from __future__ import annotations import asyncio import copy import os import time import traceback from typing import Callable from litellm.exceptions import ( # noqa APIConnectionError, APIError, AuthenticationError, BadRequestError, ContentPolicyViolationError, ContextWindowExceededError, InternalServerError, NotFoundError, OpenAIError, RateLimitError, ServiceUnavailableError, Timeout, ) from openhands.controller.agent import Agent from openhands.controller.replay import ReplayManager from openhands.controller.state.state import State from openhands.controller.state.state_tracker import StateTracker from openhands.controller.stuck import StuckDetector from openhands.core.config import AgentConfig, LLMConfig from openhands.core.exceptions import ( AgentStuckInLoopError, FunctionCallNotExistsError, FunctionCallValidationError, LLMContextWindowExceedError, LLMMalformedActionError, LLMNoActionError, LLMResponseError, ) from openhands.core.logger import LOG_ALL_EVENTS from openhands.core.logger import openhands_logger as logger from openhands.core.schema import AgentState from openhands.events import ( EventSource, EventStream, EventStreamSubscriber, RecallType, ) from openhands.events.action import ( Action, ActionConfirmationStatus, AgentDelegateAction, AgentFinishAction, AgentRejectAction, ChangeAgentStateAction, CmdRunAction, IPythonRunCellAction, MessageAction, NullAction, SystemMessageAction, ) from openhands.events.action.agent import ( CondensationAction, CondensationRequestAction, RecallAction, ) from openhands.events.event import Event from openhands.events.observation import ( AgentDelegateObservation, AgentStateChangedObservation, ErrorObservation, NullObservation, Observation, ) from openhands.events.serialization.event import truncate_content from openhands.llm.llm import LLM from openhands.llm.metrics import Metrics from openhands.runtime.runtime_status import RuntimeStatus from openhands.storage.files import FileStore # note: RESUME is only available on web GUI TRAFFIC_CONTROL_REMINDER = ( "Please click on resume button if you'd like to continue, or start a new task." ) ERROR_ACTION_NOT_EXECUTED_STOPPED_ID = 'AGENT_ERROR$ERROR_ACTION_NOT_EXECUTED_STOPPED' ERROR_ACTION_NOT_EXECUTED_ERROR_ID = 'AGENT_ERROR$ERROR_ACTION_NOT_EXECUTED_ERROR' ERROR_ACTION_NOT_EXECUTED_STOPPED = ( 'Stop button pressed. The action has not been executed.' ) ERROR_ACTION_NOT_EXECUTED_ERROR = 'The action has not been executed due to a runtime error. The runtime system may have crashed and restarted due to resource constraints. Any previously established system state, dependencies, or environment variables may have been lost.' class AgentController: id: str agent: Agent max_iterations: int event_stream: EventStream state: State confirmation_mode: bool agent_to_llm_config: dict[str, LLMConfig] agent_configs: dict[str, AgentConfig] parent: 'AgentController | None' = None delegate: 'AgentController | None' = None _pending_action_info: tuple[Action, float] | None = None # (action, timestamp) _closed: bool = False _cached_first_user_message: MessageAction | None = None def __init__( self, agent: Agent, event_stream: EventStream, iteration_delta: int, budget_per_task_delta: float | None = None, agent_to_llm_config: dict[str, LLMConfig] | None = None, agent_configs: dict[str, AgentConfig] | None = None, sid: str | None = None, file_store: FileStore | None = None, user_id: str | None = None, confirmation_mode: bool = False, initial_state: State | None = None, is_delegate: bool = False, headless_mode: bool = True, status_callback: Callable | None = None, replay_events: list[Event] | None = None, ): """Initializes a new instance of the AgentController class. Args: agent: The agent instance to control. event_stream: The event stream to publish events to. max_iterations: The maximum number of iterations the agent can run. max_budget_per_task: The maximum budget (in USD) allowed per task, beyond which the agent will stop. agent_to_llm_config: A dictionary mapping agent names to LLM configurations in the case that we delegate to a different agent. agent_configs: A dictionary mapping agent names to agent configurations in the case that we delegate to a different agent. sid: The session ID of the agent. confirmation_mode: Whether to enable confirmation mode for agent actions. initial_state: The initial state of the controller. is_delegate: Whether this controller is a delegate. headless_mode: Whether the agent is run in headless mode. status_callback: Optional callback function to handle status updates. replay_events: A list of logs to replay. """ self.id = sid or event_stream.sid self.user_id = user_id self.file_store = file_store self.agent = agent self.headless_mode = headless_mode self.is_delegate = is_delegate # the event stream must be set before maybe subscribing to it self.event_stream = event_stream # subscribe to the event stream if this is not a delegate if not self.is_delegate: self.event_stream.subscribe( EventStreamSubscriber.AGENT_CONTROLLER, self.on_event, self.id ) self.state_tracker = StateTracker(sid, file_store, user_id) # state from the previous session, state from a parent agent, or a fresh state self.set_initial_state( state=initial_state, max_iterations=iteration_delta, max_budget_per_task=budget_per_task_delta, confirmation_mode=confirmation_mode, ) self.state = self.state_tracker.state # TODO: share between manager and controller for backward compatability; we should ideally move all state related logic to the state manager self.agent_to_llm_config = agent_to_llm_config if agent_to_llm_config else {} self.agent_configs = agent_configs if agent_configs else {} self._initial_max_iterations = iteration_delta self._initial_max_budget_per_task = budget_per_task_delta # stuck helper self._stuck_detector = StuckDetector(self.state) self.status_callback = status_callback # replay-related self._replay_manager = ReplayManager(replay_events) # Add the system message to the event stream self._add_system_message() def _add_system_message(self): for event in self.event_stream.search_events(start_id=self.state.start_id): if isinstance(event, MessageAction) and event.source == EventSource.USER: # FIXME: Remove this after 6/1/2025 # Do not try to add a system message if we first run into # a user message -- this means the eventstream exits before # SystemMessageAction is introduced. # We expect *agent* to handle this case gracefully. return if isinstance(event, SystemMessageAction): # Do not try to add the system message if it already exists return # Add the system message to the event stream # This should be done for all agents, including delegates system_message = self.agent.get_system_message() if system_message and system_message.content: preview = ( system_message.content[:50] + '...' if len(system_message.content) > 50 else system_message.content ) logger.debug(f'System message: {preview}') self.event_stream.add_event(system_message, EventSource.AGENT) async def close(self, set_stop_state: bool = True) -> None: """Closes the agent controller, canceling any ongoing tasks and unsubscribing from the event stream. Note that it's fairly important that this closes properly, otherwise the state is incomplete. """ if set_stop_state: await self.set_agent_state_to(AgentState.STOPPED) self.state_tracker.close(self.event_stream) # unsubscribe from the event stream # only the root parent controller subscribes to the event stream if not self.is_delegate: self.event_stream.unsubscribe( EventStreamSubscriber.AGENT_CONTROLLER, self.id ) self._closed = True def log(self, level: str, message: str, extra: dict | None = None) -> None: """Logs a message to the agent controller's logger. Args: level (str): The logging level to use (e.g., 'info', 'debug', 'error'). message (str): The message to log. extra (dict | None, optional): Additional fields to log. Includes session_id by default. """ message = f'[Agent Controller {self.id}] {message}' if extra is None: extra = {} extra_merged = {'session_id': self.id, **extra} getattr(logger, level)(message, extra=extra_merged, stacklevel=2) async def _react_to_exception( self, e: Exception, ) -> None: """React to an exception by setting the agent state to error and sending a status message.""" # Store the error reason before setting the agent state self.state.last_error = f'{type(e).__name__}: {str(e)}' if self.status_callback is not None: runtime_status = RuntimeStatus.ERROR if isinstance(e, AuthenticationError): runtime_status = RuntimeStatus.ERROR_LLM_AUTHENTICATION self.state.last_error = runtime_status.value elif isinstance( e, ( ServiceUnavailableError, APIConnectionError, APIError, ), ): runtime_status = RuntimeStatus.ERROR_LLM_SERVICE_UNAVAILABLE self.state.last_error = runtime_status.value elif isinstance(e, InternalServerError): runtime_status = RuntimeStatus.ERROR_LLM_INTERNAL_SERVER_ERROR self.state.last_error = runtime_status.value elif isinstance(e, BadRequestError) and 'ExceededBudget' in str(e): runtime_status = RuntimeStatus.ERROR_LLM_OUT_OF_CREDITS self.state.last_error = runtime_status.value elif isinstance(e, ContentPolicyViolationError) or ( isinstance(e, BadRequestError) and 'ContentPolicyViolationError' in str(e) ): runtime_status = RuntimeStatus.ERROR_LLM_CONTENT_POLICY_VIOLATION self.state.last_error = runtime_status.value elif isinstance(e, RateLimitError): # Check if this is the final retry attempt if ( hasattr(e, 'retry_attempt') and hasattr(e, 'max_retries') and e.retry_attempt >= e.max_retries ): # All retries exhausted, set to ERROR state with a special message self.state.last_error = ( RuntimeStatus.AGENT_RATE_LIMITED_STOPPED_MESSAGE.value ) await self.set_agent_state_to(AgentState.ERROR) else: # Still retrying, set to RATE_LIMITED state await self.set_agent_state_to(AgentState.RATE_LIMITED) return self.status_callback('error', runtime_status, self.state.last_error) # Set the agent state to ERROR after storing the reason await self.set_agent_state_to(AgentState.ERROR) def step(self) -> None: asyncio.create_task(self._step_with_exception_handling()) async def _step_with_exception_handling(self) -> None: try: await self._step() except Exception as e: self.log( 'error', f'Error while running the agent (session ID: {self.id}): {e}. ' f'Traceback: {traceback.format_exc()}', ) reported = RuntimeError( f'There was an unexpected error while running the agent: {e.__class__.__name__}. You can refresh the page or ask the agent to try again.' ) if ( isinstance(e, Timeout) or isinstance(e, APIError) or isinstance(e, BadRequestError) or isinstance(e, NotFoundError) or isinstance(e, InternalServerError) or isinstance(e, AuthenticationError) or isinstance(e, RateLimitError) or isinstance(e, ContentPolicyViolationError) or isinstance(e, LLMContextWindowExceedError) ): reported = e else: self.log( 'warning', f'Unknown exception type while running the agent: {type(e).__name__}.', ) await self._react_to_exception(reported) def should_step(self, event: Event) -> bool: """Whether the agent should take a step based on an event. In general, the agent should take a step if it receives a message from the user, or observes something in the environment (after acting). """ # it might be the delegate's day in the sun if self.delegate is not None: return False if isinstance(event, Action): if isinstance(event, MessageAction) and event.source == EventSource.USER: return True if ( isinstance(event, MessageAction) and self.get_agent_state() != AgentState.AWAITING_USER_INPUT ): # TODO: this is fragile, but how else to check if eligible? return True if isinstance(event, AgentDelegateAction): return True if isinstance(event, CondensationAction): return True if isinstance(event, CondensationRequestAction): return True return False if isinstance(event, Observation): if ( isinstance(event, NullObservation) and event.cause is not None and event.cause > 0 # NullObservation has cause > 0 (RecallAction), not 0 (user message) ): return True if isinstance(event, AgentStateChangedObservation) or isinstance( event, NullObservation ): return False return True return False def on_event(self, event: Event) -> None: """Callback from the event stream. Notifies the controller of incoming events. Args: event (Event): The incoming event to process. """ # If we have a delegate that is not finished or errored, forward events to it if self.delegate is not None: delegate_state = self.delegate.get_agent_state() if ( delegate_state not in ( AgentState.FINISHED, AgentState.ERROR, AgentState.REJECTED, ) or 'RuntimeError: Agent reached maximum iteration.' in self.delegate.state.last_error or 'RuntimeError:Agent reached maximum budget for conversation' in self.delegate.state.last_error ): # Forward the event to delegate and skip parent processing asyncio.get_event_loop().run_until_complete( self.delegate._on_event(event) ) return else: # delegate is done or errored, so end it self.end_delegate() return # continue parent processing only if there's no active delegate asyncio.get_event_loop().run_until_complete(self._on_event(event)) async def _on_event(self, event: Event) -> None: if hasattr(event, 'hidden') and event.hidden: return self.state_tracker.add_history(event) if isinstance(event, Action): await self._handle_action(event) elif isinstance(event, Observation): await self._handle_observation(event) should_step = self.should_step(event) if should_step: self.log( 'debug', f'Stepping agent after event: {type(event).__name__}', extra={'msg_type': 'STEPPING_AGENT'}, ) await self._step_with_exception_handling() elif isinstance(event, MessageAction) and event.source == EventSource.USER: # If we received a user message but aren't stepping, log why self.log( 'warning', f'Not stepping agent after user message. Current state: {self.get_agent_state()}', extra={'msg_type': 'NOT_STEPPING_AFTER_USER_MESSAGE'}, ) async def _handle_action(self, action: Action) -> None: """Handles an Action from the agent or delegate.""" if isinstance(action, ChangeAgentStateAction): await self.set_agent_state_to(action.agent_state) # type: ignore elif isinstance(action, MessageAction): await self._handle_message_action(action) elif isinstance(action, AgentDelegateAction): await self.start_delegate(action) assert self.delegate is not None # Post a MessageAction with the task for the delegate if 'task' in action.inputs: self.event_stream.add_event( MessageAction(content='TASK: ' + action.inputs['task']), EventSource.USER, ) await self.delegate.set_agent_state_to(AgentState.RUNNING) return elif isinstance(action, AgentFinishAction): self.state.outputs = action.outputs await self.set_agent_state_to(AgentState.FINISHED) elif isinstance(action, AgentRejectAction): self.state.outputs = action.outputs await self.set_agent_state_to(AgentState.REJECTED) async def _handle_observation(self, observation: Observation) -> None: """Handles observation from the event stream. Args: observation (observation): The observation to handle. """ observation_to_print = copy.deepcopy(observation) if len(observation_to_print.content) > self.agent.llm.config.max_message_chars: observation_to_print.content = truncate_content( observation_to_print.content, self.agent.llm.config.max_message_chars ) # Use info level if LOG_ALL_EVENTS is set log_level = 'info' if os.getenv('LOG_ALL_EVENTS') in ('true', '1') else 'debug' self.log( log_level, str(observation_to_print), extra={'msg_type': 'OBSERVATION'} ) # TODO: these metrics come from the draft editor, and they get accumulated into controller's state metrics and the agent's llm metrics # In the future, we should have a more principled way to sharing metrics across all LLM instances for a given conversation if observation.llm_metrics is not None: self.state_tracker.merge_metrics(observation.llm_metrics) # this happens for runnable actions and microagent actions if self._pending_action and self._pending_action.id == observation.cause: if self.state.agent_state == AgentState.AWAITING_USER_CONFIRMATION: return self._pending_action = None if self.state.agent_state == AgentState.USER_CONFIRMED: await self.set_agent_state_to(AgentState.RUNNING) if self.state.agent_state == AgentState.USER_REJECTED: await self.set_agent_state_to(AgentState.AWAITING_USER_INPUT) return async def _handle_message_action(self, action: MessageAction) -> None: """Handles message actions from the event stream. Args: action (MessageAction): The message action to handle. """ if action.source == EventSource.USER: # Use info level if LOG_ALL_EVENTS is set log_level = ( 'info' if os.getenv('LOG_ALL_EVENTS') in ('true', '1') else 'debug' ) self.log( log_level, str(action), extra={'msg_type': 'ACTION', 'event_source': EventSource.USER}, ) # if this is the first user message for this agent, matters for the microagent info type first_user_message = self._first_user_message() is_first_user_message = ( action.id == first_user_message.id if first_user_message else False ) recall_type = ( RecallType.WORKSPACE_CONTEXT if is_first_user_message else RecallType.KNOWLEDGE ) recall_action = RecallAction(query=action.content, recall_type=recall_type) self._pending_action = recall_action # this is source=USER because the user message is the trigger for the microagent retrieval self.event_stream.add_event(recall_action, EventSource.USER) if self.get_agent_state() != AgentState.RUNNING: await self.set_agent_state_to(AgentState.RUNNING) elif action.source == EventSource.AGENT: # If the agent is waiting for a response, set the appropriate state if action.wait_for_response: await self.set_agent_state_to(AgentState.AWAITING_USER_INPUT) def _reset(self) -> None: """Resets the agent controller.""" # Runnable actions need an Observation # make sure there is an Observation with the tool call metadata to be recognized by the agent # otherwise the pending action is found in history, but it's incomplete without an obs with tool result if self._pending_action and hasattr(self._pending_action, 'tool_call_metadata'): # find out if there already is an observation with the same tool call metadata found_observation = False for event in self.state.history: if ( isinstance(event, Observation) and event.tool_call_metadata == self._pending_action.tool_call_metadata ): found_observation = True break # make a new ErrorObservation with the tool call metadata if not found_observation: # Use different messages and IDs based on whether the agent was stopped by user or due to error if self.state.agent_state == AgentState.STOPPED: error_content = ERROR_ACTION_NOT_EXECUTED_STOPPED error_id = ERROR_ACTION_NOT_EXECUTED_STOPPED_ID else: # AgentState.ERROR error_content = ERROR_ACTION_NOT_EXECUTED_ERROR error_id = ERROR_ACTION_NOT_EXECUTED_ERROR_ID obs = ErrorObservation( content=error_content, error_id=error_id, ) obs.tool_call_metadata = self._pending_action.tool_call_metadata obs._cause = self._pending_action.id # type: ignore[attr-defined] self.event_stream.add_event(obs, EventSource.AGENT) # NOTE: RecallActions don't need an ErrorObservation upon reset, as long as they have no tool calls # reset the pending action, this will be called when the agent is STOPPED or ERROR self._pending_action = None self.agent.reset() async def set_agent_state_to(self, new_state: AgentState) -> None: """Updates the agent's state and handles side effects. Can emit events to the event stream. Args: new_state (AgentState): The new state to set for the agent. """ self.log( 'info', f'Setting agent({self.agent.name}) state from {self.state.agent_state} to {new_state}', ) if new_state == self.state.agent_state: return # Store old state for control limits check old_state = self.state.agent_state # Update agent state BEFORE calling _reset() so _reset() sees the correct state self.state.agent_state = new_state if new_state in (AgentState.STOPPED, AgentState.ERROR): self._reset() # User is allowing to check control limits and expand them if applicable if old_state == AgentState.ERROR and new_state == AgentState.RUNNING: self.state_tracker.maybe_increase_control_flags_limits(self.headless_mode) if self._pending_action is not None and ( new_state in (AgentState.USER_CONFIRMED, AgentState.USER_REJECTED) ): if hasattr(self._pending_action, 'thought'): self._pending_action.thought = '' # type: ignore[union-attr] if new_state == AgentState.USER_CONFIRMED: confirmation_state = ActionConfirmationStatus.CONFIRMED else: confirmation_state = ActionConfirmationStatus.REJECTED self._pending_action.confirmation_state = confirmation_state # type: ignore[attr-defined] self._pending_action._id = None # type: ignore[attr-defined] self.event_stream.add_event(self._pending_action, EventSource.AGENT) # Create observation with reason field if it's an error state reason = '' if new_state == AgentState.ERROR: reason = self.state.last_error self.event_stream.add_event( AgentStateChangedObservation('', self.state.agent_state, reason), EventSource.ENVIRONMENT, ) # Save state whenever agent state changes to ensure we don't lose state # in case of crashes or unexpected circumstances self.save_state() def get_agent_state(self) -> AgentState: """Returns the current state of the agent. Returns: AgentState: The current state of the agent. """ return self.state.agent_state async def start_delegate(self, action: AgentDelegateAction) -> None: """Start a delegate agent to handle a subtask. OpenHands is a multi-agentic system. A `task` is a conversation between OpenHands (the whole system) and the user, which might involve one or more inputs from the user. It starts with an initial input (typically a task statement) from the user, and ends with either an `AgentFinishAction` initiated by the agent, a stop initiated by the user, or an error. A `subtask` is a conversation between an agent and the user, or another agent. If a `task` is conducted by a single agent, then it's also a `subtask`. Otherwise, a `task` consists of multiple `subtasks`, each executed by one agent. Args: action (AgentDelegateAction): The action containing information about the delegate agent to start. """ agent_cls: type[Agent] = Agent.get_cls(action.agent) agent_config = self.agent_configs.get(action.agent, self.agent.config) llm_config = self.agent_to_llm_config.get(action.agent, self.agent.llm.config) # Make sure metrics are shared between parent and child for global accumulation llm = LLM( config=llm_config, retry_listener=self.agent.llm.retry_listener, metrics=self.state.metrics, ) delegate_agent = agent_cls(llm=llm, config=agent_config) # Take a snapshot of the current metrics before starting the delegate state = State( session_id=self.id.removesuffix('-delegate'), user_id=self.user_id, inputs=action.inputs or {}, iteration_flag=self.state.iteration_flag, budget_flag=self.state.budget_flag, delegate_level=self.state.delegate_level + 1, # global metrics should be shared between parent and child metrics=self.state.metrics, # start on top of the stream start_id=self.event_stream.get_latest_event_id() + 1, parent_metrics_snapshot=self.state_tracker.get_metrics_snapshot(), parent_iteration=self.state.iteration_flag.current_value, ) self.log( 'debug', f'start delegate, creating agent {delegate_agent.name} using LLM {llm}', ) # Create the delegate with is_delegate=True so it does NOT subscribe directly self.delegate = AgentController( sid=self.id + '-delegate', file_store=self.file_store, user_id=self.user_id, agent=delegate_agent, event_stream=self.event_stream, iteration_delta=self._initial_max_iterations, budget_per_task_delta=self._initial_max_budget_per_task, agent_to_llm_config=self.agent_to_llm_config, agent_configs=self.agent_configs, initial_state=state, is_delegate=True, headless_mode=self.headless_mode, ) def end_delegate(self) -> None: """Ends the currently active delegate (e.g., if it is finished or errored). so that this controller can resume normal operation. """ if self.delegate is None: return delegate_state = self.delegate.get_agent_state() # update iteration that is shared across agents self.state.iteration_flag.current_value = ( self.delegate.state.iteration_flag.current_value ) # Calculate delegate-specific metrics before closing the delegate delegate_metrics = self.state.get_local_metrics() logger.info(f'Local metrics for delegate: {delegate_metrics}') # close the delegate controller before adding new events asyncio.get_event_loop().run_until_complete(self.delegate.close()) if delegate_state in (AgentState.FINISHED, AgentState.REJECTED): # retrieve delegate result delegate_outputs = ( self.delegate.state.outputs if self.delegate.state else {} ) # prepare delegate result observation # TODO: replace this with AI-generated summary (#2395) # Filter out metrics from the formatted output to avoid clutter display_outputs = { k: v for k, v in delegate_outputs.items() if k != 'metrics' } formatted_output = ', '.join( f'{key}: {value}' for key, value in display_outputs.items() ) content = ( f'{self.delegate.agent.name} finishes task with {formatted_output}' ) else: # delegate state is ERROR # emit AgentDelegateObservation with error content delegate_outputs = ( self.delegate.state.outputs if self.delegate.state else {} ) content = ( f'{self.delegate.agent.name} encountered an error during execution.' ) content = f'Delegated agent finished with result:\n\n{content}' # emit the delegate result observation obs = AgentDelegateObservation(outputs=delegate_outputs, content=content) # associate the delegate action with the initiating tool call for event in reversed(self.state.history): if isinstance(event, AgentDelegateAction): delegate_action = event obs.tool_call_metadata = delegate_action.tool_call_metadata break self.event_stream.add_event(obs, EventSource.AGENT) # unset delegate so parent can resume normal handling self.delegate = None async def _step(self) -> None: """Executes a single step of the parent or delegate agent. Detects stuck agents and limits on the number of iterations and the task budget.""" if self.get_agent_state() != AgentState.RUNNING: self.log( 'debug', f'Agent not stepping because state is {self.get_agent_state()} (not RUNNING)', extra={'msg_type': 'STEP_BLOCKED_STATE'}, ) return if self._pending_action: action_id = getattr(self._pending_action, 'id', 'unknown') action_type = type(self._pending_action).__name__ self.log( 'debug', f'Agent not stepping because of pending action: {action_type} (id={action_id})', extra={'msg_type': 'STEP_BLOCKED_PENDING_ACTION'}, ) return self.log( 'debug', f'LEVEL {self.state.delegate_level} LOCAL STEP {self.state.get_local_step()} GLOBAL STEP {self.state.iteration_flag.current_value}', extra={'msg_type': 'STEP'}, ) # Ensure budget control flag is synchronized with the latest metrics. # In the future, we should centralized the use of one LLM object per conversation. # This will help us unify the cost for auto generating titles, running the condensor, etc. # Before many microservices will touh the same llm cost field, we should sync with the budget flag for the controller # and check that we haven't exceeded budget BEFORE executing an agent step. self.state_tracker.sync_budget_flag_with_metrics() if self._is_stuck(): await self._react_to_exception( AgentStuckInLoopError('Agent got stuck in a loop') ) return try: self.state_tracker.run_control_flags() except Exception as e: logger.warning('Control flag limits hit') await self._react_to_exception(e) return action: Action = NullAction() if self._replay_manager.should_replay(): # in replay mode, we don't let the agent to proceed # instead, we replay the action from the replay trajectory action = self._replay_manager.step() else: try: action = self.agent.step(self.state) if action is None: raise LLMNoActionError('No action was returned') action._source = EventSource.AGENT # type: ignore [attr-defined] except ( LLMMalformedActionError, LLMNoActionError, LLMResponseError, FunctionCallValidationError, FunctionCallNotExistsError, ) as e: self.event_stream.add_event( ErrorObservation( content=str(e), ), EventSource.AGENT, ) return except (ContextWindowExceededError, BadRequestError, OpenAIError) as e: # FIXME: this is a hack until a litellm fix is confirmed # Check if this is a nested context window error # We have to rely on string-matching because LiteLLM doesn't consistently # wrap the failure in a ContextWindowExceededError error_str = str(e).lower() if ( 'contextwindowexceedederror' in error_str or 'prompt is too long' in error_str or 'input length and `max_tokens` exceed context limit' in error_str or 'please reduce the length of either one' in error_str # For OpenRouter context window errors or ( 'sambanovaexception' in error_str and 'maximum context length' in error_str ) # For SambaNova context window errors - only match when both patterns are present or isinstance(e, ContextWindowExceededError) ): if self.agent.config.enable_history_truncation: self.event_stream.add_event( CondensationRequestAction(), EventSource.AGENT ) return else: raise LLMContextWindowExceedError() else: raise e if action.runnable: if self.state.confirmation_mode and ( type(action) is CmdRunAction or type(action) is IPythonRunCellAction ): action.confirmation_state = ( ActionConfirmationStatus.AWAITING_CONFIRMATION ) self._pending_action = action if not isinstance(action, NullAction): if ( hasattr(action, 'confirmation_state') and action.confirmation_state == ActionConfirmationStatus.AWAITING_CONFIRMATION ): await self.set_agent_state_to(AgentState.AWAITING_USER_CONFIRMATION) # Create and log metrics for frontend display self._prepare_metrics_for_frontend(action) self.event_stream.add_event(action, action._source) # type: ignore [attr-defined] log_level = 'info' if LOG_ALL_EVENTS else 'debug' self.log(log_level, str(action), extra={'msg_type': 'ACTION'}) @property def _pending_action(self) -> Action | None: """Get the current pending action with time tracking. Returns: Action | None: The current pending action, or None if there isn't one. """ if self._pending_action_info is None: return None action, timestamp = self._pending_action_info current_time = time.time() elapsed_time = current_time - timestamp # Log if the pending action has been active for a long time (but don't clear it) if elapsed_time > 60.0: # 1 minute - just for logging purposes action_id = getattr(action, 'id', 'unknown') action_type = type(action).__name__ self.log( 'info', f'Pending action active for {elapsed_time:.2f}s: {action_type} (id={action_id})', extra={'msg_type': 'PENDING_ACTION_TIMEOUT'}, ) return action @_pending_action.setter def _pending_action(self, action: Action | None) -> None: """Set or clear the pending action with timestamp and logging. Args: action: The action to set as pending, or None to clear. """ if action is None: if self._pending_action_info is not None: prev_action, timestamp = self._pending_action_info action_id = getattr(prev_action, 'id', 'unknown') action_type = type(prev_action).__name__ elapsed_time = time.time() - timestamp self.log( 'debug', f'Cleared pending action after {elapsed_time:.2f}s: {action_type} (id={action_id})', extra={'msg_type': 'PENDING_ACTION_CLEARED'}, ) self._pending_action_info = None else: action_id = getattr(action, 'id', 'unknown') action_type = type(action).__name__ self.log( 'debug', f'Set pending action: {action_type} (id={action_id})', extra={'msg_type': 'PENDING_ACTION_SET'}, ) self._pending_action_info = (action, time.time()) def get_state(self) -> State: """Returns the current running state object. Returns: State: The current state object. """ return self.state def set_initial_state( self, state: State | None, max_iterations: int, max_budget_per_task: float | None, confirmation_mode: bool = False, ): self.state_tracker.set_initial_state( self.id, self.agent, state, max_iterations, max_budget_per_task, confirmation_mode, ) # Always load from the event stream to avoid losing history self.state_tracker._init_history( self.event_stream, ) def get_trajectory(self, include_screenshots: bool = False) -> list[dict]: # state history could be partially hidden/truncated before controller is closed assert self._closed return self.state_tracker.get_trajectory(include_screenshots) def _is_stuck(self) -> bool: """Checks if the agent or its delegate is stuck in a loop. Returns: bool: True if the agent is stuck, False otherwise. """ # check if delegate stuck if self.delegate and self.delegate._is_stuck(): return True return self._stuck_detector.is_stuck(self.headless_mode) def _prepare_metrics_for_frontend(self, action: Action) -> None: """Create a minimal metrics object for frontend display and log it. To avoid performance issues with long conversations, we only keep: - accumulated_cost: The current total cost - accumulated_token_usage: Accumulated token statistics across all API calls - max_budget_per_task: The maximum budget allowed for the task This includes metrics from both the agent's LLM and the condenser's LLM if it exists. Args: action: The action to attach metrics to """ # Get metrics from agent LLM agent_metrics = self.state.metrics # Get metrics from condenser LLM if it exists condenser_metrics: Metrics | None = None if hasattr(self.agent, 'condenser') and hasattr(self.agent.condenser, 'llm'): condenser_metrics = self.agent.condenser.llm.metrics # Create a new minimal metrics object with just what the frontend needs metrics = Metrics(model_name=agent_metrics.model_name) # Set accumulated cost (sum of agent and condenser costs) metrics.accumulated_cost = agent_metrics.accumulated_cost if condenser_metrics: metrics.accumulated_cost += condenser_metrics.accumulated_cost # Add max_budget_per_task to metrics if self.state.budget_flag: metrics.max_budget_per_task = self.state.budget_flag.max_value # Set accumulated token usage (sum of agent and condenser token usage) # Use a deep copy to ensure we don't modify the original object metrics._accumulated_token_usage = ( agent_metrics.accumulated_token_usage.model_copy(deep=True) ) if condenser_metrics: metrics._accumulated_token_usage = ( metrics._accumulated_token_usage + condenser_metrics.accumulated_token_usage ) action.llm_metrics = metrics # Log the metrics information for debugging # Get the latest usage directly from the agent's metrics latest_usage = None if self.state.metrics.token_usages: latest_usage = self.state.metrics.token_usages[-1] accumulated_usage = self.state.metrics.accumulated_token_usage self.log( 'debug', f'Action metrics - accumulated_cost: {metrics.accumulated_cost}, max_budget: {metrics.max_budget_per_task}, ' f'latest tokens (prompt/completion/cache_read/cache_write): ' f'{latest_usage.prompt_tokens if latest_usage else 0}/' f'{latest_usage.completion_tokens if latest_usage else 0}/' f'{latest_usage.cache_read_tokens if latest_usage else 0}/' f'{latest_usage.cache_write_tokens if latest_usage else 0}, ' f'accumulated tokens (prompt/completion): ' f'{accumulated_usage.prompt_tokens}/' f'{accumulated_usage.completion_tokens}', extra={'msg_type': 'METRICS'}, ) def __repr__(self) -> str: pending_action_info = '' if ( hasattr(self, '_pending_action_info') and self._pending_action_info is not None ): action, timestamp = self._pending_action_info action_id = getattr(action, 'id', 'unknown') action_type = type(action).__name__ elapsed_time = time.time() - timestamp pending_action_info = ( f'{action_type}(id={action_id}, elapsed={elapsed_time:.2f}s)' ) return ( f'AgentController(id={getattr(self, "id", "")}, ' f'agent={getattr(self, "agent", "")!r}, ' f'event_stream={getattr(self, "event_stream", "")!r}, ' f'state={getattr(self, "state", "")!r}, ' f'delegate={getattr(self, "delegate", "")!r}, ' f'_pending_action={pending_action_info})' ) def _is_awaiting_observation(self) -> bool: events = self.event_stream.search_events(reverse=True) for event in events: if isinstance(event, AgentStateChangedObservation): result = event.agent_state == AgentState.RUNNING return result return False def _first_user_message( self, events: list[Event] | None = None ) -> MessageAction | None: """Get the first user message for this agent. For regular agents, this is the first user message from the beginning (start_id=0). For delegate agents, this is the first user message after the delegate's start_id. Args: events: Optional list of events to search through. If None, uses the event stream. Returns: MessageAction | None: The first user message, or None if no user message found """ # If events list is provided, search through it if events is not None: return next( ( e for e in events if isinstance(e, MessageAction) and e.source == EventSource.USER ), None, ) # Otherwise, use the original event stream logic with caching # Return cached message if any if self._cached_first_user_message is not None: return self._cached_first_user_message # Find the first user message self._cached_first_user_message = next( ( e for e in self.event_stream.search_events( start_id=self.state.start_id, ) if isinstance(e, MessageAction) and e.source == EventSource.USER ), None, ) return self._cached_first_user_message def save_state(self): self.state_tracker.save_state()