from openhands.controller.agent import Agent from openhands.controller.state.control_flags import ( BudgetControlFlag, IterationControlFlag, ) from openhands.controller.state.state import State from openhands.core.logger import openhands_logger as logger from openhands.events.action.agent import AgentDelegateAction, ChangeAgentStateAction from openhands.events.action.empty import NullAction from openhands.events.event import Event from openhands.events.event_filter import EventFilter from openhands.events.observation.agent import AgentStateChangedObservation from openhands.events.observation.delegate import AgentDelegateObservation from openhands.events.observation.empty import NullObservation from openhands.events.serialization.event import event_to_trajectory from openhands.events.stream import EventStream from openhands.llm.metrics import Metrics from openhands.storage.files import FileStore class StateTracker: """Manages and synchronizes the state of an agent throughout its lifecycle. It is responsible for: 1. Maintaining agent state persistence across sessions 2. Managing agent history by filtering and tracking relevant events (previously done in the agent controller) 3. Synchronizing metrics between the controller and LLM components 4. Updating control flags for budget and iteration limits """ def __init__( self, sid: str | None, file_store: FileStore | None, user_id: str | None ): self.sid = sid self.file_store = file_store self.user_id = user_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, ) def set_initial_state( self, id: str, agent: Agent, state: State | None, max_iterations: int, max_budget_per_task: float | None, 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=id.removesuffix('-delegate'), user_id=self.user_id, inputs={}, iteration_flag=IterationControlFlag( limit_increase_amount=max_iterations, current_value=0, max_value=max_iterations, ), budget_flag=None if not max_budget_per_task else BudgetControlFlag( limit_increase_amount=max_budget_per_task, current_value=0, max_value=max_budget_per_task, ), confirmation_mode=confirmation_mode, ) self.state.start_id = 0 logger.info( f'AgentController {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 logger.info( f'AgentController {id} initializing history from event {self.state.start_id}', ) # Share the state metrics with the agent's LLM metrics # This ensures that all accumulated metrics are always in sync between controller and llm agent.llm.metrics = self.state.metrics def _init_history(self, event_stream: EventStream) -> 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 event_stream.get_latest_event_id() ) # sanity check if start_id > end_id + 1: logger.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( 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: logger.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 close(self, event_stream: EventStream): # 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 event_stream.get_latest_event_id() ) self.state.history = list( event_stream.search_events( start_id=start_id, end_id=end_id, reverse=False, filter=self.agent_history_filter, ) ) def add_history(self, event: Event): # if the event is not filtered out, add it to the history if self.agent_history_filter.include(event): self.state.history.append(event) def get_trajectory(self, include_screenshots: bool = False) -> list[dict]: return [ event_to_trajectory(event, include_screenshots) for event in self.state.history ] def maybe_increase_control_flags_limits(self, headless_mode: bool): # Iteration and budget extensions are independent of each other # An error will be thrown if any one of the control flags have reached or exceeded its limit self.state.iteration_flag.increase_limit(headless_mode) if self.state.budget_flag: self.state.budget_flag.increase_limit(headless_mode) def get_metrics_snapshot(self): """Deep copy of metrics This serves as a snapshot for the parent's metrics at the time a delegate is created It will be stored and used to compute local metrics for the delegate (since delegates now accumulate metrics from where its parent left off) """ return self.state.metrics.copy() def save_state(self): """Save's current state to persistent store""" if self.sid and self.file_store: self.state.save_to_session(self.sid, self.file_store, self.user_id) def run_control_flags(self): """Performs one step of the control flags""" self.state.iteration_flag.step() if self.state.budget_flag: self.state.budget_flag.step() def sync_budget_flag_with_metrics(self): """Ensures that budget flag is up to date with accumulated costs from llm completions Budget flag will monitor for when budget is exceeded """ if self.state.budget_flag: self.state.budget_flag.current_value = self.state.metrics.accumulated_cost def merge_metrics(self, metrics: Metrics): """Merges metrics with the state metrics NOTE: this should be refactored in the future. We should have services (draft llm, title autocomplete, condenser, etc) use their own LLMs, but the metrics object should be shared. This way we have one source of truth for accumulated costs from all services This would prevent having fragmented stores for metrics, and we don't have the burden of deciding where and how to store them if we decide introduce more specialized services that require llm completions """ self.state.metrics.merge(metrics) if self.state.budget_flag: self.state.budget_flag.current_value = self.state.metrics.accumulated_cost