strands.hooks.registry

Hook registry system for managing event callbacks in the Strands Agent SDK.

This module provides the core infrastructure for the typed hook system, enabling composable extension of agent functionality through strongly-typed event callbacks. The registry manages the mapping between event types and their associated callback functions, supporting both individual callback registration and bulk registration via hook provider objects.

TEvent = TypeVar('TEvent', bound=BaseHookEvent, contravariant=True) module-attribute

Generic for adding callback handlers - contravariant to allow adding handlers which take in base classes.

TInvokeEvent = TypeVar('TInvokeEvent', bound=BaseHookEvent) module-attribute

Generic for invoking events - non-contravariant to enable returning events.

logger = logging.getLogger(__name__) module-attribute

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

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)

        # Allow conversation_managers to subscribe to hooks
        self.hooks.add_hook(self.conversation_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)

        self.event_loop_metrics.reset_usage_metrics()

        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))

        agent_result: AgentResult | None = None
        try:
            yield InitEventLoopEvent()

            await self._append_messages(*messages)

            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

            # Capture the result from the final event if available
            if isinstance(event, EventLoopStopEvent):
                agent_result = AgentResult(*event["stop"])

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

    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_messages(
                    {
                        "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_messages(self, *messages: Message) -> None:
        """Appends messages to history and invoke the callbacks for the MessageAddedEvent."""
        for message in messages:
            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)

    # Allow conversation_managers to subscribe to hooks
    self.hooks.add_hook(self.conversation_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)

    self.event_loop_metrics.reset_usage_metrics()

    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))

BaseHookEvent dataclass

Base class for all hook events.

Source code in strands/hooks/registry.py

@dataclass
class BaseHookEvent:
    """Base class for all hook events."""

    @property
    def should_reverse_callbacks(self) -> bool:
        """Determine if callbacks for this event should be invoked in reverse order.

        Returns:
            False by default. Override to return True for events that should
            invoke callbacks in reverse order (e.g., cleanup/teardown events).
        """
        return False

    def _can_write(self, name: str) -> bool:
        """Check if the given property can be written to.

        Args:
            name: The name of the property to check.

        Returns:
            True if the property can be written to, False otherwise.
        """
        return False

    def __post_init__(self) -> None:
        """Disallow writes to non-approved properties."""
        # This is needed as otherwise the class can't be initialized at all, so we trigger
        # this after class initialization
        super().__setattr__("_disallow_writes", True)

    def __setattr__(self, name: str, value: Any) -> None:
        """Prevent setting attributes on hook events.

        Raises:
            AttributeError: Always raised to prevent setting attributes on hook events.
        """
        #  Allow setting attributes:
        #    - during init (when __dict__) doesn't exist
        #    - if the subclass specifically said the property is writable
        if not hasattr(self, "_disallow_writes") or self._can_write(name):
            return super().__setattr__(name, value)

        raise AttributeError(f"Property {name} is not writable")

should_reverse_callbacks property

Determine if callbacks for this event should be invoked in reverse order.

Returns:

Type	Description
bool	False by default. Override to return True for events that should
bool	invoke callbacks in reverse order (e.g., cleanup/teardown events).

__post_init__()

Disallow writes to non-approved properties.

Source code in strands/hooks/registry.py

def __post_init__(self) -> None:
    """Disallow writes to non-approved properties."""
    # This is needed as otherwise the class can't be initialized at all, so we trigger
    # this after class initialization
    super().__setattr__("_disallow_writes", True)

__setattr__(name, value)

Prevent setting attributes on hook events.

Raises:

Type	Description
AttributeError	Always raised to prevent setting attributes on hook events.

Source code in strands/hooks/registry.py

def __setattr__(self, name: str, value: Any) -> None:
    """Prevent setting attributes on hook events.

    Raises:
        AttributeError: Always raised to prevent setting attributes on hook events.
    """
    #  Allow setting attributes:
    #    - during init (when __dict__) doesn't exist
    #    - if the subclass specifically said the property is writable
    if not hasattr(self, "_disallow_writes") or self._can_write(name):
        return super().__setattr__(name, value)

    raise AttributeError(f"Property {name} is not writable")

HookCallback

Bases: Protocol, Generic[TEvent]

Protocol for callback functions that handle hook events.

Hook callbacks are functions that receive a single strongly-typed event argument and perform some action in response. They should not return values and any exceptions they raise will propagate to the caller.

Example

def my_callback(event: StartRequestEvent) -> None:
    print(f"Request started for agent: {event.agent.name}")

# Or

async def my_callback(event: StartRequestEvent) -> None:
    # await an async operation

Source code in strands/hooks/registry.py

class HookCallback(Protocol, Generic[TEvent]):
    """Protocol for callback functions that handle hook events.

    Hook callbacks are functions that receive a single strongly-typed event
    argument and perform some action in response. They should not return
    values and any exceptions they raise will propagate to the caller.

    Example:
        ```python
        def my_callback(event: StartRequestEvent) -> None:
            print(f"Request started for agent: {event.agent.name}")

        # Or

        async def my_callback(event: StartRequestEvent) -> None:
            # await an async operation
        ```
    """

    def __call__(self, event: TEvent) -> None | Awaitable[None]:
        """Handle a hook event.

        Args:
            event: The strongly-typed event to handle.
        """
        ...

__call__(event)

Handle a hook event.

Parameters:

Name	Type	Description	Default
event	TEvent	The strongly-typed event to handle.	required

Source code in strands/hooks/registry.py

def __call__(self, event: TEvent) -> None | Awaitable[None]:
    """Handle a hook event.

    Args:
        event: The strongly-typed event to handle.
    """
    ...

HookEvent dataclass

Bases: BaseHookEvent

Base class for single agent hook events.

Attributes:

Name	Type	Description
agent	Agent	The agent instance that triggered this event.

Source code in strands/hooks/registry.py

@dataclass
class HookEvent(BaseHookEvent):
    """Base class for single agent hook events.

    Attributes:
        agent: The agent instance that triggered this event.
    """

    agent: "Agent"

HookProvider

Bases: Protocol

Protocol for objects that provide hook callbacks to an agent.

Hook providers offer a composable way to extend agent functionality by subscribing to various events in the agent lifecycle. This protocol enables building reusable components that can hook into agent events.

Example

class MyHookProvider(HookProvider):
    def register_hooks(self, registry: HookRegistry) -> None:
        registry.add_callback(StartRequestEvent, self.on_request_start)
        registry.add_callback(EndRequestEvent, self.on_request_end)

agent = Agent(hooks=[MyHookProvider()])

Source code in strands/hooks/registry.py

@runtime_checkable
class HookProvider(Protocol):
    """Protocol for objects that provide hook callbacks to an agent.

    Hook providers offer a composable way to extend agent functionality by
    subscribing to various events in the agent lifecycle. This protocol enables
    building reusable components that can hook into agent events.

    Example:
        ```python
        class MyHookProvider(HookProvider):
            def register_hooks(self, registry: HookRegistry) -> None:
                registry.add_callback(StartRequestEvent, self.on_request_start)
                registry.add_callback(EndRequestEvent, self.on_request_end)

        agent = Agent(hooks=[MyHookProvider()])
        ```
    """

    def register_hooks(self, registry: "HookRegistry", **kwargs: Any) -> None:
        """Register callback functions for specific event types.

        Args:
            registry: The hook registry to register callbacks with.
            **kwargs: Additional keyword arguments for future extensibility.
        """
        ...

register_hooks(registry, **kwargs)

Register callback functions for specific event types.

Parameters:

Name	Type	Description	Default
registry	HookRegistry	The hook registry to register callbacks with.	required
**kwargs	Any	Additional keyword arguments for future extensibility.	{}

Source code in strands/hooks/registry.py

def register_hooks(self, registry: "HookRegistry", **kwargs: Any) -> None:
    """Register callback functions for specific event types.

    Args:
        registry: The hook registry to register callbacks with.
        **kwargs: Additional keyword arguments for future extensibility.
    """
    ...

HookRegistry

Registry for managing hook callbacks associated with event types.

The HookRegistry maintains a mapping of event types to callback functions and provides methods for registering callbacks and invoking them when events occur.

The registry handles callback ordering, including reverse ordering for cleanup events, and provides type-safe event dispatching.

Source code in strands/hooks/registry.py

class HookRegistry:
    """Registry for managing hook callbacks associated with event types.

    The HookRegistry maintains a mapping of event types to callback functions
    and provides methods for registering callbacks and invoking them when
    events occur.

    The registry handles callback ordering, including reverse ordering for
    cleanup events, and provides type-safe event dispatching.
    """

    def __init__(self) -> None:
        """Initialize an empty hook registry."""
        self._registered_callbacks: dict[Type, list[HookCallback]] = {}

    def add_callback(self, event_type: Type[TEvent], callback: HookCallback[TEvent]) -> None:
        """Register a callback function for a specific event type.

        Args:
            event_type: The class type of events this callback should handle.
            callback: The callback function to invoke when events of this type occur.

        Example:
            ```python
            def my_handler(event: StartRequestEvent):
                print("Request started")

            registry.add_callback(StartRequestEvent, my_handler)
            ```
        """
        # Related issue: https://github.com/strands-agents/sdk-python/issues/330
        if event_type.__name__ == "AgentInitializedEvent" and inspect.iscoroutinefunction(callback):
            raise ValueError("AgentInitializedEvent can only be registered with a synchronous callback")

        callbacks = self._registered_callbacks.setdefault(event_type, [])
        callbacks.append(callback)

    def add_hook(self, hook: HookProvider) -> None:
        """Register all callbacks from a hook provider.

        This method allows bulk registration of callbacks by delegating to
        the hook provider's register_hooks method. This is the preferred
        way to register multiple related callbacks.

        Args:
            hook: The hook provider containing callbacks to register.

        Example:
            ```python
            class MyHooks(HookProvider):
                def register_hooks(self, registry: HookRegistry):
                    registry.add_callback(StartRequestEvent, self.on_start)
                    registry.add_callback(EndRequestEvent, self.on_end)

            registry.add_hook(MyHooks())
            ```
        """
        hook.register_hooks(self)

    async def invoke_callbacks_async(self, event: TInvokeEvent) -> tuple[TInvokeEvent, list[Interrupt]]:
        """Invoke all registered callbacks for the given event.

        This method finds all callbacks registered for the event's type and
        invokes them in the appropriate order. For events with should_reverse_callbacks=True,
        callbacks are invoked in reverse registration order. Any exceptions raised by callback
        functions will propagate to the caller.

        Additionally, this method aggregates interrupts raised by the user to instantiate human-in-the-loop workflows.

        Args:
            event: The event to dispatch to registered callbacks.

        Returns:
            The event dispatched to registered callbacks and any interrupts raised by the user.

        Raises:
            ValueError: If interrupt name is used more than once.

        Example:
            ```python
            event = StartRequestEvent(agent=my_agent)
            await registry.invoke_callbacks_async(event)
            ```
        """
        interrupts: dict[str, Interrupt] = {}

        for callback in self.get_callbacks_for(event):
            try:
                if inspect.iscoroutinefunction(callback):
                    await callback(event)
                else:
                    callback(event)

            except InterruptException as exception:
                interrupt = exception.interrupt
                if interrupt.name in interrupts:
                    message = f"interrupt_name=<{interrupt.name}> | interrupt name used more than once"
                    logger.error(message)
                    raise ValueError(message) from exception

                # Each callback is allowed to raise their own interrupt.
                interrupts[interrupt.name] = interrupt

        return event, list(interrupts.values())

    def invoke_callbacks(self, event: TInvokeEvent) -> tuple[TInvokeEvent, list[Interrupt]]:
        """Invoke all registered callbacks for the given event.

        This method finds all callbacks registered for the event's type and
        invokes them in the appropriate order. For events with should_reverse_callbacks=True,
        callbacks are invoked in reverse registration order. Any exceptions raised by callback
        functions will propagate to the caller.

        Additionally, this method aggregates interrupts raised by the user to instantiate human-in-the-loop workflows.

        Args:
            event: The event to dispatch to registered callbacks.

        Returns:
            The event dispatched to registered callbacks and any interrupts raised by the user.

        Raises:
            RuntimeError: If at least one callback is async.
            ValueError: If interrupt name is used more than once.

        Example:
            ```python
            event = StartRequestEvent(agent=my_agent)
            registry.invoke_callbacks(event)
            ```
        """
        callbacks = list(self.get_callbacks_for(event))
        interrupts: dict[str, Interrupt] = {}

        if any(inspect.iscoroutinefunction(callback) for callback in callbacks):
            raise RuntimeError(f"event=<{event}> | use invoke_callbacks_async to invoke async callback")

        for callback in callbacks:
            try:
                callback(event)
            except InterruptException as exception:
                interrupt = exception.interrupt
                if interrupt.name in interrupts:
                    message = f"interrupt_name=<{interrupt.name}> | interrupt name used more than once"
                    logger.error(message)
                    raise ValueError(message) from exception

                # Each callback is allowed to raise their own interrupt.
                interrupts[interrupt.name] = interrupt

        return event, list(interrupts.values())

    def has_callbacks(self) -> bool:
        """Check if the registry has any registered callbacks.

        Returns:
            True if there are any registered callbacks, False otherwise.

        Example:
            ```python
            if registry.has_callbacks():
                print("Registry has callbacks registered")
            ```
        """
        return bool(self._registered_callbacks)

    def get_callbacks_for(self, event: TEvent) -> Generator[HookCallback[TEvent], None, None]:
        """Get callbacks registered for the given event in the appropriate order.

        This method returns callbacks in registration order for normal events,
        or reverse registration order for events that have should_reverse_callbacks=True.
        This enables proper cleanup ordering for teardown events.

        Args:
            event: The event to get callbacks for.

        Yields:
            Callback functions registered for this event type, in the appropriate order.

        Example:
            ```python
            event = EndRequestEvent(agent=my_agent)
            for callback in registry.get_callbacks_for(event):
                callback(event)
            ```
        """
        event_type = type(event)

        callbacks = self._registered_callbacks.get(event_type, [])
        if event.should_reverse_callbacks:
            yield from reversed(callbacks)
        else:
            yield from callbacks

__init__()

Initialize an empty hook registry.

Source code in strands/hooks/registry.py

def __init__(self) -> None:
    """Initialize an empty hook registry."""
    self._registered_callbacks: dict[Type, list[HookCallback]] = {}

add_callback(event_type, callback)

Register a callback function for a specific event type.

Parameters:

Name	Type	Description	Default
event_type	Type[TEvent]	The class type of events this callback should handle.	required
callback	HookCallback[TEvent]	The callback function to invoke when events of this type occur.	required

Example

def my_handler(event: StartRequestEvent):
    print("Request started")

registry.add_callback(StartRequestEvent, my_handler)

Source code in strands/hooks/registry.py

def add_callback(self, event_type: Type[TEvent], callback: HookCallback[TEvent]) -> None:
    """Register a callback function for a specific event type.

    Args:
        event_type: The class type of events this callback should handle.
        callback: The callback function to invoke when events of this type occur.

    Example:
        ```python
        def my_handler(event: StartRequestEvent):
            print("Request started")

        registry.add_callback(StartRequestEvent, my_handler)
        ```
    """
    # Related issue: https://github.com/strands-agents/sdk-python/issues/330
    if event_type.__name__ == "AgentInitializedEvent" and inspect.iscoroutinefunction(callback):
        raise ValueError("AgentInitializedEvent can only be registered with a synchronous callback")

    callbacks = self._registered_callbacks.setdefault(event_type, [])
    callbacks.append(callback)

add_hook(hook)

Register all callbacks from a hook provider.

This method allows bulk registration of callbacks by delegating to the hook provider's register_hooks method. This is the preferred way to register multiple related callbacks.

Parameters:

Name	Type	Description	Default
hook	HookProvider	The hook provider containing callbacks to register.	required

Example

class MyHooks(HookProvider):
    def register_hooks(self, registry: HookRegistry):
        registry.add_callback(StartRequestEvent, self.on_start)
        registry.add_callback(EndRequestEvent, self.on_end)

registry.add_hook(MyHooks())

Source code in strands/hooks/registry.py

def add_hook(self, hook: HookProvider) -> None:
    """Register all callbacks from a hook provider.

    This method allows bulk registration of callbacks by delegating to
    the hook provider's register_hooks method. This is the preferred
    way to register multiple related callbacks.

    Args:
        hook: The hook provider containing callbacks to register.

    Example:
        ```python
        class MyHooks(HookProvider):
            def register_hooks(self, registry: HookRegistry):
                registry.add_callback(StartRequestEvent, self.on_start)
                registry.add_callback(EndRequestEvent, self.on_end)

        registry.add_hook(MyHooks())
        ```
    """
    hook.register_hooks(self)

get_callbacks_for(event)

Get callbacks registered for the given event in the appropriate order.

This method returns callbacks in registration order for normal events, or reverse registration order for events that have should_reverse_callbacks=True. This enables proper cleanup ordering for teardown events.

Parameters:

Name	Type	Description	Default
event	TEvent	The event to get callbacks for.	required

Yields:

Type	Description
HookCallback[TEvent]	Callback functions registered for this event type, in the appropriate order.

Example

event = EndRequestEvent(agent=my_agent)
for callback in registry.get_callbacks_for(event):
    callback(event)

Source code in strands/hooks/registry.py

def get_callbacks_for(self, event: TEvent) -> Generator[HookCallback[TEvent], None, None]:
    """Get callbacks registered for the given event in the appropriate order.

    This method returns callbacks in registration order for normal events,
    or reverse registration order for events that have should_reverse_callbacks=True.
    This enables proper cleanup ordering for teardown events.

    Args:
        event: The event to get callbacks for.

    Yields:
        Callback functions registered for this event type, in the appropriate order.

    Example:
        ```python
        event = EndRequestEvent(agent=my_agent)
        for callback in registry.get_callbacks_for(event):
            callback(event)
        ```
    """
    event_type = type(event)

    callbacks = self._registered_callbacks.get(event_type, [])
    if event.should_reverse_callbacks:
        yield from reversed(callbacks)
    else:
        yield from callbacks

has_callbacks()

Check if the registry has any registered callbacks.

Returns:

Type	Description
bool	True if there are any registered callbacks, False otherwise.

Example

if registry.has_callbacks():
    print("Registry has callbacks registered")

Source code in strands/hooks/registry.py

def has_callbacks(self) -> bool:
    """Check if the registry has any registered callbacks.

    Returns:
        True if there are any registered callbacks, False otherwise.

    Example:
        ```python
        if registry.has_callbacks():
            print("Registry has callbacks registered")
        ```
    """
    return bool(self._registered_callbacks)

invoke_callbacks(event)

Invoke all registered callbacks for the given event.

This method finds all callbacks registered for the event's type and invokes them in the appropriate order. For events with should_reverse_callbacks=True, callbacks are invoked in reverse registration order. Any exceptions raised by callback functions will propagate to the caller.

Additionally, this method aggregates interrupts raised by the user to instantiate human-in-the-loop workflows.

Parameters:

Name	Type	Description	Default
event	TInvokeEvent	The event to dispatch to registered callbacks.	required

Returns:

Type	Description
tuple[TInvokeEvent, list[Interrupt]]	The event dispatched to registered callbacks and any interrupts raised by the user.

Raises:

Type	Description
RuntimeError	If at least one callback is async.
ValueError	If interrupt name is used more than once.

Example

event = StartRequestEvent(agent=my_agent)
registry.invoke_callbacks(event)

Source code in strands/hooks/registry.py

def invoke_callbacks(self, event: TInvokeEvent) -> tuple[TInvokeEvent, list[Interrupt]]:
    """Invoke all registered callbacks for the given event.

    This method finds all callbacks registered for the event's type and
    invokes them in the appropriate order. For events with should_reverse_callbacks=True,
    callbacks are invoked in reverse registration order. Any exceptions raised by callback
    functions will propagate to the caller.

    Additionally, this method aggregates interrupts raised by the user to instantiate human-in-the-loop workflows.

    Args:
        event: The event to dispatch to registered callbacks.

    Returns:
        The event dispatched to registered callbacks and any interrupts raised by the user.

    Raises:
        RuntimeError: If at least one callback is async.
        ValueError: If interrupt name is used more than once.

    Example:
        ```python
        event = StartRequestEvent(agent=my_agent)
        registry.invoke_callbacks(event)
        ```
    """
    callbacks = list(self.get_callbacks_for(event))
    interrupts: dict[str, Interrupt] = {}

    if any(inspect.iscoroutinefunction(callback) for callback in callbacks):
        raise RuntimeError(f"event=<{event}> | use invoke_callbacks_async to invoke async callback")

    for callback in callbacks:
        try:
            callback(event)
        except InterruptException as exception:
            interrupt = exception.interrupt
            if interrupt.name in interrupts:
                message = f"interrupt_name=<{interrupt.name}> | interrupt name used more than once"
                logger.error(message)
                raise ValueError(message) from exception

            # Each callback is allowed to raise their own interrupt.
            interrupts[interrupt.name] = interrupt

    return event, list(interrupts.values())

invoke_callbacks_async(event) async

Invoke all registered callbacks for the given event.

This method finds all callbacks registered for the event's type and invokes them in the appropriate order. For events with should_reverse_callbacks=True, callbacks are invoked in reverse registration order. Any exceptions raised by callback functions will propagate to the caller.

Additionally, this method aggregates interrupts raised by the user to instantiate human-in-the-loop workflows.

Parameters:

Name	Type	Description	Default
event	TInvokeEvent	The event to dispatch to registered callbacks.	required

Returns:

Type	Description
tuple[TInvokeEvent, list[Interrupt]]	The event dispatched to registered callbacks and any interrupts raised by the user.

Raises:

Type	Description
ValueError	If interrupt name is used more than once.

Example

event = StartRequestEvent(agent=my_agent)
await registry.invoke_callbacks_async(event)

Source code in strands/hooks/registry.py

async def invoke_callbacks_async(self, event: TInvokeEvent) -> tuple[TInvokeEvent, list[Interrupt]]:
    """Invoke all registered callbacks for the given event.

    This method finds all callbacks registered for the event's type and
    invokes them in the appropriate order. For events with should_reverse_callbacks=True,
    callbacks are invoked in reverse registration order. Any exceptions raised by callback
    functions will propagate to the caller.

    Additionally, this method aggregates interrupts raised by the user to instantiate human-in-the-loop workflows.

    Args:
        event: The event to dispatch to registered callbacks.

    Returns:
        The event dispatched to registered callbacks and any interrupts raised by the user.

    Raises:
        ValueError: If interrupt name is used more than once.

    Example:
        ```python
        event = StartRequestEvent(agent=my_agent)
        await registry.invoke_callbacks_async(event)
        ```
    """
    interrupts: dict[str, Interrupt] = {}

    for callback in self.get_callbacks_for(event):
        try:
            if inspect.iscoroutinefunction(callback):
                await callback(event)
            else:
                callback(event)

        except InterruptException as exception:
            interrupt = exception.interrupt
            if interrupt.name in interrupts:
                message = f"interrupt_name=<{interrupt.name}> | interrupt name used more than once"
                logger.error(message)
                raise ValueError(message) from exception

            # Each callback is allowed to raise their own interrupt.
            interrupts[interrupt.name] = interrupt

    return event, list(interrupts.values())

Interrupt dataclass

Represents an interrupt that can pause agent execution for human-in-the-loop workflows.

Attributes:

Name	Type	Description
id	str	Unique identifier.
name	str	User defined name.
reason	Any	User provided reason for raising the interrupt.
response	Any	Human response provided when resuming the agent after an interrupt.

Source code in strands/interrupt.py

@dataclass
class Interrupt:
    """Represents an interrupt that can pause agent execution for human-in-the-loop workflows.

    Attributes:
        id: Unique identifier.
        name: User defined name.
        reason: User provided reason for raising the interrupt.
        response: Human response provided when resuming the agent after an interrupt.
    """

    id: str
    name: str
    reason: Any = None
    response: Any = None

    def to_dict(self) -> dict[str, Any]:
        """Serialize to dict for session management."""
        return asdict(self)

to_dict()

Serialize to dict for session management.

Source code in strands/interrupt.py

def to_dict(self) -> dict[str, Any]:
    """Serialize to dict for session management."""
    return asdict(self)

InterruptException

Bases: Exception

Exception raised when human input is required.

Source code in strands/interrupt.py

class InterruptException(Exception):
    """Exception raised when human input is required."""

    def __init__(self, interrupt: Interrupt) -> None:
        """Set the interrupt."""
        self.interrupt = interrupt

__init__(interrupt)

Set the interrupt.

Source code in strands/interrupt.py

def __init__(self, interrupt: Interrupt) -> None:
    """Set the interrupt."""
    self.interrupt = interrupt