Stream handler

Streaming graph execution handler for TAF workflows.

This module provides the StreamHandler class, which manages the execution of graph workflows with support for streaming output, interrupts, state persistence, and event publishing. It enables incremental result processing, pause/resume capabilities, and robust error handling for agent workflows that require real-time or chunked responses.

Classes:

Name	Description
StreamHandler	Handles streaming execution for graph workflows in TAF.

Attributes:

Name	Type	Description
StateT
logger

Attributes

StateT module-attribute

StateT = TypeVar('StateT', bound=AgentState)

logger module-attribute

logger = getLogger(__name__)

Classes

StreamHandler

Bases: BaseLoggingMixin, InterruptConfigMixin

Handles streaming execution for graph workflows in TAF.

StreamHandler manages the execution of agent workflows as directed graphs, supporting streaming output, pause/resume via interrupts, state persistence, and event publishing for monitoring and debugging. It enables incremental result processing and robust error handling for complex agent workflows.

Attributes:

Name	Type	Description
nodes	dict[str, Node]	Dictionary mapping node names to Node instances.
edges	list[Edge]	List of Edge instances defining graph connections and routing.
interrupt_before		List of node names where execution should pause before execution.
interrupt_after		List of node names where execution should pause after execution.

Example

handler = StreamHandler(nodes, edges)
async for chunk in handler.stream(input_data, config, state):
    print(chunk)

Methods:

Name	Description
__init__
stream	Execute the graph asynchronously with streaming output.

Source code in agentflow/graph/utils/stream_handler.py

class StreamHandler[StateT: AgentState](
    BaseLoggingMixin,
    InterruptConfigMixin,
):
    """Handles streaming execution for graph workflows in TAF.

    StreamHandler manages the execution of agent workflows as directed graphs,
    supporting streaming output, pause/resume via interrupts, state persistence,
    and event publishing for monitoring and debugging. It enables incremental
    result processing and robust error handling for complex agent workflows.

    Attributes:
        nodes: Dictionary mapping node names to Node instances.
        edges: List of Edge instances defining graph connections and routing.
        interrupt_before: List of node names where execution should pause before execution.
        interrupt_after: List of node names where execution should pause after execution.

    Example:
        ```python
        handler = StreamHandler(nodes, edges)
        async for chunk in handler.stream(input_data, config, state):
            print(chunk)
        ```
    """

    @inject
    def __init__(
        self,
        nodes: dict[str, Node],
        edges: list[Edge],
        interrupt_before: list[str] | None = None,
        interrupt_after: list[str] | None = None,
    ):
        self.nodes: dict[str, Node] = nodes
        self.edges: list[Edge] = edges
        self.interrupt_before = interrupt_before or []
        self.interrupt_after = interrupt_after or []
        self._set_interrupts(interrupt_before, interrupt_after)

    async def _check_interrupted(
        self,
        state: StateT,
        input_data: dict[str, Any],
        config: dict[str, Any],
    ) -> dict[str, Any]:
        if state.is_interrupted():
            logger.info(
                "Resuming from interrupted state at node '%s'", state.execution_meta.current_node
            )
            # This is a resume case - clear interrupt and merge input data
            if input_data:
                config["resume_data"] = input_data
                logger.debug("Added resume data with %d keys", len(input_data))
            state.clear_interrupt()
        elif not input_data.get("messages") and not state.context:
            # This is a fresh execution - validate input data
            error_msg = "Input data must contain 'messages' for new execution."
            logger.error(error_msg)
            raise ValueError(error_msg)
        else:
            logger.info(
                "Starting fresh execution with %d messages", len(input_data.get("messages", []))
            )

        return config

    async def _check_and_handle_interrupt(
        self,
        current_node: str,
        interrupt_type: str,
        state: StateT,
        config: dict[str, Any],
    ) -> bool:
        """Check for interrupts and save state if needed. Returns True if interrupted."""
        interrupt_nodes: list[str] = (
            self.interrupt_before if interrupt_type == "before" else self.interrupt_after
        ) or []

        if current_node in interrupt_nodes:
            status = (
                ExecutionStatus.INTERRUPTED_BEFORE
                if interrupt_type == "before"
                else ExecutionStatus.INTERRUPTED_AFTER
            )
            state.set_interrupt(
                current_node,
                f"interrupt_{interrupt_type}: {current_node}",
                status,
            )
            # Save state and interrupt
            await sync_data(
                state=state,
                config=config,
                messages=[],
                trim=True,
            )
            logger.debug("Node '%s' interrupted", current_node)
            return True

        logger.debug(
            "No interrupts found for node '%s', continuing execution",
            current_node,
        )
        return False

    async def _interrupt_graph(
        self,
        current_node: str,
        state: StateT,
        config: dict[str, Any],
    ) -> bool:
        """Check for interrupts and save state if needed. Returns True if interrupted."""
        status = ExecutionStatus.INTERRUPTED_AFTER
        state.set_interrupt(
            current_node,
            f"interrupt_after: {current_node}",
            status,
        )
        # Save state and interrupt
        await sync_data(
            state=state,
            config=config,
            messages=[],
            trim=False,
        )
        logger.debug("Node '%s' interrupted", current_node)
        return True

    async def _check_stop_requested(
        self,
        state: StateT,
        current_node: str,
        event: EventModel,
        messages: list[Message],
        config: dict[str, Any],
    ) -> bool:
        """Check if a stop has been requested externally."""
        state = await reload_state(config, state)  # type: ignore

        # Check if a stop was requested externally (e.g., frontend)
        if state.is_stopped_requested():
            logger.info(
                "Stop requested for thread '%s' at node '%s'",
                config.get("thread_id"),
                current_node,
            )
            state.set_interrupt(
                current_node,
                "stop_requested",
                ExecutionStatus.INTERRUPTED_AFTER,
                data={"source": "stop", "info": "requested via is_stopped_requested"},
            )
            await sync_data(state=state, config=config, messages=messages, trim=True)
            event.event_type = EventType.INTERRUPTED
            event.metadata["interrupted"] = "Stop"
            event.metadata["status"] = "Graph execution stopped by request"
            event.data["state"] = state.model_dump()
            publish_event(event)
            return True
        return False

    async def _execute_graph(  # noqa: PLR0912, PLR0915
        self,
        state: StateT,
        input_data: dict[str, Any],
        config: dict[str, Any],
    ) -> AsyncIterable[StreamChunk]:
        """
        Execute the entire graph with support for interrupts and resuming.

        Why so many chunks are yielded?
        We allow user to set response type, if they want low granularity
        Only few chunks like Message will be sent to user
        """
        logger.info(
            "Starting graph execution from node '%s' at step %d",
            state.execution_meta.current_node,
            state.execution_meta.step,
        )
        messages: list[Message] = []
        messages_ids = set()
        max_steps = config.get("recursion_limit", 25)
        logger.debug("Max steps limit set to %d", max_steps)

        last_human_messages = input_data.get("messages", []) or []
        # Stream initial input messages (e.g., human messages) so callers see full conversation
        # Only emit when present and avoid duplicates by tracking message_ids and existing context
        for m in last_human_messages:
            if m.message_id not in messages_ids:
                messages.append(m)
                messages_ids.add(m.message_id)
                yield StreamChunk(
                    event=StreamEvent.MESSAGE,
                    message=m,
                    metadata={
                        "status": "invoking_graph",
                        "reason": "initial human message",
                    },
                    thread_id=config.get("thread_id"),
                    run_id=config.get("run_id"),
                )

        # Get current execution info from state
        current_node = state.execution_meta.current_node
        step = state.execution_meta.step

        # Create event for graph execution
        event = EventModel.default(
            config,
            data={"state": state.model_dump(exclude={"execution_meta"})},
            content_type=[ContentType.STATE],
            extra={"step": step, "current_node": current_node},
            event=Event.GRAPH_EXECUTION,
            node_name=current_node,
        )

        yield StreamChunk(
            event=StreamEvent.UPDATES,
            data={
                "status": "invoking_graph",
                "node": current_node,
                "step": step,
                "max_steps": max_steps,
            },
            thread_id=config.get("thread_id"),
            run_id=config.get("run_id"),
        )

        try:
            while current_node != END and step < max_steps:
                logger.debug("Executing step %d at node '%s'", step, current_node)

                # TODO: check if ai called for a tool in that case we should remove last message
                res = await self._check_stop_requested(
                    state,
                    current_node,
                    event,
                    messages,
                    config,
                )
                if res:
                    event.event_type = EventType.INTERRUPTED
                    event.metadata["status"] = "Graph execution stopped by request"
                    event.data["state"] = state.model_dump()
                    publish_event(event)
                    # stream updated state and updates
                    yield StreamChunk(
                        event=StreamEvent.UPDATES,
                        data={
                            "status": "invoking_node",
                            "node": current_node,
                            "step": step,
                            "max_steps": max_steps,
                            "reason": "Graph execution stopped by request",
                        },
                        thread_id=config.get("thread_id"),
                        run_id=config.get("run_id"),
                    )
                    return

                # Update execution metadata
                state.set_current_node(current_node)
                state.execution_meta.step = step
                await call_realtime_sync(state, config)

                # Update event with current step info
                event.data["step"] = step
                event.data["current_node"] = current_node
                event.event_type = EventType.PROGRESS
                event.metadata["status"] = f"Executing step {step} at node '{current_node}'"
                publish_event(event)

                # Check for interrupt_before
                if await self._check_and_handle_interrupt(
                    current_node,
                    "before",
                    state,
                    config,
                ):
                    logger.info("Graph execution interrupted before node '%s'", current_node)
                    event.event_type = EventType.INTERRUPTED
                    event.metadata["status"] = "Graph execution interrupted before node execution"
                    event.metadata["interrupted"] = "Before"
                    event.data["interrupted"] = "Before"
                    publish_event(event)
                    yield StreamChunk(
                        event=StreamEvent.UPDATES,
                        data={
                            "status": "invoking_node",
                            "node": current_node,
                            "step": step,
                            "max_steps": max_steps,
                            "reason": "Graph execution interrupted before node execution",
                        },
                        thread_id=config.get("thread_id"),
                        run_id=config.get("run_id"),
                    )
                    return

                # Execute current node
                logger.debug("Executing node '%s'", current_node)
                node = self.nodes[current_node]

                ####################################################
                ############ Execute Node ##########################
                ####################################################
                result = node.stream(config, state)  # type: ignore
                logger.debug("Node '%s' execution completed", current_node)
                ####################################################
                ############ Execute Node ##########################
                ####################################################

                res = await self._check_stop_requested(
                    state,
                    current_node,
                    event,
                    messages,
                    config,
                )
                if res:
                    event.event_type = EventType.INTERRUPTED
                    event.metadata["status"] = "Graph execution stopped by request"
                    event.data["state"] = state.model_dump()
                    publish_event(event)
                    yield StreamChunk(
                        event=StreamEvent.UPDATES,
                        data={
                            "status": "invoking_node",
                            "node": current_node,
                            "step": step,
                            "max_steps": max_steps,
                            "reason": "Graph execution stopped by request",
                        },
                        thread_id=config.get("thread_id"),
                        run_id=config.get("run_id"),
                    )
                    return

                # Process result and get next node
                yield StreamChunk(
                    event=StreamEvent.UPDATES,
                    data={
                        "status": "node_invoked",
                        "node": current_node,
                        "step": step,
                        "max_steps": max_steps,
                    },
                    thread_id=config.get("thread_id"),
                    run_id=config.get("run_id"),
                )

                # From Here message no need to stream its already streamed
                # from execute node function, only stream updates and state
                next_node = None
                async for rs in result:
                    # Allow stop to break inner result loop as well
                    if isinstance(rs, StreamChunk):
                        yield rs

                    # if message and remote tool call then yield immediately
                    elif isinstance(rs, Message) and RemoteToolCallBlock in rs.content:
                        # now interrupt the graph
                        await self._interrupt_graph(
                            current_node,
                            state,
                            config,
                        )
                        yield StreamChunk(
                            event=StreamEvent.UPDATES,
                            data={
                                "status": "node_invoked",
                                "node": current_node,
                                "step": step,
                                "max_steps": max_steps,
                                "reason": "Remote tool call - graph interrupted",
                            },
                            thread_id=config.get("thread_id"),
                            run_id=config.get("run_id"),
                        )
                        return

                    elif isinstance(rs, Message) and not rs.delta:
                        if rs.message_id not in messages_ids:
                            messages.append(rs)
                            messages_ids.add(rs.message_id)

                    elif isinstance(rs, dict) and "is_non_streaming" in rs:
                        if rs["is_non_streaming"]:
                            new_state = rs.get("state", None)
                            if new_state:
                                state = new_state
                                yield StreamChunk(
                                    event=StreamEvent.STATE,
                                    state=state,
                                    metadata={
                                        "node": current_node,
                                        "step": step,
                                    },
                                    thread_id=config.get("thread_id"),
                                    run_id=config.get("run_id"),
                                )

                            new_messages = rs.get("messages", [])
                            for m in new_messages:
                                if m.message_id not in messages_ids and not m.delta:
                                    messages.append(m)
                                    messages_ids.add(m.message_id)
                            next_node = rs.get("next_node", next_node)
                        else:
                            # Streaming path completed: ensure any collected messages are persisted
                            new_messages = rs.get("messages", [])
                            for m in new_messages:
                                if m.message_id not in messages_ids and not m.delta:
                                    messages.append(m)
                                    messages_ids.add(m.message_id)
                            next_node = rs.get("next_node", next_node)
                    else:
                        # Process as node result (non-streaming path)
                        try:
                            state, new_messages, next_node = await process_node_result(
                                rs,
                                state,
                                [],
                            )
                            for m in new_messages:
                                if m.message_id not in messages_ids and not m.delta:
                                    messages.append(m)
                                    messages_ids.add(m.message_id)
                        except Exception as e:
                            logger.error("Failed to process node result: %s", e)

                logger.debug(
                    "Node result processed, next_node=%s, total_messages=%d",
                    next_node,
                    len(messages),
                )

                # Add collected messages to state context
                if messages:
                    state.context = add_messages(state.context, messages)
                    logger.debug("Added %d messages to state context", len(messages))
                    yield StreamChunk(
                        event=StreamEvent.STATE,
                        state=state,
                        metadata={
                            "node": current_node,
                            "step": step,
                        },
                        thread_id=config.get("thread_id"),
                        run_id=config.get("run_id"),
                    )

                # Call realtime sync after node execution
                await call_realtime_sync(state, config)
                event.event_type = EventType.UPDATE
                event.data["state"] = state.model_dump()
                event.data["messages"] = [m.model_dump() for m in messages] if messages else []
                if messages:
                    lm = messages[-1]
                    event.content = lm.text() if isinstance(lm.content, list) else lm.content
                    if isinstance(lm.content, list):
                        event.content_blocks = lm.content
                event.content_type = [ContentType.STATE, ContentType.MESSAGE]
                publish_event(event)

                # Check for interrupt_after
                if await self._check_and_handle_interrupt(
                    current_node,
                    "after",
                    state,
                    config,
                ):
                    logger.info("Graph execution interrupted after node '%s'", current_node)
                    # For interrupt_after, advance to next node before pausing
                    if next_node is None:
                        next_node = get_next_node(current_node, state, self.edges)
                    state.set_current_node(next_node)

                    event.event_type = EventType.INTERRUPTED
                    event.data["interrupted"] = "After"
                    event.metadata["interrupted"] = "After"
                    event.data["state"] = state.model_dump()
                    publish_event(event)

                    yield StreamChunk(
                        event=StreamEvent.UPDATES,
                        data={
                            "status": "node_invoked",
                            "node": current_node,
                            "step": step,
                            "max_steps": max_steps,
                            "reason": "Graph execution interrupted before node execution",
                        },
                        thread_id=config.get("thread_id"),
                        run_id=config.get("run_id"),
                    )
                    return

                # Get next node
                if next_node is None:
                    current_node = get_next_node(current_node, state, self.edges)
                    logger.debug("Next node determined by graph logic: '%s'", current_node)
                else:
                    current_node = next_node
                    logger.debug("Next node determined by command: '%s'", current_node)

                # Advance step after successful node execution
                step += 1
                state.advance_step()
                await call_realtime_sync(state, config)

                event.event_type = EventType.UPDATE
                event.metadata["State_Updated"] = "State Updated"
                event.data["state"] = state.model_dump()
                publish_event(event)

                yield StreamChunk(
                    event=StreamEvent.UPDATES,
                    state=state,
                    data={
                        "status": "node_invoked",
                        "node": current_node,
                        "step": step,
                        "max_steps": max_steps,
                    },
                    thread_id=config.get("thread_id"),
                    run_id=config.get("run_id"),
                )

                if step >= max_steps:
                    error_msg = "Graph execution exceeded maximum steps"
                    logger.error(error_msg)
                    state.error(error_msg)
                    await call_realtime_sync(state, config)

                    event.event_type = EventType.ERROR
                    event.data["state"] = state.model_dump()
                    event.metadata["error"] = error_msg
                    event.metadata["step"] = step
                    event.metadata["current_node"] = current_node
                    publish_event(event)

                    yield Message(
                        role="assistant",
                        content=[ErrorBlock(text=error_msg)],  # type: ignore
                    )

                    yield StreamChunk(
                        event=StreamEvent.ERROR,
                        state=state,
                        data={
                            "status": "graph_invoked",
                            "node": current_node,
                            "step": step,
                            "max_steps": max_steps,
                            "reason": error_msg,
                        },
                        thread_id=config.get("thread_id"),
                        run_id=config.get("run_id"),
                    )

                    raise GraphRecursionError(
                        f"Graph execution exceeded recursion limit: {max_steps}"
                    )

            # Execution completed successfully
            logger.info(
                "Graph execution completed successfully at node '%s' after %d steps",
                current_node,
                step,
            )
            state.complete()
            is_context_trimmed = await sync_data(
                state=state,
                config=config,
                messages=messages,
                trim=True,
            )

            # Create completion event
            event.event_type = EventType.END
            event.data["state"] = state.model_dump()
            event.data["messages"] = [m.model_dump() for m in messages] if messages else []
            if messages:
                fm = messages[-1]
                event.content = fm.text() if isinstance(fm.content, list) else fm.content
                if isinstance(fm.content, list):
                    event.content_blocks = fm.content
            event.content_type = [ContentType.STATE, ContentType.MESSAGE]
            event.metadata["status"] = "Graph execution completed"
            event.metadata["step"] = step
            event.metadata["current_node"] = current_node
            event.metadata["is_context_trimmed"] = is_context_trimmed
            publish_event(event)

            yield StreamChunk(
                event=StreamEvent.UPDATES,
                state=state,
                data={
                    "status": "graph_invoked",
                    "node": current_node,
                    "step": step,
                    "max_steps": max_steps,
                    "is_context_trimmed": is_context_trimmed,
                    "reason": "Graph execution completed successfully",
                },
                thread_id=config.get("thread_id"),
                run_id=config.get("run_id"),
            )

        except Exception as e:
            # Handle execution errors
            logger.exception("Graph execution failed: %s", e)
            state.error(str(e))

            # Publish error event
            event.event_type = EventType.ERROR
            event.metadata["error"] = str(e)
            event.data["state"] = state.model_dump()
            publish_event(event)

            await sync_data(
                state=state,
                config=config,
                messages=messages,
                trim=True,
            )

            yield StreamChunk(
                event=StreamEvent.ERROR,
                state=state,
                data={
                    "status": "invoked_graph",
                    "node": current_node,
                    "step": step,
                    "max_steps": max_steps,
                    "reason": str(e),
                },
                thread_id=config.get("thread_id"),
                run_id=config.get("run_id"),
            )

            raise e

    async def stream(
        self,
        input_data: dict[str, Any],
        config: dict[str, Any],
        default_state: StateT,
        response_granularity: ResponseGranularity = ResponseGranularity.LOW,
    ) -> AsyncGenerator[StreamChunk]:
        """Execute the graph asynchronously with streaming output.

        Runs the graph workflow from start to finish, yielding incremental results
        as they become available. Automatically detects whether to start a fresh
        execution or resume from an interrupted state, supporting pause/resume
        and checkpointing.

        Args:
            input_data: Input dictionary for graph execution. For new executions,
                should contain 'messages' key with initial messages. For resumed
                executions, can contain additional data to merge.
            config: Configuration dictionary containing execution settings and context.
            default_state: Initial or template AgentState for workflow execution.
            response_granularity: Level of detail in the response (LOW, PARTIAL, FULL).

        Yields:
            Message objects representing incremental results from graph execution.
            The exact type and frequency of yields depends on node implementations
            and workflow configuration.

        Raises:
            GraphRecursionError: If execution exceeds recursion limit.
            ValueError: If input_data is invalid for new execution.
            Various exceptions: Depending on node execution failures.

        Example:
            ```python
            async for chunk in handler.stream(input_data, config, state):
                print(chunk)
            ```
        """
        logger.info(
            "Starting asynchronous graph execution with %d input keys, granularity=%s",
            len(input_data) if input_data else 0,
            response_granularity,
        )
        config = config or {}
        input_data = input_data or {}

        start_time = time.time()

        # Load or initialize state
        logger.debug("Loading or creating state from input data")
        new_state = await load_or_create_state(
            input_data,
            config,
            default_state,
        )
        state: StateT = new_state  # type: ignore[assignment]
        logger.debug(
            "State loaded: interrupted=%s, current_node=%s, step=%d",
            state.is_interrupted(),
            state.execution_meta.current_node,
            state.execution_meta.step,
        )

        cfg = config.copy()
        if "user" in cfg:
            # This will be available when you are calling
            # vi agentflow api
            del cfg["user"]

        event = EventModel.default(
            config,
            data={"state": state},
            content_type=[ContentType.STATE],
            extra={
                "is_interrupted": state.is_interrupted(),
                "current_node": state.execution_meta.current_node,
                "step": state.execution_meta.step,
                "config": cfg,
                "response_granularity": response_granularity.value,
            },
        )

        # Publish graph initialization event
        publish_event(event)

        # Check if this is a resume case
        config = await self._check_interrupted(state, input_data, config)

        # Now start Execution
        # Execute graph
        logger.debug("Beginning graph execution")
        result = self._execute_graph(state, input_data, config)

        # Stream results based on response granularity
        async for chunk in result:
            match response_granularity:
                case ResponseGranularity.FULL:
                    yield chunk
                case ResponseGranularity.PARTIAL:
                    if chunk.event != StreamEvent.UPDATES:
                        yield chunk
                case ResponseGranularity.LOW:
                    if chunk.event in [StreamEvent.MESSAGE, StreamEvent.ERROR]:
                        yield chunk

        # Publish graph completion event
        time_taken = time.time() - start_time
        logger.info("Graph execution finished in %.2f seconds", time_taken)

        event.event_type = EventType.END
        event.metadata.update(
            {
                "time_taken": time_taken,
                "state": state.model_dump(),
                "step": state.execution_meta.step,
                "current_node": state.execution_meta.current_node,
                "is_interrupted": state.is_interrupted(),
                "total_messages": len(state.context) if state.context else 0,
            }
        )
        publish_event(event)
        yield StreamChunk(
            event=StreamEvent.UPDATES,
            state=state,
            data={
                "status": "graph_invoked",
                "reason": "Graph execution finished",
                "time_taken": time_taken,
                "is_interrupted": state.is_interrupted(),
                "total_messages": len(state.context) if state.context else 0,
            },
            thread_id=config.get("thread_id"),
            run_id=config.get("run_id"),
        )

Attributes

edges instance-attribute

edges = edges

interrupt_after instance-attribute

interrupt_after = interrupt_after or []

interrupt_before instance-attribute

interrupt_before = interrupt_before or []

nodes instance-attribute

nodes = nodes

Functions

__init__

__init__(nodes, edges, interrupt_before=None, interrupt_after=None)

Source code in agentflow/graph/utils/stream_handler.py

@inject
def __init__(
    self,
    nodes: dict[str, Node],
    edges: list[Edge],
    interrupt_before: list[str] | None = None,
    interrupt_after: list[str] | None = None,
):
    self.nodes: dict[str, Node] = nodes
    self.edges: list[Edge] = edges
    self.interrupt_before = interrupt_before or []
    self.interrupt_after = interrupt_after or []
    self._set_interrupts(interrupt_before, interrupt_after)

stream async

stream(input_data, config, default_state, response_granularity=ResponseGranularity.LOW)

Execute the graph asynchronously with streaming output.

Runs the graph workflow from start to finish, yielding incremental results as they become available. Automatically detects whether to start a fresh execution or resume from an interrupted state, supporting pause/resume and checkpointing.

Parameters:

Name	Type	Description	Default
input_data	dict[str, Any]	Input dictionary for graph execution. For new executions, should contain 'messages' key with initial messages. For resumed executions, can contain additional data to merge.	required
config	dict[str, Any]	Configuration dictionary containing execution settings and context.	required
default_state	StateT	Initial or template AgentState for workflow execution.	required
response_granularity	ResponseGranularity	Level of detail in the response (LOW, PARTIAL, FULL).	LOW

Yields:

Type	Description
AsyncGenerator[StreamChunk]	Message objects representing incremental results from graph execution.
AsyncGenerator[StreamChunk]	The exact type and frequency of yields depends on node implementations
AsyncGenerator[StreamChunk]	and workflow configuration.

Raises:

Type	Description
GraphRecursionError	If execution exceeds recursion limit.
ValueError	If input_data is invalid for new execution.
Various exceptions	Depending on node execution failures.

Example

async for chunk in handler.stream(input_data, config, state):
    print(chunk)

Source code in agentflow/graph/utils/stream_handler.py

async def stream(
    self,
    input_data: dict[str, Any],
    config: dict[str, Any],
    default_state: StateT,
    response_granularity: ResponseGranularity = ResponseGranularity.LOW,
) -> AsyncGenerator[StreamChunk]:
    """Execute the graph asynchronously with streaming output.

    Runs the graph workflow from start to finish, yielding incremental results
    as they become available. Automatically detects whether to start a fresh
    execution or resume from an interrupted state, supporting pause/resume
    and checkpointing.

    Args:
        input_data: Input dictionary for graph execution. For new executions,
            should contain 'messages' key with initial messages. For resumed
            executions, can contain additional data to merge.
        config: Configuration dictionary containing execution settings and context.
        default_state: Initial or template AgentState for workflow execution.
        response_granularity: Level of detail in the response (LOW, PARTIAL, FULL).

    Yields:
        Message objects representing incremental results from graph execution.
        The exact type and frequency of yields depends on node implementations
        and workflow configuration.

    Raises:
        GraphRecursionError: If execution exceeds recursion limit.
        ValueError: If input_data is invalid for new execution.
        Various exceptions: Depending on node execution failures.

    Example:
        ```python
        async for chunk in handler.stream(input_data, config, state):
            print(chunk)
        ```
    """
    logger.info(
        "Starting asynchronous graph execution with %d input keys, granularity=%s",
        len(input_data) if input_data else 0,
        response_granularity,
    )
    config = config or {}
    input_data = input_data or {}

    start_time = time.time()

    # Load or initialize state
    logger.debug("Loading or creating state from input data")
    new_state = await load_or_create_state(
        input_data,
        config,
        default_state,
    )
    state: StateT = new_state  # type: ignore[assignment]
    logger.debug(
        "State loaded: interrupted=%s, current_node=%s, step=%d",
        state.is_interrupted(),
        state.execution_meta.current_node,
        state.execution_meta.step,
    )

    cfg = config.copy()
    if "user" in cfg:
        # This will be available when you are calling
        # vi agentflow api
        del cfg["user"]

    event = EventModel.default(
        config,
        data={"state": state},
        content_type=[ContentType.STATE],
        extra={
            "is_interrupted": state.is_interrupted(),
            "current_node": state.execution_meta.current_node,
            "step": state.execution_meta.step,
            "config": cfg,
            "response_granularity": response_granularity.value,
        },
    )

    # Publish graph initialization event
    publish_event(event)

    # Check if this is a resume case
    config = await self._check_interrupted(state, input_data, config)

    # Now start Execution
    # Execute graph
    logger.debug("Beginning graph execution")
    result = self._execute_graph(state, input_data, config)

    # Stream results based on response granularity
    async for chunk in result:
        match response_granularity:
            case ResponseGranularity.FULL:
                yield chunk
            case ResponseGranularity.PARTIAL:
                if chunk.event != StreamEvent.UPDATES:
                    yield chunk
            case ResponseGranularity.LOW:
                if chunk.event in [StreamEvent.MESSAGE, StreamEvent.ERROR]:
                    yield chunk

    # Publish graph completion event
    time_taken = time.time() - start_time
    logger.info("Graph execution finished in %.2f seconds", time_taken)

    event.event_type = EventType.END
    event.metadata.update(
        {
            "time_taken": time_taken,
            "state": state.model_dump(),
            "step": state.execution_meta.step,
            "current_node": state.execution_meta.current_node,
            "is_interrupted": state.is_interrupted(),
            "total_messages": len(state.context) if state.context else 0,
        }
    )
    publish_event(event)
    yield StreamChunk(
        event=StreamEvent.UPDATES,
        state=state,
        data={
            "status": "graph_invoked",
            "reason": "Graph execution finished",
            "time_taken": time_taken,
            "is_interrupted": state.is_interrupted(),
            "total_messages": len(state.context) if state.context else 0,
        },
        thread_id=config.get("thread_id"),
        run_id=config.get("run_id"),
    )

Functions