from __future__ import annotations import asyncio import copy import os import time import traceback from typing import Callable import litellm # noqa 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, TrafficControlState 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, RecallAction from openhands.events.event import Event from openhands.events.event_filter import EventFilter from openhands.events.observation import ( AgentDelegateObservation, AgentStateChangedObservation, ErrorObservation, NullObservation, Observation, ) from openhands.events.serialization.event import event_to_trajectory, truncate_content from openhands.llm.llm import LLM from openhands.llm.metrics import Metrics, TokenUsage # 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_ID = 'AGENT_ERROR$ERROR_ACTION_NOT_EXECUTED' ERROR_ACTION_NOT_EXECUTED = 'The action has not been executed. This may have occurred because the user pressed the stop button, or because the runtime system 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, max_iterations: int, max_budget_per_task: float | None = None, agent_to_llm_config: dict[str, LLMConfig] | None = None, agent_configs: dict[str, AgentConfig] | None = None, sid: 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.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 ) # filter out events that are not relevant to the agent # so they will not be included in the agent history self.agent_history_filter = EventFilter( exclude_types=( NullAction, NullObservation, ChangeAgentStateAction, AgentStateChangedObservation, ), exclude_hidden=True, ) # state from the previous session, state from a parent agent, or a fresh state self.set_initial_state( state=initial_state, max_iterations=max_iterations, confirmation_mode=confirmation_mode, ) self.max_budget_per_task = max_budget_per_task 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 = max_iterations self._initial_max_budget_per_task = max_budget_per_task # 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.get_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) # we made history, now is the time to rewrite it! # the final state.history will be used by external scripts like evals, tests, etc. # history will need to be complete WITH delegates events # like the regular agent history, it does not include: # - 'hidden' events, events with hidden=True # - backend events (the default 'filtered out' types, types in self.filter_out) start_id = self.state.start_id if self.state.start_id >= 0 else 0 end_id = ( self.state.end_id if self.state.end_id >= 0 else self.event_stream.get_latest_event_id() ) self.state.history = list( self.event_stream.search_events( start_id=start_id, end_id=end_id, reverse=False, filter=self.agent_history_filter, ) ) # 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) def update_state_before_step(self) -> None: self.state.iteration += 1 self.state.local_iteration += 1 async def update_state_after_step(self) -> None: # update metrics especially for cost. Use deepcopy to avoid it being modified by agent._reset() self.state.local_metrics = copy.deepcopy(self.agent.llm.metrics) 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: err_id = '' if isinstance(e, AuthenticationError): err_id = 'STATUS$ERROR_LLM_AUTHENTICATION' self.state.last_error = err_id elif isinstance( e, ( ServiceUnavailableError, APIConnectionError, APIError, ), ): err_id = 'STATUS$ERROR_LLM_SERVICE_UNAVAILABLE' self.state.last_error = err_id elif isinstance(e, InternalServerError): err_id = 'STATUS$ERROR_LLM_INTERNAL_SERVER_ERROR' self.state.last_error = err_id elif isinstance(e, BadRequestError) and 'ExceededBudget' in str(e): err_id = 'STATUS$ERROR_LLM_OUT_OF_CREDITS' self.state.last_error = err_id elif isinstance(e, ContentPolicyViolationError) or ( isinstance(e, BadRequestError) and 'ContentPolicyViolationError' in str(e) ): err_id = 'STATUS$ERROR_LLM_CONTENT_POLICY_VIOLATION' self.state.last_error = err_id elif isinstance(e, RateLimitError): await self.set_agent_state_to(AgentState.RATE_LIMITED) return self.status_callback('error', err_id, 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 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, ): # 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 # if the event is not filtered out, add it to the history if self.agent_history_filter.include(event): self.state.history.append(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 self.state.metrics.merge(self.state.local_metrics) await self.set_agent_state_to(AgentState.FINISHED) elif isinstance(action, AgentRejectAction): self.state.outputs = action.outputs self.state.metrics.merge(self.state.local_metrics) 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'} ) if observation.llm_metrics is not None: self.agent.llm.metrics.merge(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 elif isinstance(observation, ErrorObservation): if self.state.agent_state == AgentState.ERROR: self.state.metrics.merge(self.state.local_metrics) 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}, ) # Extend max iterations when the user sends a message (only in non-headless mode) if self._initial_max_iterations is not None and not self.headless_mode: self.state.max_iterations = ( self.state.iteration + self._initial_max_iterations ) if ( self.state.traffic_control_state == TrafficControlState.THROTTLING or self.state.traffic_control_state == TrafficControlState.PAUSED ): self.state.traffic_control_state = TrafficControlState.NORMAL self.log( 'debug', f'Extended max iterations to {self.state.max_iterations} after user message', ) # try to retrieve microagents relevant to the user message # set pending_action while we search for information # 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: obs = ErrorObservation( content=ERROR_ACTION_NOT_EXECUTED, error_id=ERROR_ACTION_NOT_EXECUTED_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 if new_state in (AgentState.STOPPED, AgentState.ERROR): # sync existing metrics BEFORE resetting the agent await self.update_state_after_step() self.state.metrics.merge(self.state.local_metrics) self._reset() elif ( new_state == AgentState.RUNNING and self.state.agent_state == AgentState.PAUSED # TODO: do we really need both THROTTLING and PAUSED states, or can we clean up one of them completely? and self.state.traffic_control_state == TrafficControlState.THROTTLING ): # user intends to interrupt traffic control and let the task resume temporarily self.state.traffic_control_state = TrafficControlState.PAUSED # User has chosen to deliberately continue - lets double the max iterations if ( self.state.iteration is not None and self.state.max_iterations is not None and self._initial_max_iterations is not None and not self.headless_mode ): if self.state.iteration >= self.state.max_iterations: self.state.max_iterations += self._initial_max_iterations if ( self.state.metrics.accumulated_cost is not None and self.max_budget_per_task is not None and self._initial_max_budget_per_task is not None ): if self.state.metrics.accumulated_cost >= self.max_budget_per_task: self.max_budget_per_task += self._initial_max_budget_per_task elif 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) self.state.agent_state = new_state # 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, ) 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) llm = LLM(config=llm_config, retry_listener=self._notify_on_llm_retry) delegate_agent = agent_cls(llm=llm, config=agent_config) state = State( session_id=self.id.removesuffix('-delegate'), inputs=action.inputs or {}, local_iteration=0, iteration=self.state.iteration, max_iterations=self.state.max_iterations, 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, ) 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', agent=delegate_agent, event_stream=self.event_stream, max_iterations=self.state.max_iterations, max_budget_per_task=self.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 = self.delegate.state.iteration # 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) formatted_output = ', '.join( f'{key}: {value}' for key, value in delegate_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.local_iteration} GLOBAL STEP {self.state.iteration}', extra={'msg_type': 'STEP'}, ) stop_step = False if self.state.iteration >= self.state.max_iterations: stop_step = await self._handle_traffic_control( 'iteration', self.state.iteration, self.state.max_iterations ) if self.max_budget_per_task is not None: current_cost = self.state.metrics.accumulated_cost if current_cost > self.max_budget_per_task: stop_step = await self._handle_traffic_control( 'budget', current_cost, self.max_budget_per_task ) if stop_step: logger.warning('Stopping agent due to traffic control') return if self._is_stuck(): await self._react_to_exception( AgentStuckInLoopError('Agent got stuck in a loop') ) return self.update_state_before_step() 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 isinstance(e, ContextWindowExceededError) ): if self.agent.config.enable_history_truncation: self._handle_long_context_error() 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] await self.update_state_after_step() log_level = 'info' if LOG_ALL_EVENTS else 'debug' self.log(log_level, str(action), extra={'msg_type': 'ACTION'}) def _notify_on_llm_retry(self, retries: int, max: int) -> None: if self.status_callback is not None: msg_id = 'STATUS$LLM_RETRY' self.status_callback( 'info', msg_id, f'Retrying LLM request, {retries} / {max}' ) async def _handle_traffic_control( self, limit_type: str, current_value: float, max_value: float ) -> bool: """Handles agent state after hitting the traffic control limit. Args: limit_type (str): The type of limit that was hit. current_value (float): The current value of the limit. max_value (float): The maximum value of the limit. """ stop_step = False if self.state.traffic_control_state == TrafficControlState.PAUSED: self.log( 'debug', 'Hitting traffic control, temporarily resume upon user request' ) self.state.traffic_control_state = TrafficControlState.NORMAL else: self.state.traffic_control_state = TrafficControlState.THROTTLING # Format values as integers for iterations, keep decimals for budget if limit_type == 'iteration': current_str = str(int(current_value)) max_str = str(int(max_value)) else: current_str = f'{current_value:.2f}' max_str = f'{max_value:.2f}' if self.headless_mode: e = RuntimeError( f'Agent reached maximum {limit_type} in headless mode. ' f'Current {limit_type}: {current_str}, max {limit_type}: {max_str}' ) await self._react_to_exception(e) else: e = RuntimeError( f'Agent reached maximum {limit_type}. ' f'Current {limit_type}: {current_str}, max {limit_type}: {max_str}. ' ) # FIXME: this isn't really an exception--we should have a different path await self._react_to_exception(e) stop_step = True return stop_step @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( 'warning', 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, confirmation_mode: bool = False, ) -> None: """Sets the initial state for the agent, either from the previous session, or from a parent agent, or by creating a new one. Args: state: The state to initialize with, or None to create a new state. max_iterations: The maximum number of iterations allowed for the task. confirmation_mode: Whether to enable confirmation mode. """ # state can come from: # - the previous session, in which case it has history # - from a parent agent, in which case it has no history # - None / a new state # If state is None, we create a brand new state and still load the event stream so we can restore the history if state is None: self.state = State( session_id=self.id.removesuffix('-delegate'), inputs={}, max_iterations=max_iterations, confirmation_mode=confirmation_mode, ) self.state.start_id = 0 self.log( 'info', f'AgentController {self.id} - created new state. start_id: {self.state.start_id}', ) else: self.state = state if self.state.start_id <= -1: self.state.start_id = 0 self.log( 'info', f'AgentController {self.id} initializing history from event {self.state.start_id}', ) # Always load from the event stream to avoid losing history self._init_history() 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 [ event_to_trajectory(event, include_screenshots) for event in self.state.history ] def _init_history(self) -> None: """Initializes the agent's history from the event stream. The history is a list of events that: - Excludes events of types listed in self.filter_out - Excludes events with hidden=True attribute - For delegate events (between AgentDelegateAction and AgentDelegateObservation): - Excludes all events between the action and observation - Includes the delegate action and observation themselves """ # define range of events to fetch # delegates start with a start_id and initially won't find any events # otherwise we're restoring a previous session start_id = self.state.start_id if self.state.start_id >= 0 else 0 end_id = ( self.state.end_id if self.state.end_id >= 0 else self.event_stream.get_latest_event_id() ) # sanity check if start_id > end_id + 1: self.log( 'warning', f'start_id {start_id} is greater than end_id + 1 ({end_id + 1}). History will be empty.', ) self.state.history = [] return events: list[Event] = [] # Get rest of history events_to_add = list( self.event_stream.search_events( start_id=start_id, end_id=end_id, reverse=False, filter=self.agent_history_filter, ) ) events.extend(events_to_add) # Find all delegate action/observation pairs delegate_ranges: list[tuple[int, int]] = [] delegate_action_ids: list[int] = [] # stack of unmatched delegate action IDs for event in events: if isinstance(event, AgentDelegateAction): delegate_action_ids.append(event.id) # Note: we can get agent=event.agent and task=event.inputs.get('task','') # if we need to track these in the future elif isinstance(event, AgentDelegateObservation): # Match with most recent unmatched delegate action if not delegate_action_ids: self.log( 'warning', f'Found AgentDelegateObservation without matching action at id={event.id}', ) continue action_id = delegate_action_ids.pop() delegate_ranges.append((action_id, event.id)) # Filter out events between delegate action/observation pairs if delegate_ranges: filtered_events: list[Event] = [] current_idx = 0 for start_id, end_id in sorted(delegate_ranges): # Add events before delegate range filtered_events.extend( event for event in events[current_idx:] if event.id < start_id ) # Add delegate action and observation filtered_events.extend( event for event in events if event.id in (start_id, end_id) ) # Update index to after delegate range current_idx = next( (i for i, e in enumerate(events) if e.id > end_id), len(events) ) # Add any remaining events after last delegate range filtered_events.extend(events[current_idx:]) self.state.history = filtered_events else: self.state.history = events # make sure history is in sync self.state.start_id = start_id def _handle_long_context_error(self) -> None: # When context window is exceeded, keep roughly half of agent interactions kept_events = self._apply_conversation_window() kept_event_ids = {e.id for e in kept_events} self.log( 'info', f'Context window exceeded. Keeping events with IDs: {kept_event_ids}', ) # The events to forget are those that are not in the kept set forgotten_event_ids = {e.id for e in self.state.history} - kept_event_ids if len(kept_event_ids) == 0: self.log( 'warning', 'No events kept after applying conversation window. This should not happen.', ) # verify that the first event id in kept_event_ids is the same as the start_id if len(kept_event_ids) > 0 and self.state.history[0].id not in kept_event_ids: self.log( 'warning', f'First event after applying conversation window was not kept: {self.state.history[0].id} not in {kept_event_ids}', ) # Add an error event to trigger another step by the agent self.event_stream.add_event( CondensationAction( forgotten_events_start_id=min(forgotten_event_ids) if forgotten_event_ids else 0, forgotten_events_end_id=max(forgotten_event_ids) if forgotten_event_ids else 0, ), EventSource.AGENT, ) def _apply_conversation_window(self) -> list[Event]: """Cuts history roughly in half when context window is exceeded. It preserves action-observation pairs and ensures that the system message, the first user message, and its associated recall observation are always included at the beginning of the context window. The algorithm: 1. Identify essential initial events: System Message, First User Message, Recall Observation. 2. Determine the slice of recent events to potentially keep. 3. Validate the start of the recent slice for dangling observations. 4. Combine essential events and validated recent events, ensuring essentials come first. Args: events: List of events to filter Returns: Filtered list of events keeping newest half while preserving pairs and essential initial events. """ if not self.state.history: return [] history = self.state.history # 1. Identify essential initial events system_message: SystemMessageAction | None = None first_user_msg: MessageAction | None = None recall_action: RecallAction | None = None recall_observation: Observation | None = None # Find System Message (should be the first event, if it exists) system_message = next( (e for e in history if isinstance(e, SystemMessageAction)), None ) assert ( system_message is None or isinstance(system_message, SystemMessageAction) and system_message.id == history[0].id ) # Find First User Message, which MUST exist first_user_msg = self._first_user_message() if first_user_msg is None: raise RuntimeError('No first user message found in the event stream.') first_user_msg_index = -1 for i, event in enumerate(history): if isinstance(event, MessageAction) and event.source == EventSource.USER: first_user_msg = event first_user_msg_index = i break # Find Recall Action and Observation related to the First User Message if first_user_msg is not None and first_user_msg_index != -1: # Look for RecallAction after the first user message for i in range(first_user_msg_index + 1, len(history)): event = history[i] if ( isinstance(event, RecallAction) and event.query == first_user_msg.content ): # Found RecallAction, now look for its Observation recall_action = event for j in range(i + 1, len(history)): obs_event = history[j] # Check for Observation caused by this RecallAction if ( isinstance(obs_event, Observation) and obs_event.cause == recall_action.id ): recall_observation = obs_event break # Found the observation, stop inner loop break # Found the recall action (and maybe obs), stop outer loop essential_events: list[Event] = [] if system_message: essential_events.append(system_message) if first_user_msg: essential_events.append(first_user_msg) # Also keep the RecallAction that triggered the essential RecallObservation if recall_action: essential_events.append(recall_action) if recall_observation: essential_events.append(recall_observation) # 2. Determine the slice of recent events to potentially keep num_non_essential_events = len(history) - len(essential_events) # Keep roughly half of the non-essential events, minimum 1 num_recent_to_keep = max(1, num_non_essential_events // 2) # Calculate the starting index for the recent slice slice_start_index = len(history) - num_recent_to_keep slice_start_index = max(0, slice_start_index) # Ensure index is not negative recent_events_slice = history[slice_start_index:] # 3. Validate the start of the recent slice for dangling observations # IMPORTANT: Most observations in history are tool call results, which cannot be without their action, or we get an LLM API error first_valid_event_index = 0 for i, event in enumerate(recent_events_slice): if isinstance(event, Observation): first_valid_event_index += 1 else: break # If all events in the slice are dangling observations, we need to keep at least one if first_valid_event_index == len(recent_events_slice): self.log( 'warning', 'All recent events are dangling observations, which we truncate. This means the agent has only the essential first events. This should not happen.', ) # Adjust the recent_events_slice if dangling observations were found at the start if first_valid_event_index < len(recent_events_slice): validated_recent_events = recent_events_slice[first_valid_event_index:] if first_valid_event_index > 0: self.log( 'debug', f'Removed {first_valid_event_index} dangling observation(s) from the start of recent event slice.', ) else: validated_recent_events = [] # 4. Combine essential events and validated recent events events_to_keep: list[Event] = essential_events + validated_recent_events self.log('debug', f'History truncated. Kept {len(events_to_keep)} events.') return events_to_keep 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 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.agent.llm.metrics # Get metrics from condenser LLM if it exists condenser_metrics: TokenUsage | 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 # 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.agent.llm.metrics.token_usages: latest_usage = self.agent.llm.metrics.token_usages[-1] accumulated_usage = self.agent.llm.metrics.accumulated_token_usage self.log( 'debug', f'Action metrics - accumulated_cost: {metrics.accumulated_cost}, ' 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.get_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) -> 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. Returns: MessageAction | None: The first user message, or None if no user message found """ # 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.get_events( start_id=self.state.start_id, ) if isinstance(e, MessageAction) and e.source == EventSource.USER ), None, ) return self._cached_first_user_message