation_state=merged_state)

                if event.is_callback_event:
                    as_dict = event.as_dict()
                    callback_handler(**as_dict)
                    yield as_dict

            result = AgentResult(*event["stop"])
            callback_handler(result=result)
            yield AgentResultEvent(result=result).as_dict()

            self._end_agent_trace_span(response=result)

        except Exception as e:
            self._end_agent_trace_span(error=e)
            raise

`structured_output(output_model, prompt=None)` ¶

This method allows you to get structured output from the agent.

If you pass in a prompt, it will be used temporarily without adding it to the conversation history. If you don't pass in a prompt, it will use only the existing conversation history to respond.

For smaller models, you may want to use the optional prompt to add additional instructions to explicitly instruct the model to output the structured data.

Parameters:

Name	Type	Description	Default
`output_model`	`Type[T]`	The output model (a JSON schema written as a Pydantic BaseModel) that the agent will use when responding.	required
`prompt`	`AgentInput`	The prompt to use for the agent in various formats: - str: Simple text input - list[ContentBlock]: Multi-modal content blocks - list[Message]: Complete messages with roles - None: Use existing conversation history	`None`

Raises:

Type	Description
`ValueError`	If no conversation history or prompt is provided.

Source code in strands/agent/agent.py

def structured_output(self, output_model: Type[T], prompt: AgentInput = None) -> T:
    """This method allows you to get structured output from the agent.

    If you pass in a prompt, it will be used temporarily without adding it to the conversation history.
    If you don't pass in a prompt, it will use only the existing conversation history to respond.

    For smaller models, you may want to use the optional prompt to add additional instructions to explicitly
    instruct the model to output the structured data.

    Args:
        output_model: The output model (a JSON schema written as a Pydantic BaseModel)
            that the agent will use when responding.
        prompt: The prompt to use for the agent in various formats:
            - str: Simple text input
            - list[ContentBlock]: Multi-modal content blocks
            - list[Message]: Complete messages with roles
            - None: Use existing conversation history

    Raises:
        ValueError: If no conversation history or prompt is provided.
    """
    warnings.warn(
        "Agent.structured_output method is deprecated."
        " You should pass in `structured_output_model` directly into the agent invocation."
        " see: https://strandsagents.com/latest/documentation/docs/user-guide/concepts/agents/structured-output/",
        category=DeprecationWarning,
        stacklevel=2,
    )

    return run_async(lambda: self.structured_output_async(output_model, prompt))

`structured_output_async(output_model, prompt=None)` `async` ¶

This method allows you to get structured output from the agent.

If you pass in a prompt, it will be used temporarily without adding it to the conversation history. If you don't pass in a prompt, it will use only the existing conversation history to respond.

For smaller models, you may want to use the optional prompt to add additional instructions to explicitly instruct the model to output the structured data.

Parameters:

Name	Type	Description	Default
`output_model`	`Type[T]`	The output model (a JSON schema written as a Pydantic BaseModel) that the agent will use when responding.	required
`prompt`	`AgentInput`	The prompt to use for the agent (will not be added to conversation history).	`None`

Raises:

Type	Description
`ValueError`	If no conversation history or prompt is provided.

-

Source code in strands/agent/agent.py

async def structured_output_async(self, output_model: Type[T], prompt: AgentInput = None) -> T:
    """This method allows you to get structured output from the agent.

    If you pass in a prompt, it will be used temporarily without adding it to the conversation history.
    If you don't pass in a prompt, it will use only the existing conversation history to respond.

    For smaller models, you may want to use the optional prompt to add additional instructions to explicitly
    instruct the model to output the structured data.

    Args:
        output_model: The output model (a JSON schema written as a Pydantic BaseModel)
            that the agent will use when responding.
        prompt: The prompt to use for the agent (will not be added to conversation history).

    Raises:
        ValueError: If no conversation history or prompt is provided.
    -
    """
    if self._interrupt_state.activated:
        raise RuntimeError("cannot call structured output during interrupt")

    warnings.warn(
        "Agent.structured_output_async method is deprecated."
        " You should pass in `structured_output_model` directly into the agent invocation."
        " see: https://strandsagents.com/latest/documentation/docs/user-guide/concepts/agents/structured-output/",
        category=DeprecationWarning,
        stacklevel=2,
    )
    await self.hooks.invoke_callbacks_async(BeforeInvocationEvent(agent=self))
    with self.tracer.tracer.start_as_current_span(
        "execute_structured_output", kind=trace_api.SpanKind.CLIENT
    ) as structured_output_span:
        try:
            if not self.messages and not prompt:
                raise ValueError("No conversation history or prompt provided")

            temp_messages: Messages = self.messages + await self._convert_prompt_to_messages(prompt)

            structured_output_span.set_attributes(
                {
                    "gen_ai.system": "strands-agents",
                    "gen_ai.agent.name": self.name,
                    "gen_ai.agent.id": self.agent_id,
                    "gen_ai.operation.name": "execute_structured_output",
                }
            )
            if self.system_prompt:
                structured_output_span.add_event(
                    "gen_ai.system.message",
                    attributes={"role": "system", "content": serialize([{"text": self.system_prompt}])},
                )
            for message in temp_messages:
                structured_output_span.add_event(
                    f"gen_ai.{message['role']}.message",
                    attributes={"role": message["role"], "content": serialize(message["content"])},
                )
            events = self.model.structured_output(output_model, temp_messages, system_prompt=self.system_prompt)
            async for event in events:
                if isinstance(event, TypedEvent):
                    event.prepare(invocation_state={})
                    if event.is_callback_event:
                        self.callback_handler(**event.as_dict())

            structured_output_span.add_event(
                "gen_ai.choice", attributes={"message": serialize(event["output"].model_dump())}
            )
            return event["output"]

        finally:
            await self.hooks.invoke_callbacks_async(AfterInvocationEvent(agent=self))

`strands.agent.agent_result` ¶

Agent result handling for SDK.

This module defines the AgentResult class which encapsulates the complete response from an agent's processing cycle.

`AgentResult` `dataclass` ¶

Represents the last result of invoking an agent with a prompt.

Attributes:

Name	Type	Description
`stop_reason`	`StopReason`	The reason why the agent's processing stopped.
`message`	`Message`	The last message generated by the agent.
`metrics`	`EventLoopMetrics`	Performance metrics collected during processing.
`state`	`Any`	Additional state information from the event loop.
`interrupts`	`Sequence[Interrupt] \| None`	List of interrupts if raised by user.
`structured_output`	`BaseModel \| None`	Parsed structured output when structured_output_model was specified.

Source code in strands/agent/agent_result.py

@dataclass
class AgentResult:
    """Represents the last result of invoking an agent with a prompt.

    Attributes:
        stop_reason: The reason why the agent's processing stopped.
        message: The last message generated by the agent.
        metrics: Performance metrics collected during processing.
        state: Additional state information from the event loop.
        interrupts: List of interrupts if raised by user.
        structured_output: Parsed structured output when structured_output_model was specified.
    """

    stop_reason: StopReason
    message: Message
    metrics: EventLoopMetrics
    state: Any
    interrupts: Sequence[Interrupt] | None = None
    structured_output: BaseModel | None = None

    def __str__(self) -> str:
        """Get the agent's last message as a string.

        This method extracts and concatenates all text content from the final message, ignoring any non-text content
        like images or structured data.

        Returns:
            The agent's last message as a string.
        """
        content_array = self.message.get("content", [])

        result = ""
        for item in content_array:
            if isinstance(item, dict) and "text" in item:
                result += item.get("text", "") + "\n"
        return result

    @classmethod
    def from_dict(cls, data: dict[str, Any]) -> "AgentResult":
        """Rehydrate an AgentResult from persisted JSON.

        Args:
            data: Dictionary containing the serialized AgentResult data
        Returns:
            AgentResult instance
        Raises:
            TypeError: If the data format is invalid@
        """
        if data.get("type") != "agent_result":
            raise TypeError(f"AgentResult.from_dict: unexpected type {data.get('type')!r}")

        message = cast(Message, data.get("message"))
        stop_reason = cast(StopReason, data.get("stop_reason"))

        return cls(message=message, stop_reason=stop_reason, metrics=EventLoopMetrics(), state={})

    def to_dict(self) -> dict[str, Any]:
        """Convert this AgentResult to JSON-serializable dictionary.

        Returns:
            Dictionary containing serialized AgentResult data
        """
        return {
            "type": "agent_result",
            "message": self.message,
            "stop_reason": self.stop_reason,
        }

`str()` ¶

Get the agent's last message as a string.

This method extracts and concatenates all text content from the final message, ignoring any non-text content like images or structured data.

Returns:

Type	Description
`str`	The agent's last message as a string.

Source code in strands/agent/agent_result.py

def __str__(self) -> str:
    """Get the agent's last message as a string.

    This method extracts and concatenates all text content from the final message, ignoring any non-text content
    like images or structured data.

    Returns:
        The agent's last message as a string.
    """
    content_array = self.message.get("content", [])

    result = ""
    for item in content_array:
        if isinstance(item, dict) and "text" in item:
            result += item.get("text", "") + "\n"
    return result

`from_dict(data)` `classmethod` ¶

Rehydrate an AgentResult from persisted JSON.

Parameters:

Name	Type	Description	Default
`data`	`dict[str, Any]`	Dictionary containing the serialized AgentResult data	required

Source code in strands/agent/agent_result.py

@classmethod
def from_dict(cls, data: dict[str, Any]) -> "AgentResult":
    """Rehydrate an AgentResult from persisted JSON.

    Args:
        data: Dictionary containing the serialized AgentResult data
    Returns:
        AgentResult instance
    Raises:
        TypeError: If the data format is invalid@
    """
    if data.get("type") != "agent_result":
        raise TypeError(f"AgentResult.from_dict: unexpected type {data.get('type')!r}")

    message = cast(Message, data.get("message"))
    stop_reason = cast(StopReason, data.get("stop_reason"))

    return cls(message=message, stop_reason=stop_reason, metrics=EventLoopMetrics(), state={})

`to_dict()` ¶

Convert this AgentResult to JSON-serializable dictionary.

Returns:

Type	Description
`dict[str, Any]`	Dictionary containing serialized AgentResult data

Source code in strands/agent/agent_result.py

def to_dict(self) -> dict[str, Any]:
    """Convert this AgentResult to JSON-serializable dictionary.

    Returns:
        Dictionary containing serialized AgentResult data
    """
    return {
        "type": "agent_result",
        "message": self.message,
        "stop_reason": self.stop_reason,
    }

`strands.agent.conversation_manager` ¶

This package provides classes for managing conversation history during agent execution.

It includes:

ConversationManager: Abstract base class defining the conversation management interface
NullConversationManager: A no-op implementation that does not modify conversation history
SlidingWindowConversationManager: An implementation that maintains a sliding window of messages to control context size while preserving conversation coherence
SummarizingConversationManager: An implementation that summarizes older context instead of simply trimming it

Conversation managers help control memory usage and context length while maintaining relevant conversation state, which is critical for effective agent interactions.

`strands.agent.conversation_manager.conversation_manager` ¶

Abstract interface for conversation history management.

`ConversationManager` ¶

Bases: ABC

Abstract base class for managing conversation history.

This class provides an interface for implementing conversation management strategies to control the size of message arrays/conversation histories, helping to:

Manage memory usage
Control context length
Maintain relevant conversation state

Source code in strands/agent/conversation_manager/conversation_manager.py

class ConversationManager(ABC):
    """Abstract base class for managing conversation history.

    This class provides an interface for implementing conversation management strategies to control the size of message
    arrays/conversation histories, helping to:

    - Manage memory usage
    - Control context length
    - Maintain relevant conversation state
    """

    def __init__(self) -> None:
        """Initialize the ConversationManager.

        Attributes:
          removed_message_count: The messages that have been removed from the agents messages array.
              These represent messages provided by the user or LLM that have been removed, not messages
              included by the conversation manager through something like summarization.
        """
        self.removed_message_count = 0

    def restore_from_session(self, state: dict[str, Any]) -> Optional[list[Message]]:
        """Restore the Conversation Manager's state from a session.

        Args:
            state: Previous state of the conversation manager
        Returns:
            Optional list of messages to prepend to the agents messages. By default returns None.
        """
        if state.get("__name__") != self.__class__.__name__:
            raise ValueError("Invalid conversation manager state.")
        self.removed_message_count = state["removed_message_count"]
        return None

    def get_state(self) -> dict[str, Any]:
        """Get the current state of a Conversation Manager as a Json serializable dictionary."""
        return {
            "__name__": self.__class__.__name__,
            "removed_message_count": self.removed_message_count,
        }

    @abstractmethod
    def apply_management(self, agent: "Agent", **kwargs: Any) -> None:
        """Applies management strategy to the provided agent.

        Processes the conversation history to maintain appropriate size by modifying the messages list in-place.
        Implementations should handle message pruning, summarization, or other size management techniques to keep the
        conversation context within desired bounds.

        Args:
            agent: The agent whose conversation history will be manage.
                This list is modified in-place.
            **kwargs: Additional keyword arguments for future extensibility.
        """
        pass

    @abstractmethod
    def reduce_context(self, agent: "Agent", e: Optional[Exception] = None, **kwargs: Any) -> None:
        """Called when the model's context window is exceeded.

        This method should implement the specific strategy for reducing the window size when a context overflow occurs.
        It is typically called after a ContextWindowOverflowException is caught.

        Implementations might use strategies such as:

        - Removing the N oldest messages
        - Summarizing older context
        - Applying importance-based filtering
        - Maintaining critical conversation markers

        Args:
            agent: The agent whose conversation history will be reduced.
                This list is modified in-place.
            e: The exception that triggered the context reduction, if any.
            **kwargs: Additional keyword arguments for future extensibility.
        """
        pass

`init()` ¶

Initialize the ConversationManager.

Attributes:

Name	Type	Description
`removed_message_count`		The messages that have been removed from the agents messages array. These represent messages provided by the user or LLM that have been removed, not messages included by the conversation manager through something like summarization.

Source code in strands/agent/conversation_manager/conversation_manager.py

def __init__(self) -> None:
    """Initialize the ConversationManager.

    Attributes:
      removed_message_count: The messages that have been removed from the agents messages array.
          These represent messages provided by the user or LLM that have been removed, not messages
          included by the conversation manager through something like summarization.
    """
    self.removed_message_count = 0

`apply_management(agent, **kwargs)` `abstractmethod` ¶

Applies management strategy to the provided agent.

Processes the conversation history to maintain appropriate size by modifying the messages list in-place. Implementations should handle message pruning, summarization, or other size management techniques to keep the conversation context within desired bounds.

Parameters:

Name	Type	Description	Default
`agent`	`Agent`	The agent whose conversation history will be manage. This list is modified in-place.	required
`**kwargs`	`Any`	Additional keyword arguments for future extensibility.	`{}`

Source code in strands/agent/conversation_manager/conversation_manager.py

@abstractmethod
def apply_management(self, agent: "Agent", **kwargs: Any) -> None:
    """Applies management strategy to the provided agent.

    Processes the conversation history to maintain appropriate size by modifying the messages list in-place.
    Implementations should handle message pruning, summarization, or other size management techniques to keep the
    conversation context within desired bounds.

    Args:
        agent: The agent whose conversation history will be manage.
            This list is modified in-place.
        **kwargs: Additional keyword arguments for future extensibility.
    """
    pass

`get_state()` ¶

Get the current state of a Conversation Manager as a Json serializable dictionary.

Source code in strands/agent/conversation_manager/conversation_manager.py

def get_state(self) -> dict[str, Any]:
    """Get the current state of a Conversation Manager as a Json serializable dictionary."""
    return {
        "__name__": self.__class__.__name__,
        "removed_message_count": self.removed_message_count,
    }

`reduce_context(agent, e=None, **kwargs)` `abstractmethod` ¶

Called when the model's context window is exceeded.

This method should implement the specific strategy for reducing the window size when a context overflow occurs. It is typically called after a ContextWindowOverflowException is caught.

Implementations might use strategies such as:

Removing the N oldest messages
Summarizing older context
Applying importance-based filtering
Maintaining critical conversation markers

Parameters:

Name	Type	Description	Default
`agent`	`Agent`	The agent whose conversation history will be reduced. This list is modified in-place.	required
`e`	`Optional[Exception]`	The exception that triggered the context reduction, if any.	`None`
`**kwargs`	`Any`	Additional keyword arguments for future extensibility.	`{}`

Source code in strands/agent/conversation_manager/conversation_manager.py

@abstractmethod
def reduce_context(self, agent: "Agent", e: Optional[Exception] = None, **kwargs: Any) -> None:
    """Called when the model's context window is exceeded.

    This method should implement the specific strategy for reducing the window size when a context overflow occurs.
    It is typically called after a ContextWindowOverflowException is caught.

    Implementations might use strategies such as:

    - Removing the N oldest messages
    - Summarizing older context
    - Applying importance-based filtering
    - Maintaining critical conversation markers

    Args:
        agent: The agent whose conversation history will be reduced.
            This list is modified in-place.
        e: The exception that triggered the context reduction, if any.
        **kwargs: Additional keyword arguments for future extensibility.
    """
    pass

`restore_from_session(state)` ¶

Restore the Conversation Manager's state from a session.

Parameters:

Name	Type	Description	Default
`state`	`dict[str, Any]`	Previous state of the conversation manager	required

Source code in strands/agent/conversation_manager/conversation_manager.py

def restore_from_session(self, state: dict[str, Any]) -> Optional[list[Message]]:
    """Restore the Conversation Manager's state from a session.

    Args:
        state: Previous state of the conversation manager
    Returns:
        Optional list of messages to prepend to the agents messages. By default returns None.
    """
    if state.get("__name__") != self.__class__.__name__:
        raise ValueError("Invalid conversation manager state.")
    self.removed_message_count = state["removed_message_count"]
    return None

`strands.agent.conversation_manager.null_conversation_manager` ¶

Null implementation of conversation management.

`NullConversationManager` ¶

Bases: ConversationManager

A no-op conversation manager that does not modify the conversation history.

Useful for:

Testing scenarios where conversation management should be disabled
Cases where conversation history is managed externally
Situations where the full conversation history should be preserved

Source code in strands/agent/conversation_manager/null_conversation_manager.py

class NullConversationManager(ConversationManager):
    """A no-op conversation manager that does not modify the conversation history.

    Useful for:

    - Testing scenarios where conversation management should be disabled
    - Cases where conversation history is managed externally
    - Situations where the full conversation history should be preserved
    """

    def apply_management(self, agent: "Agent", **kwargs: Any) -> None:
        """Does nothing to the conversation history.

        Args:
            agent: The agent whose conversation history will remain unmodified.
            **kwargs: Additional keyword arguments for future extensibility.
        """
        pass

    def reduce_context(self, agent: "Agent", e: Optional[Exception] = None, **kwargs: Any) -> None:
        """Does not reduce context and raises an exception.

        Args:
            agent: The agent whose conversation history will remain unmodified.
            e: The exception that triggered the context reduction, if any.
            **kwargs: Additional keyword arguments for future extensibility.

        Raises:
            e: If provided.
            ContextWindowOverflowException: If e is None.
        """
        if e:
            raise e
        else:
            raise ContextWindowOverflowException("Context window overflowed!")

`apply_management(agent, **kwargs)` ¶

Does nothing to the conversation history.

Parameters:

Name	Type	Description	Default
`agent`	`Agent`	The agent whose conversation history will remain unmodified.	required
`**kwargs`	`Any`	Additional keyword arguments for future extensibility.	`{}`

Source code in strands/agent/conversation_manager/null_conversation_manager.py

def apply_management(self, agent: "Agent", **kwargs: Any) -> None:
    """Does nothing to the conversation history.

    Args:
        agent: The agent whose conversation history will remain unmodified.
        **kwargs: Additional keyword arguments for future extensibility.
    """
    pass

`reduce_context(agent, e=None, **kwargs)` ¶

Does not reduce context and raises an exception.

Parameters:

Name	Type	Description	Default
`agent`	`Agent`	The agent whose conversation history will remain unmodified.	required
`e`	`Optional[Exception]`	The exception that triggered the context reduction, if any.	`None`
`**kwargs`	`Any`	Additional keyword arguments for future extensibility.	`{}`

Raises:

Type	Description
`e`	If provided.
`ContextWindowOverflowException`	If e is None.

Source code in strands/agent/conversation_manager/null_conversation_manager.py

def reduce_context(self, agent: "Agent", e: Optional[Exception] = None, **kwargs: Any) -> None:
    """Does not reduce context and raises an exception.

    Args:
        agent: The agent whose conversation history will remain unmodified.
        e: The exception that triggered the context reduction, if any.
        **kwargs: Additional keyword arguments for future extensibility.

    Raises:
        e: If provided.
        ContextWindowOverflowException: If e is None.
    """
    if e:
        raise e
    else:
        raise ContextWindowOverflowException("Context window overflowed!")

`strands.agent.conversation_manager.sliding_window_conversation_manager` ¶

Sliding window conversation history management.

`SlidingWindowConversationManager` ¶

Bases: ConversationManager

Implements a sliding window strategy for managing conversation history.

This class handles the logic of maintaining a conversation window that preserves tool usage pairs and avoids invalid window states.

Source code in strands/agent/conversation_manager/sliding_window_conversation_manager.py

class SlidingWindowConversationManager(ConversationManager):
    """Implements a sliding window strategy for managing conversation history.

    This class handles the logic of maintaining a conversation window that preserves tool usage pairs and avoids
    invalid window states.
    """

    def __init__(self, window_size: int = 40, should_truncate_results: bool = True):
        """Initialize the sliding window conversation manager.

        Args:
            window_size: Maximum number of messages to keep in the agent's history.
                Defaults to 40 messages.
            should_truncate_results: Truncate tool results when a message is too large for the model's context window
        """
        super().__init__()
        self.window_size = window_size
        self.should_truncate_results = should_truncate_results

    def apply_management(self, agent: "Agent", **kwargs: Any) -> None:
        """Apply the sliding window to the agent's messages array to maintain a manageable history size.

        This method is called after every event loop cycle to apply a sliding window if the message count
        exceeds the window size.

        Args:
            agent: The agent whose messages will be managed.
                This list is modified in-place.
            **kwargs: Additional keyword arguments for future extensibility.
        """
        messages = agent.messages

        if len(messages) <= self.window_size:
            logger.debug(
                "message_count=<%s>, window_size=<%s> | skipping context reduction", len(messages), self.window_size
            )
            return
        self.reduce_context(agent)

    def reduce_context(self, agent: "Agent", e: Optional[Exception] = None, **kwargs: Any) -> None:
        """Trim the oldest messages to reduce the conversation context size.

        The method handles special cases where trimming the messages leads to:
         - toolResult with no corresponding toolUse
         - toolUse with no corresponding toolResult

        Args:
            agent: The agent whose messages will be reduce.
                This list is modified in-place.
            e: The exception that triggered the context reduction, if any.
            **kwargs: Additional keyword arguments for future extensibility.

        Raises:
            ContextWindowOverflowException: If the context cannot be reduced further.
                Such as when the conversation is already minimal or when tool result messages cannot be properly
                converted.
        """
        messages = agent.messages

        # Try to truncate the tool result first
        last_message_idx_with_tool_results = self._find_last_message_with_tool_results(messages)
        if last_message_idx_with_tool_results is not None and self.should_truncate_results:
            logger.debug(
                "message_index=<%s> | found message with tool results at index", last_message_idx_with_tool_results
            )
            results_truncated = self._truncate_tool_results(messages, last_message_idx_with_tool_results)
            if results_truncated:
                logger.debug("message_index=<%s> | tool results truncated", last_message_idx_with_tool_results)
                return

        # Try to trim index id when tool result cannot be truncated anymore
        # If the number of messages is less than the window_size, then we default to 2, otherwise, trim to window size
        trim_index = 2 if len(messages) <= self.window_size else len(messages) - self.window_size

        # Find the next valid trim_index
        while trim_index < len(messages):
            if (
                # Oldest message cannot be a toolResult because it needs a toolUse preceding it
                any("toolResult" in content for content in messages[trim_index]["content"])
                or (
                    # Oldest message can be a toolUse only if a toolResult immediately follows it.
                    any("toolUse" in content for content in messages[trim_index]["content"])
                    and trim_index + 1 < len(messages)
                    and not any("toolResult" in content for content in messages[trim_index + 1]["content"])
                )
            ):
                trim_index += 1
            else:
                break
        else:
            # If we didn't find a valid trim_index, then we throw
            raise ContextWindowOverflowException("Unable to trim conversation context!") from e

        # trim_index represents the number of messages being removed from the agents messages array
        self.removed_message_count += trim_index

        # Overwrite message history
        messages[:] = messages[trim_index:]

    def _truncate_tool_results(self, messages: Messages, msg_idx: int) -> bool:
        """Truncate tool results in a message to reduce context size.

        When a message contains tool results that are too large for the model's context window, this function
        replaces the content of those tool results with a simple error message.

        Args:
            messages: The conversation message history.
            msg_idx: Index of the message containing tool results to truncate.

        Returns:
            True if any changes were made to the message, False otherwise.
        """
        if msg_idx >= len(messages) or msg_idx < 0:
            return False

        message = messages[msg_idx]
        changes_made = False
        tool_result_too_large_message = "The tool result was too large!"
        for i, content in enumerate(message.get("content", [])):
            if isinstance(content, dict) and "toolResult" in content:
                tool_result_content_text = next(
                    (item["text"] for item in content["toolResult"]["content"] if "text" in item),
                    "",
                )
                # make the overwriting logic togglable
                if (
                    message["content"][i]["toolResult"]["status"] == "error"
                    and tool_result_content_text == tool_result_too_large_message
                ):
                    logger.info("ToolResult has already been updated, skipping overwrite")
                    return False
                # Update status to error with informative message
                message["content"][i]["toolResult"]["status"] = "error"
                message["content"][i]["toolResult"]["content"] = [{"text": tool_result_too_large_message}]
                changes_made = True

        return changes_made

    def _find_last_message_with_tool_results(self, messages: Messages) -> Optional[int]:
        """Find the index of the last message containing tool results.

        This is useful for identifying messages that might need to be truncated to reduce context size.

        Args:
            messages: The conversation message history.

        Returns:
            Index of the last message with tool results, or None if no such message exists.
        """
        # Iterate backwards through all messages (from newest to oldest)
        for idx in range(len(messages) - 1, -1, -1):
            # Check if this message has any content with toolResult
            current_message = messages[idx]
            has_tool_result = False

            for content in current_message.get("content", []):
                if isinstance(content, dict) and "toolResult" in content:
                    has_tool_result = True
                    break

            if has_tool_result:
                return idx

        return None

`init(window_size=40, should_truncate_results=True)` ¶

Initialize the sliding window conversation manager.

Parameters:

Name	Type	Description	Default
`window_size`	`int`	Maximum number of messages to keep in the agent's history. Defaults to 40 messages.	`40`
`should_truncate_results`	`bool`	Truncate tool results when a message is too large for the model's context window	`True`

Source code in strands/agent/conversation_manager/sliding_window_conversation_manager.py

def __init__(self, window_size: int = 40, should_truncate_results: bool = True):
    """Initialize the sliding window conversation manager.

    Args:
        window_size: Maximum number of messages to keep in the agent's history.
            Defaults to 40 messages.
        should_truncate_results: Truncate tool results when a message is too large for the model's context window
    """
    super().__init__()
    self.window_size = window_size
    self.should_truncate_results = should_truncate_results

`apply_management(agent, **kwargs)` ¶

Apply the sliding window to the agent's messages array to maintain a manageable history size.

This method is called after every event loop cycle to apply a sliding window if the message count exceeds the window size.

Parameters:

Name	Type	Description	Default
`agent`	`Agent`	The agent whose messages will be managed. This list is modified in-place.	required
`**kwargs`	`Any`	Additional keyword arguments for future extensibility.	`{}`

Source code in strands/agent/conversation_manager/sliding_window_conversation_manager.py

def apply_management(self, agent: "Agent", **kwargs: Any) -> None:
    """Apply the sliding window to the agent's messages array to maintain a manageable history size.

    This method is called after every event loop cycle to apply a sliding window if the message count
    exceeds the window size.

    Args:
        agent: The agent whose messages will be managed.
            This list is modified in-place.
        **kwargs: Additional keyword arguments for future extensibility.
    """
    messages = agent.messages

    if len(messages) <= self.window_size:
        logger.debug(
            "message_count=<%s>, window_size=<%s> | skipping context reduction", len(messages), self.window_size
        )
        return
    self.reduce_context(agent)

`reduce_context(agent, e=None, **kwargs)` ¶

Trim the oldest messages to reduce the conversation context size.

The method handles special cases where trimming the messages leads to

toolResult with no corresponding toolUse
toolUse with no corresponding toolResult

Parameters:

Name	Type	Description	Default
`agent`	`Agent`	The agent whose messages will be reduce. This list is modified in-place.	required
`e`	`Optional[Exception]`	The exception that triggered the context reduction, if any.	`None`
`**kwargs`	`Any`	Additional keyword arguments for future extensibility.	`{}`

Raises:

Type	Description
`ContextWindowOverflowException`	If the context cannot be reduced further. Such as when the conversation is already minimal or when tool result messages cannot be properly converted.

Source code in strands/agent/conversation_manager/sliding_window_conversation_manager.py

def reduce_context(self, agent: "Agent", e: Optional[Exception] = None, **kwargs: Any) -> None:
    """Trim the oldest messages to reduce the conversation context size.

    The method handles special cases where trimming the messages leads to:
     - toolResult with no corresponding toolUse
     - toolUse with no corresponding toolResult

    Args:
        agent: The agent whose messages will be reduce.
            This list is modified in-place.
        e: The exception that triggered the context reduction, if any.
        **kwargs: Additional keyword arguments for future extensibility.

    Raises:
        ContextWindowOverflowException: If the context cannot be reduced further.
            Such as when the conversation is already minimal or when tool result messages cannot be properly
            converted.
    """
    messages = agent.messages

    # Try to truncate the tool result first
    last_message_idx_with_tool_results = self._find_last_message_with_tool_results(messages)
    if last_message_idx_with_tool_results is not None and self.should_truncate_results:
        logger.debug(
            "message_index=<%s> | found message with tool results at index", last_message_idx_with_tool_results
        )
        results_truncated = self._truncate_tool_results(messages, last_message_idx_with_tool_results)
        if results_truncated:
            logger.debug("message_index=<%s> | tool results truncated", last_message_idx_with_tool_results)
            return

    # Try to trim index id when tool result cannot be truncated anymore
    # If the number of messages is less than the window_size, then we default to 2, otherwise, trim to window size
    trim_index = 2 if len(messages) <= self.window_size else len(messages) - self.window_size

    # Find the next valid trim_index
    while trim_index < len(messages):
        if (
            # Oldest message cannot be a toolResult because it needs a toolUse preceding it
            any("toolResult" in content for content in messages[trim_index]["content"])
            or (
                # Oldest message can be a toolUse only if a toolResult immediately follows it.
                any("toolUse" in content for content in messages[trim_index]["content"])
                and trim_index + 1 < len(messages)
                and not any("toolResult" in content for content in messages[trim_index + 1]["content"])
            )
        ):
            trim_index += 1
        else:
            break
    else:
        # If we didn't find a valid trim_index, then we throw
        raise ContextWindowOverflowException("Unable to trim conversation context!") from e

    # trim_index represents the number of messages being removed from the agents messages array
    self.removed_message_count += trim_index

    # Overwrite message history
    messages[:] = messages[trim_index:]

`strands.agent.conversation_manager.summarizing_conversation_manager` ¶

Summarizing conversation history management with configurable options.

`SummarizingConversationManager` ¶

Bases: ConversationManager

Implements a summarizing window manager.

This manager provides a configurable option to summarize older context instead of simply trimming it, helping preserve important information while staying within context limits.

Source code in strands/agent/conversation_manager/summarizing_conversation_manager.py

class SummarizingConversationManager(ConversationManager):
    """Implements a summarizing window manager.

    This manager provides a configurable option to summarize older context instead of
    simply trimming it, helping preserve important information while staying within
    context limits.
    """

    def __init__(
        self,
        summary_ratio: float = 0.3,
        preserve_recent_messages: int = 10,
        summarization_agent: Optional["Agent"] = None,
        summarization_system_prompt: Optional[str] = None,
    ):
        """Initialize the summarizing conversation manager.

        Args:
            summary_ratio: Ratio of messages to summarize vs keep when context overflow occurs.
                Value between 0.1 and 0.8. Defaults to 0.3 (summarize 30% of oldest messages).
            preserve_recent_messages: Minimum number of recent messages to always keep.
                Defaults to 10 messages.
            summarization_agent: Optional agent to use for summarization instead of the parent agent.
                If provided, this agent can use tools as part of the summarization process.
            summarization_system_prompt: Optional system prompt override for summarization.
                If None, uses the default summarization prompt.
        """
        super().__init__()
        if summarization_agent is not None and summarization_system_prompt is not None:
            raise ValueError(
                "Cannot provide both summarization_agent and summarization_system_prompt. "
                "Agents come with their own system prompt."
            )

        self.summary_ratio = max(0.1, min(0.8, summary_ratio))
        self.preserve_recent_messages = preserve_recent_messages
        self.summarization_agent = summarization_agent
        self.summarization_system_prompt = summarization_system_prompt
        self._summary_message: Optional[Message] = None

    @override
    def restore_from_session(self, state: dict[str, Any]) -> Optional[list[Message]]:
        """Restores the Summarizing Conversation manager from its previous state in a session.

        Args:
            state: The previous state of the Summarizing Conversation Manager.

        Returns:
            Optionally returns the previous conversation summary if it exists.
        """
        super().restore_from_session(state)
        self._summary_message = state.get("summary_message")
        return [self._summary_message] if self._summary_message else None

    def get_state(self) -> dict[str, Any]:
        """Returns a dictionary representation of the state for the Summarizing Conversation Manager."""
        return {"summary_message": self._summary_message, **super().get_state()}

    def apply_management(self, agent: "Agent", **kwargs: Any) -> None:
        """Apply management strategy to conversation history.

        For the summarizing conversation manager, no proactive management is performed.
        Summarization only occurs when there's a context overflow that triggers reduce_context.

        Args:
            agent: The agent whose conversation history will be managed.
                The agent's messages list is modified in-place.
            **kwargs: Additional keyword arguments for future extensibility.
        """
        # No proactive management - summarization only happens on context overflow
        pass

    def reduce_context(self, agent: "Agent", e: Optional[Exception] = None, **kwargs: Any) -> None:
        """Reduce context using summarization.

        Args:
            agent: The agent whose conversation history will be reduced.
                The agent's messages list is modified in-place.
            e: The exception that triggered the context reduction, if any.
            **kwargs: Additional keyword arguments for future extensibility.

        Raises:
            ContextWindowOverflowException: If the context cannot be summarized.
        """
        try:
            # Calculate how many messages to summarize
            messages_to_summarize_count = max(1, int(len(agent.messages) * self.summary_ratio))

            # Ensure we don't summarize recent messages
            messages_to_summarize_count = min(
                messages_to_summarize_count, len(agent.messages) - self.preserve_recent_messages
            )

            if messages_to_summarize_count <= 0:
                raise ContextWindowOverflowException("Cannot summarize: insufficient messages for summarization")

            # Adjust split point to avoid breaking ToolUse/ToolResult pairs
            messages_to_summarize_count = self._adjust_split_point_for_tool_pairs(
                agent.messages, messages_to_summarize_count
            )

            if messages_to_summarize_count <= 0:
                raise ContextWindowOverflowException("Cannot summarize: insufficient messages for summarization")

            # Extract messages to summarize
            messages_to_summarize = agent.messages[:messages_to_summarize_count]
            remaining_messages = agent.messages[messages_to_summarize_count:]

            # Keep track of the number of messages that have been summarized thus far.
            self.removed_message_count += len(messages_to_summarize)
            # If there is a summary message, don't count it in the removed_message_count.
            if self._summary_message:
                self.removed_message_count -= 1

            # Generate summary
            self._summary_message = self._generate_summary(messages_to_summarize, agent)

            # Replace the summarized messages with the summary
            agent.messages[:] = [self._summary_message] + remaining_messages

        except Exception as summarization_error:
            logger.error("Summarization failed: %s", summarization_error)
            raise summarization_error from e

    def _generate_summary(self, messages: List[Message], agent: "Agent") -> Message:
        """Generate a summary of the provided messages.

        Args:
            messages: The messages to summarize.
            agent: The agent instance to use for summarization.

        Returns:
            A message containing the conversation summary.

        Raises:
            Exception: If summary generation fails.
        """
        # Choose which agent to use for summarization
        summarization_agent = self.summarization_agent if self.summarization_agent is not None else agent

        # Save original system prompt, messages, and tool registry to restore later
        original_system_prompt = summarization_agent.system_prompt
        original_messages = summarization_agent.messages.copy()
        original_tool_registry = summarization_agent.tool_registry

        try:
            # Only override system prompt if no agent was provided during initialization
            if self.summarization_agent is None:
                # Use custom system prompt if provided, otherwise use default
                system_prompt = (
                    self.summarization_system_prompt
                    if self.summarization_system_prompt is not None
                    else DEFAULT_SUMMARIZATION_PROMPT
                )
                # Temporarily set the system prompt for summarization
                summarization_agent.system_prompt = system_prompt

            # Add no-op tool if agent has no tools to satisfy tool spec requirement
            if not summarization_agent.tool_names:
                tool_registry = ToolRegistry()
                tool_registry.register_tool(cast(AgentTool, noop_tool))
                summarization_agent.tool_registry = tool_registry

            summarization_agent.messages = messages

            # Use the agent to generate summary with rich content (can use tools if needed)
            result = summarization_agent("Please summarize this conversation.")
            return cast(Message, {**result.message, "role": "user"})

        finally:
            # Restore original agent state
            summarization_agent.system_prompt = original_system_prompt
            summarization_agent.messages = original_messages
            summarization_agent.tool_registry = original_tool_registry

    def _adjust_split_point_for_tool_pairs(self, messages: List[Message], split_point: int) -> int:
        """Adjust the split point to avoid breaking ToolUse/ToolResult pairs.

        Uses the same logic as SlidingWindowConversationManager for consistency.

        Args:
            messages: The full list of messages.
            split_point: The initially calculated split point.

        Returns:
            The adjusted split point that doesn't break ToolUse/ToolResult pairs.

        Raises:
            ContextWindowOverflowException: If no valid split point can be found.
        """
        if split_point > len(messages):
            raise ContextWindowOverflowException("Split point exceeds message array length")

        if split_point == len(messages):
            return split_point

        # Find the next valid split_point
        while split_point < len(messages):
            if (
                # Oldest message cannot be a toolResult because it needs a toolUse preceding it
                any("toolResult" in content for content in messages[split_point]["content"])
                or (
                    # Oldest message can be a toolUse only if a toolResult immediately follows it.
                    any("toolUse" in content for content in messages[split_point]["content"])
                    and split_point + 1 < len(messages)
                    and not any("toolResult" in content for content in messages[split_point + 1]["content"])
                )
            ):
                split_point += 1
            else:
                break
        else:
            # If we didn't find a valid split_point, then we throw
            raise ContextWindowOverflowException("Unable to trim conversation context!")

        return split_point

`init(summary_ratio=0.3, preserve_recent_messages=10, summarization_agent=None, summarization_system_prompt=None)` ¶

Initialize the summarizing conversation manager.

Parameters:

Name	Type	Description	Default
`summary_ratio`	`float`	Ratio of messages to summarize vs keep when context overflow occurs. Value between 0.1 and 0.8. Defaults to 0.3 (summarize 30% of oldest messages).	`0.3`
`preserve_recent_messages`	`int`	Minimum number of recent messages to always keep. Defaults to 10 messages.	`10`
`summarization_agent`	`Optional[Agent]`	Optional agent to use for summarization instead of the parent agent. If provided, this agent can use tools as part of the summarization process.	`None`
`summarization_system_prompt`	`Optional[str]`	Optional system prompt override for summarization. If None, uses the default summarization prompt.	`None`

Source code in strands/agent/conversation_manager/summarizing_conversation_manager.py

def __init__(
    self,
    summary_ratio: float = 0.3,
    preserve_recent_messages: int = 10,
    summarization_agent: Optional["Agent"] = None,
    summarization_system_prompt: Optional[str] = None,
):
    """Initialize the summarizing conversation manager.

    Args:
        summary_ratio: Ratio of messages to summarize vs keep when context overflow occurs.
            Value between 0.1 and 0.8. Defaults to 0.3 (summarize 30% of oldest messages).
        preserve_recent_messages: Minimum number of recent messages to always keep.
            Defaults to 10 messages.
        summarization_agent: Optional agent to use for summarization instead of the parent agent.
            If provided, this agent can use tools as part of the summarization process.
        summarization_system_prompt: Optional system prompt override for summarization.
            If None, uses the default summarization prompt.
    """
    super().__init__()
    if summarization_agent is not None and summarization_system_prompt is not None:
        raise ValueError(
            "Cannot provide both summarization_agent and summarization_system_prompt. "
            "Agents come with their own system prompt."
        )

    self.summary_ratio = max(0.1, min(0.8, summary_ratio))
    self.preserve_recent_messages = preserve_recent_messages
    self.summarization_agent = summarization_agent
    self.summarization_system_prompt = summarization_system_prompt
    self._summary_message: Optional[Message] = None

`apply_management(agent, **kwargs)` ¶

Apply management strategy to conversation history.

For the summarizing conversation manager, no proactive management is performed. Summarization only occurs when there's a context overflow that triggers reduce_context.

Parameters:

Name	Type	Description	Default
`agent`	`Agent`	The agent whose conversation history will be managed. The agent's messages list is modified in-place.	required
`**kwargs`	`Any`	Additional keyword arguments for future extensibility.	`{}`

Source code in strands/agent/conversation_manager/summarizing_conversation_manager.py

def apply_management(self, agent: "Agent", **kwargs: Any) -> None:
    """Apply management strategy to conversation history.

    For the summarizing conversation manager, no proactive management is performed.
    Summarization only occurs when there's a context overflow that triggers reduce_context.

    Args:
        agent: The agent whose conversation history will be managed.
            The agent's messages list is modified in-place.
        **kwargs: Additional keyword arguments for future extensibility.
    """
    # No proactive management - summarization only happens on context overflow
    pass

`get_state()` ¶

Returns a dictionary representation of the state for the Summarizing Conversation Manager.

Source code in strands/agent/conversation_manager/summarizing_conversation_manager.py

def get_state(self) -> dict[str, Any]:
    """Returns a dictionary representation of the state for the Summarizing Conversation Manager."""
    return {"summary_message": self._summary_message, **super().get_state()}

`reduce_context(agent, e=None, **kwargs)` ¶

Reduce context using summarization.

Parameters:

Name	Type	Description	Default
`agent`	`Agent`	The agent whose conversation history will be reduced. The agent's messages list is modified in-place.	required
`e`	`Optional[Exception]`	The exception that triggered the context reduction, if any.	`None`
`**kwargs`	`Any`	Additional keyword arguments for future extensibility.	`{}`

Raises:

Type	Description
`ContextWindowOverflowException`	If the context cannot be summarized.

Source code in strands/agent/conversation_manager/summarizing_conversation_manager.py

def reduce_context(self, agent: "Agent", e: Optional[Exception] = None, **kwargs: Any) -> None:
    """Reduce context using summarization.

    Args:
        agent: The agent whose conversation history will be reduced.
            The agent's messages list is modified in-place.
        e: The exception that triggered the context reduction, if any.
        **kwargs: Additional keyword arguments for future extensibility.

    Raises:
        ContextWindowOverflowException: If the context cannot be summarized.
    """
    try:
        # Calculate how many messages to summarize
        messages_to_summarize_count = max(1, int(len(agent.messages) * self.summary_ratio))

        # Ensure we don't summarize recent messages
        messages_to_summarize_count = min(
            messages_to_summarize_count, len(agent.messages) - self.preserve_recent_messages
        )

        if messages_to_summarize_count <= 0:
            raise ContextWindowOverflowException("Cannot summarize: insufficient messages for summarization")

        # Adjust split point to avoid breaking ToolUse/ToolResult pairs
        messages_to_summarize_count = self._adjust_split_point_for_tool_pairs(
            agent.messages, messages_to_summarize_count
        )

        if messages_to_summarize_count <= 0:
            raise ContextWindowOverflowException("Cannot summarize: insufficient messages for summarization")

        # Extract messages to summarize
        messages_to_summarize = agent.messages[:messages_to_summarize_count]
        remaining_messages = agent.messages[messages_to_summarize_count:]

        # Keep track of the number of messages that have been summarized thus far.
        self.removed_message_count += len(messages_to_summarize)
        # If there is a summary message, don't count it in the removed_message_count.
        if self._summary_message:
            self.removed_message_count -= 1

        # Generate summary
        self._summary_message = self._generate_summary(messages_to_summarize, agent)

        # Replace the summarized messages with the summary
        agent.messages[:] = [self._summary_message] + remaining_messages

    except Exception as summarization_error:
        logger.error("Summarization failed: %s", summarization_error)
        raise summarization_error from e

`restore_from_session(state)` ¶

Restores the Summarizing Conversation manager from its previous state in a session.

Parameters:

Name	Type	Description	Default
`state`	`dict[str, Any]`	The previous state of the Summarizing Conversation Manager.	required

Returns:

Type	Description
`Optional[list[Message]]`	Optionally returns the previous conversation summary if it exists.

Source code in strands/agent/conversation_manager/summarizing_conversation_manager.py

@override
def restore_from_session(self, state: dict[str, Any]) -> Optional[list[Message]]:
    """Restores the Summarizing Conversation manager from its previous state in a session.

    Args:
        state: The previous state of the Summarizing Conversation Manager.

    Returns:
        Optionally returns the previous conversation summary if it exists.
    """
    super().restore_from_session(state)
    self._summary_message = state.get("summary_message")
    return [self._summary_message] if self._summary_message else None

`strands.agent.state` ¶

Agent state management.

`AgentState` ¶

Represents an Agent's stateful information outside of context provided to a model.

Provides a key-value store for agent state with JSON serialization validation and persistence support. Key features: - JSON serialization validation on assignment - Get/set/delete operations

Source code in strands/agent/state.py

class AgentState:
    """Represents an Agent's stateful information outside of context provided to a model.

    Provides a key-value store for agent state with JSON serialization validation and persistence support.
    Key features:
    - JSON serialization validation on assignment
    - Get/set/delete operations
    """

    def __init__(self, initial_state: Optional[Dict[str, Any]] = None):
        """Initialize AgentState."""
        self._state: Dict[str, Dict[str, Any]]
        if initial_state:
            self._validate_json_serializable(initial_state)
            self._state = copy.deepcopy(initial_state)
        else:
            self._state = {}

    def set(self, key: str, value: Any) -> None:
        """Set a value in the state.

        Args:
            key: The key to store the value under
            value: The value to store (must be JSON serializable)

        Raises:
            ValueError: If key is invalid, or if value is not JSON serializable
        """
        self._validate_key(key)
        self._validate_json_serializable(value)

        self._state[key] = copy.deepcopy(value)

    def get(self, key: Optional[str] = None) -> Any:
        """Get a value or entire state.

        Args:
            key: The key to retrieve (if None, returns entire state object)

        Returns:
            The stored value, entire state dict, or None if not found
        """
        if key is None:
            return copy.deepcopy(self._state)
        else:
            # Return specific key
            return copy.deepcopy(self._state.get(key))

    def delete(self, key: str) -> None:
        """Delete a specific key from the state.

        Args:
            key: The key to delete
        """
        self._validate_key(key)

        self._state.pop(key, None)

    def _validate_key(self, key: str) -> None:
        """Validate that a key is valid.

        Args:
            key: The key to validate

        Raises:
            ValueError: If key is invalid
        """
        if key is None:
            raise ValueError("Key cannot be None")
        if not isinstance(key, str):
            raise ValueError("Key must be a string")
        if not key.strip():
            raise ValueError("Key cannot be empty")

    def _validate_json_serializable(self, value: Any) -> None:
        """Validate that a value is JSON serializable.

        Args:
            value: The value to validate

        Raises:
            ValueError: If value is not JSON serializable
        """
        try:
            json.dumps(value)
        except (TypeError, ValueError) as e:
            raise ValueError(
                f"Value is not JSON serializable: {type(value).__name__}. "
                f"Only JSON-compatible types (str, int, float, bool, list, dict, None) are allowed."
            ) from e

`init(initial_state=None)` ¶

Initialize AgentState.

Source code in strands/agent/state.py

def __init__(self, initial_state: Optional[Dict[str, Any]] = None):
    """Initialize AgentState."""
    self._state: Dict[str, Dict[str, Any]]
    if initial_state:
        self._validate_json_serializable(initial_state)
        self._state = copy.deepcopy(initial_state)
    else:
        self._state = {}

`delete(key)` ¶

Delete a specific key from the state.

Parameters:

Name	Type	Description	Default
`key`	`str`	The key to delete	required

Source code in strands/agent/state.py

def delete(self, key: str) -> None:
    """Delete a specific key from the state.

    Args:
        key: The key to delete
    """
    self._validate_key(key)

    self._state.pop(key, None)

`get(key=None)` ¶

Get a value or entire state.

Parameters:

Name	Type	Description	Default
`key`	`Optional[str]`	The key to retrieve (if None, returns entire state object)	`None`

Returns:

Type	Description
`Any`	The stored value, entire state dict, or None if not found

Source code in strands/agent/state.py

def get(self, key: Optional[str] = None) -> Any:
    """Get a value or entire state.

    Args:
        key: The key to retrieve (if None, returns entire state object)

    Returns:
        The stored value, entire state dict, or None if not found
    """
    if key is None:
        return copy.deepcopy(self._state)
    else:
        # Return specific key
        return copy.deepcopy(self._state.get(key))

`set(key, value)` ¶

Set a value in the state.

Parameters:

Name	Type	Description	Default
`key`	`str`	The key to store the value under	required
`value`	`Any`	The value to store (must be JSON serializable)	required

Raises:

Type	Description
`ValueError`	If key is invalid, or if value is not JSON serializable

Source code in strands/agent/state.py

def set(self, key: str, value: Any) -> None:
    """Set a value in the state.

    Args:
        key: The key to store the value under
        value: The value to store (must be JSON serializable)

    Raises:
        ValueError: If key is invalid, or if value is not JSON serializable
    """
    self._validate_key(key)
    self._validate_json_serializable(value)

    self._state[key] = copy.deepcopy(value)

strands.agent ¶

strands.agent.agent ¶

Agent ¶

system_prompt property writable ¶

tool property ¶

tool_names property ¶

__call__(prompt=None, *, invocation_state=None, structured_output_model=None, **kwargs) ¶

__del__() ¶

cleanup() ¶

invoke_async(prompt=None, *, invocation_state=None, structured_output_model=None, **kwargs) async ¶

stream_async(prompt=None, *, invocation_state=None, structured_output_model=None, **kwargs) async ¶

structured_output(output_model, prompt=None) ¶

structured_output_async(output_model, prompt=None) async ¶

strands.agent.agent_result ¶

AgentResult dataclass ¶

__str__() ¶

from_dict(data) classmethod ¶

to_dict() ¶

strands.agent.conversation_manager ¶

strands.agent.conversation_manager.conversation_manager ¶

ConversationManager ¶

__init__() ¶

apply_management(agent, **kwargs) abstractmethod ¶

get_state() ¶

reduce_context(agent, e=None, **kwargs) abstractmethod ¶

restore_from_session(state) ¶

strands.agent.conversation_manager.null_conversation_manager ¶

NullConversationManager ¶

apply_management(agent, **kwargs) ¶

reduce_context(agent, e=None, **kwargs) ¶

strands.agent.conversation_manager.sliding_window_conversation_manager ¶

SlidingWindowConversationManager ¶

__init__(window_size=40, should_truncate_results=True) ¶

apply_management(agent, **kwargs) ¶

reduce_context(agent, e=None, **kwargs) ¶

strands.agent.conversation_manager.summarizing_conversation_manager ¶

SummarizingConversationManager ¶

__init__(summary_ratio=0.3, preserve_recent_messages=10, summarization_agent=None, summarization_system_prompt=None) ¶

apply_management(agent, **kwargs) ¶

get_state() ¶

reduce_context(agent, e=None, **kwargs) ¶

restore_from_session(state) ¶

strands.agent.state ¶

AgentState ¶

__init__(initial_state=None) ¶

delete(key) ¶

get(key=None) ¶

set(key, value) ¶

`strands.agent` ¶

`strands.agent.agent` ¶

`Agent` ¶

`system_prompt` `property` `writable` ¶

`tool` `property` ¶

`tool_names` `property` ¶

`call(prompt=None, *, invocation_state=None, structured_output_model=None, **kwargs)` ¶

`del()` ¶

`cleanup()` ¶

`invoke_async(prompt=None, *, invocation_state=None, structured_output_model=None, **kwargs)` `async` ¶

`stream_async(prompt=None, *, invocation_state=None, structured_output_model=None, **kwargs)` `async` ¶

`structured_output(output_model, prompt=None)` ¶

`structured_output_async(output_model, prompt=None)` `async` ¶

`strands.agent.agent_result` ¶

`AgentResult` `dataclass` ¶

`str()` ¶

`from_dict(data)` `classmethod` ¶

`to_dict()` ¶

`strands.agent.conversation_manager` ¶

`strands.agent.conversation_manager.conversation_manager` ¶

`ConversationManager` ¶

`init()` ¶

`apply_management(agent, **kwargs)` `abstractmethod` ¶

`get_state()` ¶

`reduce_context(agent, e=None, **kwargs)` `abstractmethod` ¶

`restore_from_session(state)` ¶

`strands.agent.conversation_manager.null_conversation_manager` ¶

`NullConversationManager` ¶

`apply_management(agent, **kwargs)` ¶

`reduce_context(agent, e=None, **kwargs)` ¶

`strands.agent.conversation_manager.sliding_window_conversation_manager` ¶

`SlidingWindowConversationManager` ¶

`init(window_size=40, should_truncate_results=True)` ¶

`apply_management(agent, **kwargs)` ¶

`reduce_context(agent, e=None, **kwargs)` ¶

`strands.agent.conversation_manager.summarizing_conversation_manager` ¶

`SummarizingConversationManager` ¶

`init(summary_ratio=0.3, preserve_recent_messages=10, summarization_agent=None, summarization_system_prompt=None)` ¶

`apply_management(agent, **kwargs)` ¶

`get_state()` ¶

`reduce_context(agent, e=None, **kwargs)` ¶

`restore_from_session(state)` ¶

`strands.agent.state` ¶

`AgentState` ¶

`init(initial_state=None)` ¶

`delete(key)` ¶

`get(key=None)` ¶

`set(key, value)` ¶