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feat(frontend): add user feedback Likert scale for agent performance rating (only on OH Cloud) (#8992)

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Co-authored-by: openhands <openhands@all-hands.dev>
Co-authored-by: sp.wack <83104063+amanape@users.noreply.github.com>
This commit is contained in:
Xingyao Wang
2025-06-16 15:26:24 -04:00
committed by GitHub
parent 2fd1fdcd7e
commit 2c4ecd02f7
14 changed files with 710 additions and 100 deletions
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44
openhands/controller/state/state_tracker.py
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@@ -1,5 +1,8 @@
from openhands.controller.agent import Agent
from openhands.controller.state.control_flags import BudgetControlFlag, IterationControlFlag
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
@@ -71,9 +74,19 @@ class StateTracker:
self.state = State(
session_id=id.removesuffix('-delegate'),
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
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
@@ -89,7 +102,6 @@ class StateTracker:
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
@@ -221,9 +233,7 @@ class StateTracker:
for event in self.state.history
]
def maybe_increase_control_flags_limits(
self, headless_mode: bool
):
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)
@@ -247,7 +257,6 @@ class StateTracker:
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
@@ -256,25 +265,24 @@ class StateTracker:
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
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
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
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
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)