from __future__ import annotations

import asyncio
import copy
import os
import logging
from dataclasses import dataclass, field
from typing import Any, cast

from openai.types.responses import ResponseCompletedEvent

logger = logging.getLogger(__name__)

from ._run_impl import (
    AgentToolUseTracker,
    NextStepFinalOutput,
    NextStepHandoff,
    NextStepRunAgain,
    QueueCompleteSentinel,
    RunImpl,
    SingleStepResult,
    TraceCtxManager,
    get_model_tracing_impl,
)
from .agent import Agent
from .agent_output import AgentOutputSchema
from .exceptions import (
    AgentsException,
    InputGuardrailTripwireTriggered,
    MaxTurnsExceeded,
    ModelBehaviorError,
    OutputGuardrailTripwireTriggered,
)
from .guardrail import InputGuardrail, InputGuardrailResult, OutputGuardrail, OutputGuardrailResult
from .handoffs import Handoff, HandoffInputFilter, handoff
from .items import ItemHelpers, ModelResponse, RunItem, TResponseInputItem
from .lifecycle import RunHooks
from .logger import logger
from .model_settings import ModelSettings
from .models.interface import Model, ModelProvider
from .models.openai_provider import OpenAIProvider
from .result import RunResult, RunResultStreaming
from .run_context import RunContextWrapper, TContext
from .stream_events import AgentUpdatedStreamEvent, RawResponsesStreamEvent
from .tool import Tool
from .tracing import Span, SpanError, agent_span, get_current_trace, trace
from .tracing.span_data import AgentSpanData
from .usage import Usage
from .util import _coro, _error_tracing

# CYBERSENTRY_MAX_TURNS must be converted to an int to avoid type mismatch error when comparing.
max_turns_env = os.getenv("CYBERSENTRY_MAX_TURNS")
if max_turns_env is not None:
    try:
        DEFAULT_MAX_TURNS = int(max_turns_env)
    except ValueError:
        try:
            DEFAULT_MAX_TURNS = float(max_turns_env)
        except ValueError:
            DEFAULT_MAX_TURNS = float("inf")
else:
    DEFAULT_MAX_TURNS = float("inf")

price_limit_env = os.getenv("CYBERSENTRY_PRICE_LIMIT")
if price_limit_env is not None:
    try:
        DEFAULT_PRICE_LIMIT = float(price_limit_env)
    except ValueError:
        DEFAULT_PRICE_LIMIT = float("inf")
else:
    DEFAULT_PRICE_LIMIT = float("inf")


@dataclass
class RunConfig:
    """Configures settings for the entire agent run."""

    model: str | Model | None = None
    """The model to use for the entire agent run. If set, will override the model set on every
    agent. The model_provider passed in below must be able to resolve this model name.
    """

    model_provider: ModelProvider = field(default_factory=OpenAIProvider)
    """The model provider to use when looking up string model names. Defaults to OpenAI."""

    model_settings: ModelSettings | None = None
    """Configure global model settings. Any non-null values will override the agent-specific model
    settings.
    """

    handoff_input_filter: HandoffInputFilter | None = None
    """A global input filter to apply to all handoffs. If `Handoff.input_filter` is set, then that
    will take precedence. The input filter allows you to edit the inputs that are sent to the new
    agent. See the documentation in `Handoff.input_filter` for more details.
    """

    input_guardrails: list[InputGuardrail[Any]] | None = None
    """A list of input guardrails to run on the initial run input."""

    output_guardrails: list[OutputGuardrail[Any]] | None = None
    """A list of output guardrails to run on the final output of the run."""

    tracing_disabled: bool = False
    """Whether tracing is disabled for the agent run. If disabled, we will not trace the agent run.
    """

    trace_include_sensitive_data: bool = True
    """Whether we include potentially sensitive data (for example: inputs/outputs of tool calls or
    LLM generations) in traces. If False, we'll still create spans for these events, but the
    sensitive data will not be included.
    """

    workflow_name: str = "Agent workflow"
    """The name of the run, used for tracing. Should be a logical name for the run, like
    "Code generation workflow" or "Customer support agent".
    """

    trace_id: str | None = None
    """A custom trace ID to use for tracing. If not provided, we will generate a new trace ID."""

    group_id: str | None = None
    """
    A grouping identifier to use for tracing, to link multiple traces from the same conversation
    or process. For example, you might use a chat thread ID.
    """

    trace_metadata: dict[str, Any] | None = None
    """
    An optional dictionary of additional metadata to include with the trace.
    """


class Runner:
    @classmethod
    async def run(
        cls,
        starting_agent: Agent[TContext],
        input: str | list[TResponseInputItem],
        *,
        context: TContext | None = None,
        max_turns: int = DEFAULT_MAX_TURNS,
        hooks: RunHooks[TContext] | None = None,
        run_config: RunConfig | None = None,
    ) -> RunResult:
        """Run a workflow starting at the given agent. The agent will run in a loop until a final
        output is generated. The loop runs like so:
        1. The agent is invoked with the given input.
        2. If there is a final output (i.e. the agent produces something of type
            `agent.output_type`, the loop terminates.
        3. If there's a handoff, we run the loop again, with the new agent.
        4. Else, we run tool calls (if any), and re-run the loop.

        In two cases, the agent may raise an exception:
        1. If the max_turns is exceeded, a MaxTurnsExceeded exception is raised.
        2. If a guardrail tripwire is triggered, a GuardrailTripwireTriggered exception is raised.

        Note that only the first agent's input guardrails are run.

        Args:
            starting_agent: The starting agent to run.
            input: The initial input to the agent. You can pass a single string for a user message,
                or a list of input items.
            context: The context to run the agent with.
            max_turns: The maximum number of turns to run the agent for. A turn is defined as one
                AI invocation (including any tool calls that might occur).
            hooks: An object that receives callbacks on various lifecycle events.
            run_config: Global settings for the entire agent run.

        Returns:
            A run result containing all the inputs, guardrail results and the output of the last
            agent. Agents may perform handoffs, so we don't know the specific type of the output.
        """
        if hooks is None:
            hooks = RunHooks[Any]()
        if run_config is None:
            run_config = RunConfig()

        tool_use_tracker = AgentToolUseTracker()

        with TraceCtxManager(
            workflow_name=run_config.workflow_name,
            trace_id=run_config.trace_id,
            group_id=run_config.group_id,
            metadata=run_config.trace_metadata,
            disabled=run_config.tracing_disabled,
        ):
            current_turn = 0
            original_input: str | list[TResponseInputItem] = copy.deepcopy(input)
            generated_items: list[RunItem] = []
            model_responses: list[ModelResponse] = []

            context_wrapper: RunContextWrapper[TContext] = RunContextWrapper(
                context=context,  # type: ignore
            )

            input_guardrail_results: list[InputGuardrailResult] = []

            current_span: Span[AgentSpanData] | None = None
            current_agent = starting_agent
            should_run_agent_start_hooks = True

            try:
                while True:
                    # Start an agent span if we don't have one. This span is ended if the current
                    # agent changes, or if the agent loop ends.
                    if current_span is None:
                        handoff_names = [h.agent_name for h in cls._get_handoffs(current_agent)]
                        if output_schema := cls._get_output_schema(current_agent):
                            output_type_name = output_schema.output_type_name()
                        else:
                            output_type_name = "str"

                        current_span = agent_span(
                            name=current_agent.name,
                            handoffs=handoff_names,
                            output_type=output_type_name,
                        )
                        current_span.start(mark_as_current=True)

                        all_tools = await cls._get_all_tools(current_agent)
                        current_span.span_data.tools = [t.name for t in all_tools]

                    current_turn += 1
                    if current_turn > max_turns:
                        _error_tracing.attach_error_to_span(
                            current_span,
                            SpanError(
                                message="Max turns exceeded",
                                data={"max_turns": max_turns},
                            ),
                        )
                        raise MaxTurnsExceeded(f"Max turns ({max_turns}) exceeded")

                    logger.debug(
                        f"Running agent {current_agent.name} (turn {current_turn})",
                    )

                    if current_turn == 1:
                        input_guardrail_results, turn_result = await asyncio.gather(
                            cls._run_input_guardrails(
                                starting_agent,
                                starting_agent.input_guardrails
                                + (run_config.input_guardrails or []),
                                copy.deepcopy(input),
                                context_wrapper,
                            ),
                            cls._run_single_turn(
                                agent=current_agent,
                                all_tools=all_tools,
                                original_input=original_input,
                                generated_items=generated_items,
                                hooks=hooks,
                                context_wrapper=context_wrapper,
                                run_config=run_config,
                                should_run_agent_start_hooks=should_run_agent_start_hooks,
                                tool_use_tracker=tool_use_tracker,
                            ),
                        )
                    else:
                        turn_result = await cls._run_single_turn(
                            agent=current_agent,
                            all_tools=all_tools,
                            original_input=original_input,
                            generated_items=generated_items,
                            hooks=hooks,
                            context_wrapper=context_wrapper,
                            run_config=run_config,
                            should_run_agent_start_hooks=should_run_agent_start_hooks,
                            tool_use_tracker=tool_use_tracker,
                        )
                    should_run_agent_start_hooks = False

                    model_responses.append(turn_result.model_response)
                    original_input = turn_result.original_input
                    generated_items = turn_result.generated_items

                    if isinstance(turn_result.next_step, NextStepFinalOutput):
                        output_guardrail_results = await cls._run_output_guardrails(
                            current_agent.output_guardrails + (run_config.output_guardrails or []),
                            current_agent,
                            turn_result.next_step.output,
                            context_wrapper,
                        )
                        return RunResult(
                            input=original_input,
                            new_items=generated_items,
                            raw_responses=model_responses,
                            final_output=turn_result.next_step.output,
                            _last_agent=current_agent,
                            input_guardrail_results=input_guardrail_results,
                            output_guardrail_results=output_guardrail_results,
                        )
                    elif isinstance(turn_result.next_step, NextStepHandoff):
                        # Get the previous agent before switching
                        previous_agent = current_agent
                        current_agent = cast(Agent[TContext], turn_result.next_step.new_agent)
                        
                        # Transfer message history for swarm patterns
                        # Check if both agents have models with message_history
                        if (hasattr(previous_agent, 'model') and hasattr(previous_agent.model, 'message_history') and
                            hasattr(current_agent, 'model') and hasattr(current_agent.model, 'message_history')):
                            # Import the is_swarm_pattern function from patterns utils
                            try:
                                from cybersentry.agents.patterns.utils import is_swarm_pattern
                                # Check if either agent is part of a swarm pattern
                                if is_swarm_pattern(previous_agent) or is_swarm_pattern(current_agent):
                                    # Transfer the message history to the new agent
                                    current_agent.model.message_history = previous_agent.model.message_history
                                    # Also share history in AGENT_MANAGER
                                    if hasattr(previous_agent, 'name') and hasattr(current_agent, 'name'):
                                        from cybersentry.sdk.agents.simple_agent_manager import AGENT_MANAGER
                                        AGENT_MANAGER.share_swarm_history(previous_agent.name, current_agent.name)
                            except ImportError:
                                # If we can't import, check if agents have bidirectional handoffs
                                # by looking if the new agent can handoff back to the previous agent
                                if hasattr(current_agent, 'handoffs'):
                                    for handoff_item in current_agent.handoffs:
                                        if hasattr(handoff_item, 'agent_name') and handoff_item.agent_name == previous_agent.name:
                                            # Bidirectional handoff detected, share history
                                            current_agent.model.message_history = previous_agent.model.message_history
                                            break
                        
                        # Register the handoff agent with AGENT_MANAGER for tracking
                        # This ensures patterns/swarms work with commands like /history and /graph
                        from cybersentry.sdk.agents.simple_agent_manager import AGENT_MANAGER
                        if hasattr(current_agent, 'name'):
                            # For non-parallel patterns, use set_active_agent which will handle it as single agent
                            # This maintains compatibility with single agent commands
                            AGENT_MANAGER.set_active_agent(current_agent, current_agent.name)
                        
                        current_span.finish(reset_current=True)
                        current_span = None
                        should_run_agent_start_hooks = True
                    elif isinstance(turn_result.next_step, NextStepRunAgain):
                        pass
                    else:
                        raise AgentsException(
                            f"Unknown next step type: {type(turn_result.next_step)}"
                        )
            finally:
                if current_span:
                    current_span.finish(reset_current=True)

    @classmethod
    def run_sync(
        cls,
        starting_agent: Agent[TContext],
        input: str | list[TResponseInputItem],
        *,
        context: TContext | None = None,
        max_turns: int = DEFAULT_MAX_TURNS,
        hooks: RunHooks[TContext] | None = None,
        run_config: RunConfig | None = None,
    ) -> RunResult:
        """Run a workflow synchronously, starting at the given agent. Note that this just wraps the
        `run` method, so it will not work if there's already an event loop (e.g. inside an async
        function, or in a Jupyter notebook or async context like FastAPI). For those cases, use
        the `run` method instead.

        The agent will run in a loop until a final output is generated. The loop runs like so:
        1. The agent is invoked with the given input.
        2. If there is a final output (i.e. the agent produces something of type
            `agent.output_type`, the loop terminates.
        3. If there's a handoff, we run the loop again, with the new agent.
        4. Else, we run tool calls (if any), and re-run the loop.

        In two cases, the agent may raise an exception:
        1. If the max_turns is exceeded, a MaxTurnsExceeded exception is raised.
        2. If a guardrail tripwire is triggered, a GuardrailTripwireTriggered exception is raised.

        Note that only the first agent's input guardrails are run.

        Args:
            starting_agent: The starting agent to run.
            input: The initial input to the agent. You can pass a single string for a user message,
                or a list of input items.
            context: The context to run the agent with.
            max_turns: The maximum number of turns to run the agent for. A turn is defined as one
                AI invocation (including any tool calls that might occur).
            hooks: An object that receives callbacks on various lifecycle events.
            run_config: Global settings for the entire agent run.

        Returns:
            A run result containing all the inputs, guardrail results and the output of the last
            agent. Agents may perform handoffs, so we don't know the specific type of the output.
        """
        return asyncio.get_event_loop().run_until_complete(
            cls.run(
                starting_agent,
                input,
                context=context,
                max_turns=max_turns,
                hooks=hooks,
                run_config=run_config,
            )
        )

    @classmethod
    def run_streamed(
        cls,
        starting_agent: Agent[TContext],
        input: str | list[TResponseInputItem],
        context: TContext | None = None,
        max_turns: int = DEFAULT_MAX_TURNS,
        hooks: RunHooks[TContext] | None = None,
        run_config: RunConfig | None = None,
    ) -> RunResultStreaming:
        """Run a workflow starting at the given agent in streaming mode. The returned result object
        contains a method you can use to stream semantic events as they are generated.

        The agent will run in a loop until a final output is generated. The loop runs like so:
        1. The agent is invoked with the given input.
        2. If there is a final output (i.e. the agent produces something of type
            `agent.output_type`, the loop terminates.
        3. If there's a handoff, we run the loop again, with the new agent.
        4. Else, we run tool calls (if any), and re-run the loop.

        In two cases, the agent may raise an exception:
        1. If the max_turns is exceeded, a MaxTurnsExceeded exception is raised.
        2. If a guardrail tripwire is triggered, a GuardrailTripwireTriggered exception is raised.

        Note that only the first agent's input guardrails are run.

        Args:
            starting_agent: The starting agent to run.
            input: The initial input to the agent. You can pass a single string for a user message,
                or a list of input items.
            context: The context to run the agent with.
            max_turns: The maximum number of turns to run the agent for. A turn is defined as one
                AI invocation (including any tool calls that might occur).
            hooks: An object that receives callbacks on various lifecycle events.
            run_config: Global settings for the entire agent run.

        Returns:
            A result object that contains data about the run, as well as a method to stream events.
        """
        if hooks is None:
            hooks = RunHooks[Any]()
        if run_config is None:
            run_config = RunConfig()

        # If there's already a trace, we don't create a new one. In addition, we can't end the
        # trace here, because the actual work is done in `stream_events` and this method ends
        # before that.
        new_trace = (
            None
            if get_current_trace()
            else trace(
                workflow_name=run_config.workflow_name,
                trace_id=run_config.trace_id,
                group_id=run_config.group_id,
                metadata=run_config.trace_metadata,
                disabled=run_config.tracing_disabled,
            )
        )
        # Need to start the trace here, because the current trace contextvar is captured at
        # asyncio.create_task time
        if new_trace:
            new_trace.start(mark_as_current=True)

        output_schema = cls._get_output_schema(starting_agent)
        context_wrapper: RunContextWrapper[TContext] = RunContextWrapper(
            context=context  # type: ignore
        )

        streamed_result = RunResultStreaming(
            input=copy.deepcopy(input),
            new_items=[],
            current_agent=starting_agent,
            raw_responses=[],
            final_output=None,
            is_complete=False,
            current_turn=0,
            max_turns=max_turns,
            input_guardrail_results=[],
            output_guardrail_results=[],
            _current_agent_output_schema=output_schema,
            _trace=new_trace,
        )

        # Kick off the actual agent loop in the background and return the streamed result object.
        streamed_result._run_impl_task = asyncio.create_task(
            cls._run_streamed_impl(
                starting_input=input,
                streamed_result=streamed_result,
                starting_agent=starting_agent,
                max_turns=max_turns,
                hooks=hooks,
                context_wrapper=context_wrapper,
                run_config=run_config,
            )
        )
        return streamed_result

    @classmethod
    async def _run_input_guardrails_with_queue(
        cls,
        agent: Agent[Any],
        guardrails: list[InputGuardrail[TContext]],
        input: str | list[TResponseInputItem],
        context: RunContextWrapper[TContext],
        streamed_result: RunResultStreaming,
        parent_span: Span[Any],
    ):
        queue = streamed_result._input_guardrail_queue

        # We'll run the guardrails and push them onto the queue as they complete
        guardrail_tasks = [
            asyncio.create_task(
                RunImpl.run_single_input_guardrail(agent, guardrail, input, context)
            )
            for guardrail in guardrails
        ]
        guardrail_results = []
        try:
            for done in asyncio.as_completed(guardrail_tasks):
                result = await done
                if result.output.tripwire_triggered:
                    _error_tracing.attach_error_to_span(
                        parent_span,
                        SpanError(
                            message="Guardrail tripwire triggered",
                            data={
                                "guardrail": result.guardrail.get_name(),
                                "type": "input_guardrail",
                            },
                        ),
                    )
                queue.put_nowait(result)
                guardrail_results.append(result)
        except Exception:
            for t in guardrail_tasks:
                t.cancel()
            raise

        streamed_result.input_guardrail_results = guardrail_results

    @classmethod
    async def _run_streamed_impl(
        cls,
        starting_input: str | list[TResponseInputItem],
        streamed_result: RunResultStreaming,
        starting_agent: Agent[TContext],
        max_turns: int,
        hooks: RunHooks[TContext],
        context_wrapper: RunContextWrapper[TContext],
        run_config: RunConfig,
    ):
        current_span: Span[AgentSpanData] | None = None
        current_agent = starting_agent
        current_turn = 0
        should_run_agent_start_hooks = True
        tool_use_tracker = AgentToolUseTracker()

        streamed_result._event_queue.put_nowait(AgentUpdatedStreamEvent(new_agent=current_agent))

        try:
            while True:
                if streamed_result.is_complete:
                    break

                # Start an agent span if we don't have one. This span is ended if the current
                # agent changes, or if the agent loop ends.
                if current_span is None:
                    handoff_names = [h.agent_name for h in cls._get_handoffs(current_agent)]
                    if output_schema := cls._get_output_schema(current_agent):
                        output_type_name = output_schema.output_type_name()
                    else:
                        output_type_name = "str"

                    current_span = agent_span(
                        name=current_agent.name,
                        handoffs=handoff_names,
                        output_type=output_type_name,
                    )
                    current_span.start(mark_as_current=True)

                    all_tools = await cls._get_all_tools(current_agent)
                    tool_names = [t.name for t in all_tools]
                    current_span.span_data.tools = tool_names
                current_turn += 1
                streamed_result.current_turn = current_turn

                if current_turn > max_turns:
                    _error_tracing.attach_error_to_span(
                        current_span,
                        SpanError(
                            message="Max turns exceeded",
                            data={"max_turns": max_turns},
                        ),
                    )
                    streamed_result._event_queue.put_nowait(QueueCompleteSentinel())
                    break

                if current_turn == 1:
                    # Run the input guardrails in the background and put the results on the queue
                    streamed_result._input_guardrails_task = asyncio.create_task(
                        cls._run_input_guardrails_with_queue(
                            starting_agent,
                            starting_agent.input_guardrails + (run_config.input_guardrails or []),
                            copy.deepcopy(ItemHelpers.input_to_new_input_list(starting_input)),
                            context_wrapper,
                            streamed_result,
                            current_span,
                        )
                    )
                try:
                    turn_result = await cls._run_single_turn_streamed(
                        streamed_result,
                        current_agent,
                        hooks,
                        context_wrapper,
                        run_config,
                        should_run_agent_start_hooks,
                        tool_use_tracker,
                        all_tools,
                    )
                    should_run_agent_start_hooks = False
                    
                    # Process the turn result
                    streamed_result.raw_responses = streamed_result.raw_responses + [
                        turn_result.model_response
                    ]
                    streamed_result.input = turn_result.original_input
                    streamed_result.new_items = turn_result.generated_items

                    if isinstance(turn_result.next_step, NextStepHandoff):
                        # Get the previous agent before switching
                        previous_agent = current_agent
                        current_agent = turn_result.next_step.new_agent
                        
                        # Transfer message history for swarm patterns
                        # Check if both agents have models with message_history
                        if (hasattr(previous_agent, 'model') and hasattr(previous_agent.model, 'message_history') and
                            hasattr(current_agent, 'model') and hasattr(current_agent.model, 'message_history')):
                            # Import the is_swarm_pattern function from patterns utils
                            try:
                                from cybersentry.agents.patterns.utils import is_swarm_pattern
                                # Check if either agent is part of a swarm pattern
                                if is_swarm_pattern(previous_agent) or is_swarm_pattern(current_agent):
                                    # Transfer the message history to the new agent
                                    current_agent.model.message_history = previous_agent.model.message_history
                                    # Also share history in AGENT_MANAGER
                                    if hasattr(previous_agent, 'name') and hasattr(current_agent, 'name'):
                                        from cybersentry.sdk.agents.simple_agent_manager import AGENT_MANAGER
                                        AGENT_MANAGER.share_swarm_history(previous_agent.name, current_agent.name)
                            except ImportError:
                                # If we can't import, check if agents have bidirectional handoffs
                                # by looking if the new agent can handoff back to the previous agent
                                if hasattr(current_agent, 'handoffs'):
                                    for handoff_item in current_agent.handoffs:
                                        if hasattr(handoff_item, 'agent_name') and handoff_item.agent_name == previous_agent.name:
                                            # Bidirectional handoff detected, share history
                                            current_agent.model.message_history = previous_agent.model.message_history
                                            break
                        
                        current_span.finish(reset_current=True)
                        current_span = None
                        should_run_agent_start_hooks = True
                        streamed_result._event_queue.put_nowait(
                            AgentUpdatedStreamEvent(new_agent=current_agent)
                        )
                    elif isinstance(turn_result.next_step, NextStepFinalOutput):
                        streamed_result._output_guardrails_task = asyncio.create_task(
                            cls._run_output_guardrails(
                                current_agent.output_guardrails
                                + (run_config.output_guardrails or []),
                                current_agent,
                                turn_result.next_step.output,
                                context_wrapper,
                            )
                        )

                        try:
                            output_guardrail_results = await streamed_result._output_guardrails_task
                        except Exception:
                            # Exceptions will be checked in the stream_events loop
                            output_guardrail_results = []

                        streamed_result.output_guardrail_results = output_guardrail_results
                        streamed_result.final_output = turn_result.next_step.output
                        streamed_result.is_complete = True
                        streamed_result._event_queue.put_nowait(QueueCompleteSentinel())
                    elif isinstance(turn_result.next_step, NextStepRunAgain):
                        pass
                except (KeyboardInterrupt, asyncio.CancelledError) as e:
                    # Re-raise to propagate the interruption
                    raise e
                except Exception as e:
                    if current_span:
                        _error_tracing.attach_error_to_span(
                            current_span,
                            SpanError(
                                message="Error in agent run",
                                data={"error": str(e)},
                            ),
                        )
                    streamed_result.is_complete = True
                    streamed_result._event_queue.put_nowait(QueueCompleteSentinel())
                    raise

            streamed_result.is_complete = True
        finally:
            if current_span:
                current_span.finish(reset_current=True)

    @classmethod
    async def _run_single_turn_streamed(
        cls,
        streamed_result: RunResultStreaming,
        agent: Agent[TContext],
        hooks: RunHooks[TContext],
        context_wrapper: RunContextWrapper[TContext],
        run_config: RunConfig,
        should_run_agent_start_hooks: bool,
        tool_use_tracker: AgentToolUseTracker,
        all_tools: list[Tool],
    ) -> SingleStepResult:
        if should_run_agent_start_hooks:
            await asyncio.gather(
                hooks.on_agent_start(context_wrapper, agent),
                (
                    agent.hooks.on_start(context_wrapper, agent)
                    if agent.hooks
                    else _coro.noop_coroutine()
                ),
            )

        output_schema = cls._get_output_schema(agent)

        streamed_result.current_agent = agent
        streamed_result._current_agent_output_schema = output_schema

        system_prompt = await agent.get_system_prompt(context_wrapper)

        handoffs = cls._get_handoffs(agent)
        model = cls._get_model(agent, run_config)
        model_settings = agent.model_settings.resolve(run_config.model_settings)
        model_settings = RunImpl.maybe_reset_tool_choice(agent, tool_use_tracker, model_settings)

        # Ensure agent model is set in model_settings for streaming mode
        if not hasattr(model_settings, "agent_model") or not model_settings.agent_model:
            if isinstance(agent.model, str):
                model_settings.agent_model = agent.model
            elif isinstance(run_config.model, str):
                model_settings.agent_model = run_config.model

        final_response: ModelResponse | None = None

        input = ItemHelpers.input_to_new_input_list(streamed_result.input)
        input.extend([item.to_input_item() for item in streamed_result.new_items])

        # 1. Stream the output events
        async for event in model.stream_response(
            system_prompt,
            input,
            model_settings,
            all_tools,
            output_schema,
            handoffs,
            get_model_tracing_impl(
                run_config.tracing_disabled, run_config.trace_include_sensitive_data
            ),
        ):
            if isinstance(event, ResponseCompletedEvent):
                usage = (
                    Usage(
                        requests=1,
                        input_tokens=event.response.usage.input_tokens,
                        output_tokens=event.response.usage.output_tokens,
                        total_tokens=event.response.usage.total_tokens,
                    )
                    if event.response.usage
                    else Usage()
                )
                final_response = ModelResponse(
                    output=event.response.output,
                    usage=usage,
                    referenceable_id=event.response.id,
                )

            streamed_result._event_queue.put_nowait(RawResponsesStreamEvent(data=event))

        # 2. At this point, the streaming is complete for this turn of the agent loop.
        if not final_response:
            raise ModelBehaviorError("Model did not produce a final response!")

        # 3. Now, we can process the turn as we do in the non-streaming case
        single_step_result = None
        try:
            single_step_result = await cls._get_single_step_result_from_response(
                agent=agent,
                original_input=streamed_result.input,
                pre_step_items=streamed_result.new_items,
                new_response=final_response,
                output_schema=output_schema,
                all_tools=all_tools,
                handoffs=handoffs,
                hooks=hooks,
                context_wrapper=context_wrapper,
                run_config=run_config,
                tool_use_tracker=tool_use_tracker,
            )

            RunImpl.stream_step_result_to_queue(single_step_result, streamed_result._event_queue)
            return single_step_result
        except (KeyboardInterrupt, asyncio.CancelledError) as e:
            # When interrupted, we need to ensure the message history is consistent
            # The tool calls were already added during streaming, but results were not
            # If we have a partial result, stream it before re-raising
            if single_step_result:
                RunImpl.stream_step_result_to_queue(single_step_result, streamed_result._event_queue)
            raise e

    @classmethod
    async def _run_single_turn(
        cls,
        *,
        agent: Agent[TContext],
        all_tools: list[Tool],
        original_input: str | list[TResponseInputItem],
        generated_items: list[RunItem],
        hooks: RunHooks[TContext],
        context_wrapper: RunContextWrapper[TContext],
        run_config: RunConfig,
        should_run_agent_start_hooks: bool,
        tool_use_tracker: AgentToolUseTracker,
    ) -> SingleStepResult:
        # Ensure we run the hooks before anything else
        if should_run_agent_start_hooks:
            await asyncio.gather(
                hooks.on_agent_start(context_wrapper, agent),
                (
                    agent.hooks.on_start(context_wrapper, agent)
                    if agent.hooks
                    else _coro.noop_coroutine()
                ),
            )

        system_prompt = await agent.get_system_prompt(context_wrapper)

        output_schema = cls._get_output_schema(agent)
        handoffs = cls._get_handoffs(agent)
        input = ItemHelpers.input_to_new_input_list(original_input)
        input.extend([generated_item.to_input_item() for generated_item in generated_items])

        new_response = await cls._get_new_response(
            agent,
            system_prompt,
            input,
            output_schema,
            all_tools,
            handoffs,
            context_wrapper,
            run_config,
            tool_use_tracker,
        )

        return await cls._get_single_step_result_from_response(
            agent=agent,
            original_input=original_input,
            pre_step_items=generated_items,
            new_response=new_response,
            output_schema=output_schema,
            all_tools=all_tools,
            handoffs=handoffs,
            hooks=hooks,
            context_wrapper=context_wrapper,
            run_config=run_config,
            tool_use_tracker=tool_use_tracker,
        )

    @classmethod
    async def _get_single_step_result_from_response(
        cls,
        *,
        agent: Agent[TContext],
        all_tools: list[Tool],
        original_input: str | list[TResponseInputItem],
        pre_step_items: list[RunItem],
        new_response: ModelResponse,
        output_schema: AgentOutputSchema | None,
        handoffs: list[Handoff],
        hooks: RunHooks[TContext],
        context_wrapper: RunContextWrapper[TContext],
        run_config: RunConfig,
        tool_use_tracker: AgentToolUseTracker,
    ) -> SingleStepResult:
        processed_response = RunImpl.process_model_response(
            agent=agent,
            all_tools=all_tools,
            response=new_response,
            output_schema=output_schema,
            handoffs=handoffs,
        )

        # Log tools used with robust type checking
        if hasattr(processed_response, "tools_used") and processed_response.tools_used:
            for i, tool_call in enumerate(processed_response.tools_used):
                try:
                    # Safely extract tool name with multiple fallbacks
                    tool_name = "Unknown"
                    try:
                        if hasattr(tool_call, "tool"):
                            if isinstance(tool_call.tool, str):
                                tool_name = tool_call.tool
                            elif hasattr(tool_call.tool, "name"):
                                tool_name = tool_call.tool.name
                            else:
                                tool_name = str(tool_call.tool)
                    except Exception:
                        pass

                    # Safely extract call_id
                    call_id = "Unknown"
                    try:
                        if hasattr(tool_call, "call_id"):
                            call_id = str(tool_call.call_id)
                    except Exception:
                        pass

                    # Safely extract parsed_args
                    parsed_args = "Unknown"
                    try:
                        if hasattr(tool_call, "parsed_args"):
                            parsed_args = str(tool_call.parsed_args)
                    except Exception:
                        pass
                except Exception:
                    pass

        tool_use_tracker.add_tool_use(agent, processed_response.tools_used)

        return await RunImpl.execute_tools_and_side_effects(
            agent=agent,
            original_input=original_input,
            pre_step_items=pre_step_items,
            new_response=new_response,
            processed_response=processed_response,
            output_schema=output_schema,
            hooks=hooks,
            context_wrapper=context_wrapper,
            run_config=run_config,
        )

    @classmethod
    async def _run_input_guardrails(
        cls,
        agent: Agent[Any],
        guardrails: list[InputGuardrail[TContext]],
        input: str | list[TResponseInputItem],
        context: RunContextWrapper[TContext],
    ) -> list[InputGuardrailResult]:
        if not guardrails:
            return []

        guardrail_tasks = [
            asyncio.create_task(
                RunImpl.run_single_input_guardrail(agent, guardrail, input, context)
            )
            for guardrail in guardrails
        ]

        guardrail_results = []

        for done in asyncio.as_completed(guardrail_tasks):
            result = await done
            if result.output.tripwire_triggered:
                # Cancel all guardrail tasks if a tripwire is triggered.
                for t in guardrail_tasks:
                    t.cancel()
                _error_tracing.attach_error_to_current_span(
                    SpanError(
                        message="Guardrail tripwire triggered",
                        data={"guardrail": result.guardrail.get_name()},
                    )
                )
                raise InputGuardrailTripwireTriggered(result)
            else:
                guardrail_results.append(result)

        return guardrail_results

    @classmethod
    async def _run_output_guardrails(
        cls,
        guardrails: list[OutputGuardrail[TContext]],
        agent: Agent[TContext],
        agent_output: Any,
        context: RunContextWrapper[TContext],
    ) -> list[OutputGuardrailResult]:
        if not guardrails:
            return []

        guardrail_tasks = [
            asyncio.create_task(
                RunImpl.run_single_output_guardrail(guardrail, agent, agent_output, context)
            )
            for guardrail in guardrails
        ]

        guardrail_results = []

        for done in asyncio.as_completed(guardrail_tasks):
            result = await done
            if result.output.tripwire_triggered:
                # Cancel all guardrail tasks if a tripwire is triggered.
                for t in guardrail_tasks:
                    t.cancel()
                _error_tracing.attach_error_to_current_span(
                    SpanError(
                        message="Guardrail tripwire triggered",
                        data={"guardrail": result.guardrail.get_name()},
                    )
                )
                raise OutputGuardrailTripwireTriggered(result)
            else:
                guardrail_results.append(result)

        return guardrail_results

    @classmethod
    async def _get_new_response(
        cls,
        agent: Agent[TContext],
        system_prompt: str | None,
        input: list[TResponseInputItem],
        output_schema: AgentOutputSchema | None,
        all_tools: list[Tool],
        handoffs: list[Handoff],
        context_wrapper: RunContextWrapper[TContext],
        run_config: RunConfig,
        tool_use_tracker: AgentToolUseTracker,
    ) -> ModelResponse:
        model = cls._get_model(agent, run_config)
        model_settings = agent.model_settings.resolve(run_config.model_settings)
        model_settings = RunImpl.maybe_reset_tool_choice(agent, tool_use_tracker, model_settings)

        # Ensure agent model is set in model_settings
        if not hasattr(model_settings, "agent_model") or not model_settings.agent_model:
            if isinstance(agent.model, str):
                model_settings.agent_model = agent.model
            elif isinstance(run_config.model, str):
                model_settings.agent_model = run_config.model

        new_response = await model.get_response(
            system_instructions=system_prompt,
            input=input,
            model_settings=model_settings,
            tools=all_tools,
            output_schema=output_schema,
            handoffs=handoffs,
            tracing=get_model_tracing_impl(
                run_config.tracing_disabled, run_config.trace_include_sensitive_data
            ),
        )

        context_wrapper.usage.add(new_response.usage)

        return new_response

    @classmethod
    def _get_output_schema(cls, agent: Agent[Any]) -> AgentOutputSchema | None:
        if agent.output_type is None or agent.output_type is str:
            return None

        return AgentOutputSchema(agent.output_type)

    @classmethod
    def _get_handoffs(cls, agent: Agent[Any]) -> list[Handoff]:
        handoffs = []
        for handoff_item in agent.handoffs:
            if isinstance(handoff_item, Handoff):
                handoffs.append(handoff_item)
            elif isinstance(handoff_item, Agent):
                handoffs.append(handoff(handoff_item))
        return handoffs

    @classmethod
    async def _get_all_tools(cls, agent: Agent[Any]) -> list[Tool]:
        return await agent.get_all_tools()

    @classmethod
    def _get_model(cls, agent: Agent[Any], run_config: RunConfig) -> Model:
        model = None
        agent_model = None
        if isinstance(run_config.model, Model):
            model = run_config.model
        elif isinstance(run_config.model, str):
            model = run_config.model_provider.get_model(run_config.model)
            agent_model = run_config.model
        elif isinstance(agent.model, Model):
            model = agent.model
        else:
            model = run_config.model_provider.get_model(agent.model)
            agent_model = agent.model

        # Store the original agent model in model_settings for later use
        if agent_model and hasattr(agent, "model_settings"):
            agent.model_settings.agent_model = agent_model

        # Set agent name if the model supports it (for CLI display)
        if hasattr(model, "set_agent_name"):
            model.set_agent_name(agent.name)

        return model
