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from __future__ import annotations
import asyncio
import contextlib
import warnings
from typing import Union, cast
from typing_extensions import Unpack
from . import _debug
from .agent import Agent
from .agent_tool_state import set_agent_tool_state_scope
from .exceptions import (
AgentsException,
InputGuardrailTripwireTriggered,
MaxTurnsExceeded,
RunErrorDetails,
UserError,
)
from .guardrail import (
InputGuardrailResult,
)
from .items import (
ItemHelpers,
RunItem,
TResponseInputItem,
)
from .lifecycle import RunHooks
from .logger import logger
from .memory import Session
from .result import RunResult, RunResultStreaming
from .run_config import (
DEFAULT_MAX_TURNS,
CallModelData,
CallModelInputFilter,
ModelInputData,
ReasoningItemIdPolicy,
RunConfig,
RunOptions,
ToolErrorFormatter,
ToolErrorFormatterArgs,
)
from .run_context import RunContextWrapper, TContext
from .run_error_handlers import RunErrorHandlers
from .run_internal.agent_runner_helpers import (
append_model_response_if_new,
apply_resumed_conversation_settings,
build_interruption_result,
build_resumed_stream_debug_extra,
ensure_context_wrapper,
finalize_conversation_tracking,
input_guardrails_triggered,
resolve_processed_response,
resolve_resumed_context,
resolve_trace_settings,
save_turn_items_if_needed,
should_cancel_parallel_model_task_on_input_guardrail_trip,
update_run_state_for_interruption,
validate_session_conversation_settings,
)
from .run_internal.approvals import approvals_from_step
from .run_internal.error_handlers import (
build_run_error_data,
create_message_output_item,
format_final_output_text,
resolve_run_error_handler_result,
validate_handler_final_output,
)
from .run_internal.items import (
copy_input_items,
normalize_resumed_input,
)
from .run_internal.oai_conversation import OpenAIServerConversationTracker
from .run_internal.run_loop import (
get_all_tools,
get_handoffs,
get_output_schema,
initialize_computer_tools,
resolve_interrupted_turn,
run_final_output_hooks,
run_input_guardrails,
run_output_guardrails,
run_single_turn,
start_streaming,
validate_run_hooks,
)
from .run_internal.run_steps import (
NextStepFinalOutput,
NextStepHandoff,
NextStepInterruption,
NextStepRunAgain,
)
from .run_internal.session_persistence import (
persist_session_items_for_guardrail_trip,
prepare_input_with_session,
resumed_turn_items,
save_result_to_session,
save_resumed_turn_items,
session_items_for_turn,
update_run_state_after_resume,
)
from .run_internal.tool_use_tracker import (
AgentToolUseTracker,
hydrate_tool_use_tracker,
serialize_tool_use_tracker,
)
from .run_state import RunState
from .tool import dispose_resolved_computers
from .tool_guardrails import ToolInputGuardrailResult, ToolOutputGuardrailResult
from .tracing import Span, SpanError, agent_span, get_current_trace
from .tracing.context import TraceCtxManager, create_trace_for_run
from .tracing.span_data import AgentSpanData
from .util import _error_tracing
DEFAULT_AGENT_RUNNER: AgentRunner = None # type: ignore
# the value is set at the end of the module
__all__ = [
"AgentRunner",
"Runner",
"RunConfig",
"RunOptions",
"RunState",
"RunContextWrapper",
"ModelInputData",
"CallModelData",
"CallModelInputFilter",
"ReasoningItemIdPolicy",
"ToolErrorFormatter",
"ToolErrorFormatterArgs",
"DEFAULT_MAX_TURNS",
"set_default_agent_runner",
"get_default_agent_runner",
]
def set_default_agent_runner(runner: AgentRunner | None) -> None:
"""
WARNING: this class is experimental and not part of the public API
It should not be used directly.
"""
global DEFAULT_AGENT_RUNNER
DEFAULT_AGENT_RUNNER = runner or AgentRunner()
def get_default_agent_runner() -> AgentRunner:
"""
WARNING: this class is experimental and not part of the public API
It should not be used directly.
"""
global DEFAULT_AGENT_RUNNER
return DEFAULT_AGENT_RUNNER
class Runner:
@classmethod
async def run(
cls,
starting_agent: Agent[TContext],
input: str | list[TResponseInputItem] | RunState[TContext],
*,
context: TContext | None = None,
max_turns: int = DEFAULT_MAX_TURNS,
hooks: RunHooks[TContext] | None = None,
run_config: RunConfig | None = None,
error_handlers: RunErrorHandlers[TContext] | None = None,
previous_response_id: str | None = None,
auto_previous_response_id: bool = False,
conversation_id: str | None = None,
session: Session | 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 unless handled.
2. If a guardrail tripwire is triggered, a GuardrailTripwireTriggered
exception is raised.
Note:
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.
error_handlers: Error handlers keyed by error kind. Currently supports max_turns.
previous_response_id: The ID of the previous response. If using OpenAI
models via the Responses API, this allows you to skip passing in input
from the previous turn.
conversation_id: The conversation ID
(https://platform.openai.com/docs/guides/conversation-state?api-mode=responses).
If provided, the conversation will be used to read and write items.
Every agent will have access to the conversation history so far,
and its output items will be written to the conversation.
We recommend only using this if you are exclusively using OpenAI models;
other model providers don't write to the Conversation object,
so you'll end up having partial conversations stored.
session: A session for automatic conversation history management.
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.
"""
runner = DEFAULT_AGENT_RUNNER
return await runner.run(
starting_agent,
input,
context=context,
max_turns=max_turns,
hooks=hooks,
run_config=run_config,
error_handlers=error_handlers,
previous_response_id=previous_response_id,
auto_previous_response_id=auto_previous_response_id,
conversation_id=conversation_id,
session=session,
)
@classmethod
def run_sync(
cls,
starting_agent: Agent[TContext],
input: str | list[TResponseInputItem] | RunState[TContext],
*,
context: TContext | None = None,
max_turns: int = DEFAULT_MAX_TURNS,
hooks: RunHooks[TContext] | None = None,
run_config: RunConfig | None = None,
error_handlers: RunErrorHandlers[TContext] | None = None,
previous_response_id: str | None = None,
auto_previous_response_id: bool = False,
conversation_id: str | None = None,
session: Session | None = None,
) -> RunResult:
"""
Run a workflow synchronously, starting at the given agent.
Note:
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:
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 unless handled.
2. If a guardrail tripwire is triggered, a GuardrailTripwireTriggered
exception is raised.
Note:
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.
error_handlers: Error handlers keyed by error kind. Currently supports max_turns.
previous_response_id: The ID of the previous response, if using OpenAI
models via the Responses API, this allows you to skip passing in input
from the previous turn.
conversation_id: The ID of the stored conversation, if any.
session: A session for automatic conversation history management.
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.
"""
runner = DEFAULT_AGENT_RUNNER
return runner.run_sync(
starting_agent,
input,
context=context,
max_turns=max_turns,
hooks=hooks,
run_config=run_config,
error_handlers=error_handlers,
previous_response_id=previous_response_id,
conversation_id=conversation_id,
session=session,
auto_previous_response_id=auto_previous_response_id,
)
@classmethod
def run_streamed(
cls,
starting_agent: Agent[TContext],
input: str | list[TResponseInputItem] | RunState[TContext],
context: TContext | None = None,
max_turns: int = DEFAULT_MAX_TURNS,
hooks: RunHooks[TContext] | None = None,
run_config: RunConfig | None = None,
previous_response_id: str | None = None,
auto_previous_response_id: bool = False,
conversation_id: str | None = None,
session: Session | None = None,
*,
error_handlers: RunErrorHandlers[TContext] | 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 unless handled.
2. If a guardrail tripwire is triggered, a GuardrailTripwireTriggered
exception is raised.
Note:
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.
error_handlers: Error handlers keyed by error kind. Currently supports max_turns.
previous_response_id: The ID of the previous response, if using OpenAI
models via the Responses API, this allows you to skip passing in input
from the previous turn.
conversation_id: The ID of the stored conversation, if any.
session: A session for automatic conversation history management.
Returns:
A result object that contains data about the run, as well as a method to
stream events.
"""
runner = DEFAULT_AGENT_RUNNER
return runner.run_streamed(
starting_agent,
input,
context=context,
max_turns=max_turns,
hooks=hooks,
run_config=run_config,
error_handlers=error_handlers,
previous_response_id=previous_response_id,
auto_previous_response_id=auto_previous_response_id,
conversation_id=conversation_id,
session=session,
)
class AgentRunner:
"""
WARNING: this class is experimental and not part of the public API
It should not be used directly or subclassed.
"""
async def run(
self,
starting_agent: Agent[TContext],
input: str | list[TResponseInputItem] | RunState[TContext],
**kwargs: Unpack[RunOptions[TContext]],
) -> RunResult:
context = kwargs.get("context")
max_turns = kwargs.get("max_turns", DEFAULT_MAX_TURNS)
hooks = cast(RunHooks[TContext], validate_run_hooks(kwargs.get("hooks")))
run_config = kwargs.get("run_config")
error_handlers = kwargs.get("error_handlers")
previous_response_id = kwargs.get("previous_response_id")
auto_previous_response_id = kwargs.get("auto_previous_response_id", False)
conversation_id = kwargs.get("conversation_id")
session = kwargs.get("session")
if run_config is None:
run_config = RunConfig()
is_resumed_state = isinstance(input, RunState)
run_state: RunState[TContext] | None = None
starting_input = input if not is_resumed_state else None
original_user_input: str | list[TResponseInputItem] | None = None
session_input_items_for_persistence: list[TResponseInputItem] | None = (
[] if (session is not None and is_resumed_state) else None
)
# Track the most recent input batch we persisted so conversation-lock retries can rewind
# exactly those items (and not the full history).
last_saved_input_snapshot_for_rewind: list[TResponseInputItem] | None = None
if is_resumed_state:
run_state = cast(RunState[TContext], input)
(
conversation_id,
previous_response_id,
auto_previous_response_id,
) = apply_resumed_conversation_settings(
run_state=run_state,
conversation_id=conversation_id,
previous_response_id=previous_response_id,
auto_previous_response_id=auto_previous_response_id,
)
validate_session_conversation_settings(
session,
conversation_id=conversation_id,
previous_response_id=previous_response_id,
auto_previous_response_id=auto_previous_response_id,
)
starting_input = run_state._original_input
original_user_input = copy_input_items(run_state._original_input)
prepared_input = normalize_resumed_input(original_user_input)
context_wrapper = resolve_resumed_context(
run_state=run_state,
context=context,
)
context = context_wrapper.context
max_turns = run_state._max_turns
else:
raw_input = cast(Union[str, list[TResponseInputItem]], input)
original_user_input = raw_input
validate_session_conversation_settings(
session,
conversation_id=conversation_id,
previous_response_id=previous_response_id,
auto_previous_response_id=auto_previous_response_id,
)
server_manages_conversation = (
conversation_id is not None
or previous_response_id is not None
or auto_previous_response_id
)
if server_manages_conversation:
prepared_input, _ = await prepare_input_with_session(
raw_input,
session,
run_config.session_input_callback,
run_config.session_settings,
include_history_in_prepared_input=False,
preserve_dropped_new_items=True,
)
original_input_for_state = raw_input
session_input_items_for_persistence = []
else:
(
prepared_input,
session_input_items_for_persistence,
) = await prepare_input_with_session(
raw_input,
session,
run_config.session_input_callback,
run_config.session_settings,
)
original_input_for_state = prepared_input
resolved_reasoning_item_id_policy: ReasoningItemIdPolicy | None = (
run_config.reasoning_item_id_policy
if run_config.reasoning_item_id_policy is not None
else (run_state._reasoning_item_id_policy if run_state is not None else None)
)
if run_state is not None:
run_state._reasoning_item_id_policy = resolved_reasoning_item_id_policy
# Check whether to enable OpenAI server-managed conversation
if (
conversation_id is not None
or previous_response_id is not None
or auto_previous_response_id
):
server_conversation_tracker = OpenAIServerConversationTracker(
conversation_id=conversation_id,
previous_response_id=previous_response_id,
auto_previous_response_id=auto_previous_response_id,
reasoning_item_id_policy=resolved_reasoning_item_id_policy,
)
else:
server_conversation_tracker = None
session_persistence_enabled = session is not None and server_conversation_tracker is None
if server_conversation_tracker is not None and is_resumed_state and run_state is not None:
session_input_items: list[TResponseInputItem] | None = None
if session is not None:
try:
session_input_items = await session.get_items()
except Exception:
session_input_items = None
server_conversation_tracker.hydrate_from_state(
original_input=run_state._original_input,
generated_items=run_state._generated_items,
model_responses=run_state._model_responses,
session_items=session_input_items,
)
tool_use_tracker = AgentToolUseTracker()
if is_resumed_state and run_state is not None:
hydrate_tool_use_tracker(tool_use_tracker, run_state, starting_agent)
(
trace_workflow_name,
trace_id,
trace_group_id,
trace_metadata,
trace_config,
) = resolve_trace_settings(run_state=run_state, run_config=run_config)
with TraceCtxManager(
workflow_name=trace_workflow_name,
trace_id=trace_id,
group_id=trace_group_id,
metadata=trace_metadata,
tracing=trace_config,
disabled=run_config.tracing_disabled,
trace_state=run_state._trace_state if run_state is not None else None,
reattach_resumed_trace=is_resumed_state,
):
if is_resumed_state and run_state is not None:
run_state.set_trace(get_current_trace())
current_turn = run_state._current_turn
raw_original_input = run_state._original_input
original_input = normalize_resumed_input(raw_original_input)
generated_items = run_state._generated_items
session_items = list(run_state._session_items)
model_responses = run_state._model_responses
# Cast to the correct type since we know this is TContext
context_wrapper = cast(RunContextWrapper[TContext], run_state._context)
else:
current_turn = 0
original_input = copy_input_items(original_input_for_state)
generated_items = []
session_items = []
model_responses = []
context_wrapper = ensure_context_wrapper(context)
set_agent_tool_state_scope(context_wrapper, None)
run_state = RunState(
context=context_wrapper,
original_input=original_input,
starting_agent=starting_agent,
max_turns=max_turns,
conversation_id=conversation_id,
previous_response_id=previous_response_id,
auto_previous_response_id=auto_previous_response_id,
)
run_state._reasoning_item_id_policy = resolved_reasoning_item_id_policy
run_state.set_trace(get_current_trace())
def _with_reasoning_item_id_policy(result: RunResult) -> RunResult:
result._reasoning_item_id_policy = resolved_reasoning_item_id_policy
if run_state is not None:
run_state._reasoning_item_id_policy = resolved_reasoning_item_id_policy
return result
pending_server_items: list[RunItem] | None = None
input_guardrail_results: list[InputGuardrailResult] = (
list(run_state._input_guardrail_results) if run_state is not None else []
)
tool_input_guardrail_results: list[ToolInputGuardrailResult] = (
list(getattr(run_state, "_tool_input_guardrail_results", []))
if run_state is not None
else []
)
tool_output_guardrail_results: list[ToolOutputGuardrailResult] = (
list(getattr(run_state, "_tool_output_guardrail_results", []))
if run_state is not None
else []
)
current_span: Span[AgentSpanData] | None = None
if is_resumed_state and run_state is not None and run_state._current_agent is not None:
current_agent = run_state._current_agent
else:
current_agent = starting_agent
should_run_agent_start_hooks = True
store_setting = current_agent.model_settings.resolve(run_config.model_settings).store
if (
not is_resumed_state
and session_persistence_enabled
and original_user_input is not None
and session_input_items_for_persistence is None
):
session_input_items_for_persistence = ItemHelpers.input_to_new_input_list(
original_user_input
)
if session_persistence_enabled and session_input_items_for_persistence:
# Capture the exact input saved so it can be rewound on conversation lock retries.
last_saved_input_snapshot_for_rewind = list(session_input_items_for_persistence)
await save_result_to_session(
session,
session_input_items_for_persistence,
[],
run_state,
store=store_setting,
)
session_input_items_for_persistence = []
try:
while True:
resuming_turn = is_resumed_state
normalized_starting_input: str | list[TResponseInputItem] = (
starting_input
if starting_input is not None and not isinstance(starting_input, RunState)
else ""
)
store_setting = current_agent.model_settings.resolve(
run_config.model_settings
).store
if run_state is not None and run_state._current_step is not None:
if isinstance(run_state._current_step, NextStepInterruption):
logger.debug("Continuing from interruption")
if (
not run_state._model_responses
or not run_state._last_processed_response
):
raise UserError("No model response found in previous state")
turn_result = await resolve_interrupted_turn(
agent=current_agent,
original_input=original_input,
original_pre_step_items=generated_items,
new_response=run_state._model_responses[-1],
processed_response=run_state._last_processed_response,
hooks=hooks,
context_wrapper=context_wrapper,
run_config=run_config,
run_state=run_state,
)
if run_state._last_processed_response is not None:
tool_use_tracker.record_processed_response(
current_agent,
run_state._last_processed_response,
)
original_input = turn_result.original_input
generated_items, turn_session_items = resumed_turn_items(turn_result)
session_items.extend(turn_session_items)
if run_state is not None:
update_run_state_after_resume(
run_state,
turn_result=turn_result,
generated_items=generated_items,
session_items=session_items,
)
if (
session_persistence_enabled
and turn_result.new_step_items
and run_state is not None
):
run_state._current_turn_persisted_item_count = (
await save_resumed_turn_items(
session=session,
items=turn_session_items,
persisted_count=(
run_state._current_turn_persisted_item_count
),
response_id=turn_result.model_response.response_id,
reasoning_item_id_policy=(
run_state._reasoning_item_id_policy
),
store=store_setting,
)
)
# After the resumed turn, treat subsequent turns as fresh so
# counters and input saving behave normally.
is_resumed_state = False
if isinstance(turn_result.next_step, NextStepInterruption):
interruption_result_input: str | list[TResponseInputItem] = (
original_input
)
append_model_response_if_new(
model_responses, turn_result.model_response
)
processed_response_for_state = resolve_processed_response(
run_state=run_state,
processed_response=turn_result.processed_response,
)
if run_state is not None:
update_run_state_for_interruption(
run_state=run_state,
model_responses=model_responses,
processed_response=processed_response_for_state,
generated_items=generated_items,
session_items=session_items,
current_turn=current_turn,
next_step=turn_result.next_step,
)
result = build_interruption_result(
result_input=interruption_result_input,
session_items=session_items,
model_responses=model_responses,
current_agent=current_agent,
input_guardrail_results=input_guardrail_results,
tool_input_guardrail_results=(
turn_result.tool_input_guardrail_results
),
tool_output_guardrail_results=(
turn_result.tool_output_guardrail_results
),
context_wrapper=context_wrapper,
interruptions=approvals_from_step(turn_result.next_step),
processed_response=processed_response_for_state,
tool_use_tracker=tool_use_tracker,
max_turns=max_turns,
current_turn=current_turn,
generated_items=generated_items,
run_state=run_state,
original_input=original_input,
)
return finalize_conversation_tracking(
_with_reasoning_item_id_policy(result),
server_conversation_tracker=server_conversation_tracker,
run_state=run_state,
)
if isinstance(turn_result.next_step, NextStepRunAgain):
continue
append_model_response_if_new(
model_responses, turn_result.model_response
)
tool_input_guardrail_results.extend(
turn_result.tool_input_guardrail_results
)
tool_output_guardrail_results.extend(
turn_result.tool_output_guardrail_results
)
if isinstance(turn_result.next_step, NextStepFinalOutput):
output_guardrail_results = await run_output_guardrails(
current_agent.output_guardrails
+ (run_config.output_guardrails or []),
current_agent,
turn_result.next_step.output,
context_wrapper,
)
current_step = getattr(run_state, "_current_step", None)
approvals_from_state = approvals_from_step(current_step)
result = RunResult(
input=turn_result.original_input,
new_items=session_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,
tool_input_guardrail_results=tool_input_guardrail_results,
tool_output_guardrail_results=tool_output_guardrail_results,
context_wrapper=context_wrapper,
interruptions=approvals_from_state,
_tool_use_tracker_snapshot=serialize_tool_use_tracker(
tool_use_tracker
),
max_turns=max_turns,
)
result._current_turn = current_turn
result._model_input_items = list(generated_items)
if run_state is not None:
result._trace_state = run_state._trace_state
if session_persistence_enabled:
input_items_for_save_1: list[TResponseInputItem] = (
session_input_items_for_persistence
if session_input_items_for_persistence is not None
else []
)
await save_result_to_session(
session,
input_items_for_save_1,
session_items_for_turn(turn_result),
run_state,
response_id=turn_result.model_response.response_id,
store=store_setting,
)
result._original_input = copy_input_items(original_input)
return finalize_conversation_tracking(
_with_reasoning_item_id_policy(result),
server_conversation_tracker=server_conversation_tracker,
run_state=run_state,
)
elif isinstance(turn_result.next_step, NextStepHandoff):
current_agent = cast(
Agent[TContext], turn_result.next_step.new_agent
)
if run_state is not None:
run_state._current_agent = current_agent
starting_input = turn_result.original_input
original_input = turn_result.original_input
if current_span is not None:
current_span.finish(reset_current=True)
current_span = None
should_run_agent_start_hooks = True
continue
continue
if run_state is not None:
if run_state._current_step is None:
run_state._current_step = NextStepRunAgain() # type: ignore[assignment]
all_tools = await get_all_tools(current_agent, context_wrapper)
await initialize_computer_tools(
tools=all_tools, context_wrapper=context_wrapper
)
if current_span is None:
handoff_names = [
h.agent_name for h in await get_handoffs(current_agent, context_wrapper)
]
if output_schema := get_output_schema(current_agent):
output_type_name = output_schema.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)
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},
),
)
max_turns_error = MaxTurnsExceeded(f"Max turns ({max_turns}) exceeded")
run_error_data = build_run_error_data(
input=original_input,
new_items=session_items,
raw_responses=model_responses,
last_agent=current_agent,
reasoning_item_id_policy=resolved_reasoning_item_id_policy,
)
handler_result = await resolve_run_error_handler_result(
error_handlers=error_handlers,
error=max_turns_error,
context_wrapper=context_wrapper,
run_data=run_error_data,
)
if handler_result is None:
raise max_turns_error
validated_output = validate_handler_final_output(
current_agent, handler_result.final_output
)
output_text = format_final_output_text(current_agent, validated_output)
synthesized_item = create_message_output_item(current_agent, output_text)
include_in_history = handler_result.include_in_history
if include_in_history:
generated_items.append(synthesized_item)
session_items.append(synthesized_item)
await run_final_output_hooks(
current_agent,
hooks,
context_wrapper,
validated_output,
)
output_guardrail_results = await run_output_guardrails(
current_agent.output_guardrails + (run_config.output_guardrails or []),
current_agent,
validated_output,
context_wrapper,
)
current_step = getattr(run_state, "_current_step", None)
approvals_from_state = approvals_from_step(current_step)
result = RunResult(
input=original_input,
new_items=session_items,
raw_responses=model_responses,
final_output=validated_output,
_last_agent=current_agent,
input_guardrail_results=input_guardrail_results,
output_guardrail_results=output_guardrail_results,
tool_input_guardrail_results=tool_input_guardrail_results,
tool_output_guardrail_results=tool_output_guardrail_results,
context_wrapper=context_wrapper,
interruptions=approvals_from_state,
_tool_use_tracker_snapshot=serialize_tool_use_tracker(tool_use_tracker),
max_turns=max_turns,
)
result._current_turn = max_turns
result._model_input_items = list(generated_items)
if run_state is not None:
result._trace_state = run_state._trace_state
if session_persistence_enabled and include_in_history:
handler_input_items_for_save: list[TResponseInputItem] = (
session_input_items_for_persistence
if session_input_items_for_persistence is not None
else []
)
await save_result_to_session(
session,
handler_input_items_for_save,
[synthesized_item],
run_state,
response_id=None,
store=store_setting,
)
result._original_input = copy_input_items(original_input)
return finalize_conversation_tracking(
_with_reasoning_item_id_policy(result),
server_conversation_tracker=server_conversation_tracker,
run_state=run_state,
)
if run_state is not None and not resuming_turn:
run_state._current_turn_persisted_item_count = 0
logger.debug("Running agent %s (turn %s)", current_agent.name, current_turn)
if session_persistence_enabled:
try:
last_saved_input_snapshot_for_rewind = (
ItemHelpers.input_to_new_input_list(original_input)
)
except Exception:
last_saved_input_snapshot_for_rewind = None
items_for_model = (
pending_server_items
if server_conversation_tracker is not None and pending_server_items
else generated_items
)
if current_turn <= 1:
all_input_guardrails = starting_agent.input_guardrails + (
run_config.input_guardrails or []
)
sequential_guardrails = [
g for g in all_input_guardrails if not g.run_in_parallel
]
parallel_guardrails = [g for g in all_input_guardrails if g.run_in_parallel]
try:
sequential_results = []
if sequential_guardrails:
sequential_results = await run_input_guardrails(
starting_agent,
sequential_guardrails,
copy_input_items(prepared_input),
context_wrapper,
)
except InputGuardrailTripwireTriggered:
session_input_items_for_persistence = (
await persist_session_items_for_guardrail_trip(
session,
server_conversation_tracker,
session_input_items_for_persistence,
original_user_input,
run_state,
store=store_setting,
)
)