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import asyncio
import json
import os
from typing import Any
import browsergym.miniwob # noqa F401 register miniwob tasks as gym environments
import gymnasium as gym
import pandas as pd
from evaluation.utils.shared import (
EvalMetadata,
EvalOutput,
codeact_user_response,
compatibility_for_eval_history_pairs,
make_metadata,
prepare_dataset,
reset_logger_for_multiprocessing,
run_evaluation,
update_llm_config_for_completions_logging,
)
from openhands.controller.state.state import State
from openhands.core.config import (
AppConfig,
SandboxConfig,
get_llm_config_arg,
parse_arguments,
)
from openhands.core.logger import openhands_logger as logger
from openhands.core.main import create_runtime, run_controller
from openhands.events.action import (
BrowseInteractiveAction,
CmdRunAction,
MessageAction,
)
from openhands.events.observation import (
BrowserOutputObservation,
CmdOutputObservation,
)
from openhands.runtime.base import Runtime
from openhands.runtime.browser.browser_env import (
BROWSER_EVAL_GET_GOAL_ACTION,
BROWSER_EVAL_GET_REWARDS_ACTION,
)
from openhands.utils.async_utils import call_async_from_sync
SUPPORTED_AGENT_CLS = {'BrowsingAgent', 'CodeActAgent'}
AGENT_CLS_TO_FAKE_USER_RESPONSE_FN = {
'CodeActAgent': codeact_user_response,
'BrowsingAgent': 'Continue the task. IMPORTANT: do not talk to the user until you have finished the task',
}
def get_config(
metadata: EvalMetadata,
env_id: str,
) -> AppConfig:
config = AppConfig(
default_agent=metadata.agent_class,
run_as_openhands=False,
runtime=os.environ.get('RUNTIME', 'docker'),
max_iterations=metadata.max_iterations,
sandbox=SandboxConfig(
base_container_image='xingyaoww/od-eval-miniwob:v1.0',
enable_auto_lint=True,
use_host_network=False,
browsergym_eval_env=env_id,
api_key=os.environ.get('ALLHANDS_API_KEY', None),
remote_runtime_api_url=os.environ.get('SANDBOX_REMOTE_RUNTIME_API_URL'),
remote_runtime_init_timeout=1800,
keep_runtime_alive=False,
timeout=120,
),
# do not mount workspace
workspace_base=None,
workspace_mount_path=None,
)
config.set_llm_config(
update_llm_config_for_completions_logging(
metadata.llm_config, metadata.eval_output_dir, env_id
)
)
return config
def initialize_runtime(
runtime: Runtime,
) -> tuple[str, BrowserOutputObservation]:
"""Initialize the runtime for the agent.
This function is called before the runtime is used to run the agent.
"""
logger.info(f"{'-' * 50} BEGIN Runtime Initialization Fn {'-' * 50}")
obs: CmdOutputObservation
# Set instance id
action = CmdRunAction(command='mkdir -p /workspace')
logger.info(action, extra={'msg_type': 'ACTION'})
obs = runtime.run_action(action)
assert obs.exit_code == 0
action = BrowseInteractiveAction(browser_actions=BROWSER_EVAL_GET_GOAL_ACTION)
logger.info(action, extra={'msg_type': 'ACTION'})
obs = runtime.run_action(action)
logger.info(obs, extra={'msg_type': 'OBSERVATION'})
goal = obs.content
# Run noop to get the initial browser observation (e.g., the page URL & content)
action = BrowseInteractiveAction(browser_actions='noop(1000)')
logger.info(action, extra={'msg_type': 'ACTION'})
obs = runtime.run_action(action)
logger.info(obs, extra={'msg_type': 'OBSERVATION'})
logger.info(f"{'-' * 50} END Runtime Initialization Fn {'-' * 50}")
return goal, obs
def complete_runtime(
runtime: Runtime,
) -> dict[str, Any]:
"""Complete the runtime for the agent.
This function is called before the runtime is used to run the agent.
If you need to do something in the sandbox to get the correctness metric after
the agent has run, modify this function.
"""
logger.info(f"{'-' * 50} BEGIN Runtime Completion Fn {'-' * 50}")
obs: CmdOutputObservation
action = BrowseInteractiveAction(browser_actions=BROWSER_EVAL_GET_REWARDS_ACTION)
logger.info(action, extra={'msg_type': 'ACTION'})
obs = runtime.run_action(action)
logger.info(obs, extra={'msg_type': 'OBSERVATION'})
logger.info(f"{'-' * 50} END Runtime Completion Fn {'-' * 50}")
return {
'rewards': json.loads(obs.content),
}
def process_instance(
instance: pd.Series,
metadata: EvalMetadata,
reset_logger: bool = True,
) -> EvalOutput:
env_id = instance.instance_id
config = get_config(metadata, env_id)
# Setup the logger properly, so you can run multi-processing to parallelize the evaluation
if reset_logger:
log_dir = os.path.join(metadata.eval_output_dir, 'infer_logs')
reset_logger_for_multiprocessing(logger, env_id, log_dir)
else:
logger.info(f'Starting evaluation for instance {env_id}.')
runtime = create_runtime(config)
call_async_from_sync(runtime.connect)
task_str, obs = initialize_runtime(runtime)
task_str += (
f'\nInitial browser state (output of `noop(1000)`):\n{obs.get_agent_obs_text()}'
)
state: State | None = asyncio.run(
run_controller(
config=config,
initial_user_action=MessageAction(
content=task_str
), # take output from initialize_runtime
runtime=runtime,
fake_user_response_fn=AGENT_CLS_TO_FAKE_USER_RESPONSE_FN[
metadata.agent_class
],
)
)
# ======= Attempt to evaluate the agent's environment impact =======
# If you are working on some simpler benchmark that only evaluates the final model output (e.g., in a MessageAction)
# You can simply get the LAST `MessageAction` from the returned `state.history` and parse it for evaluation.
if state is None:
raise ValueError('State should not be None.')
metrics = state.metrics.get() if state.metrics else None
# Instruction is the first message from the USER
instruction = ''
for event in state.history:
if isinstance(event, MessageAction):
instruction = event.content
break
return_val = complete_runtime(runtime)
logger.info(f'Return value from complete_runtime: {return_val}')
reward = max(return_val['rewards'], default=0)
# history is now available as a stream of events, rather than list of pairs of (Action, Observation)
# for compatibility with the existing output format, we can remake the pairs here
# remove when it becomes unnecessary
histories = compatibility_for_eval_history_pairs(state.history)
# Save the output
output = EvalOutput(
instance_id=env_id,
instruction=instruction,
metadata=metadata,
history=histories,
metrics=metrics,
error=state.last_error if state and state.last_error else None,
test_result={
'reward': reward,
},
)
return output
if __name__ == '__main__':
args = parse_arguments()
dataset = pd.DataFrame(
{
'instance_id': [
id
for id in gym.envs.registry.keys()
if id.startswith('browsergym/miniwob')
]
}
)
llm_config = None
if args.llm_config:
llm_config = get_llm_config_arg(args.llm_config)
# modify_params must be False for evaluation purpose, for reproducibility and accurancy of results
llm_config.modify_params = False
if llm_config is None:
raise ValueError(f'Could not find LLM config: --llm_config {args.llm_config}')
metadata = make_metadata(
llm_config,
'miniwob',
args.agent_cls,
args.max_iterations,
args.eval_note,
args.eval_output_dir,
)
output_file = os.path.join(metadata.eval_output_dir, 'output.jsonl')
instances = prepare_dataset(dataset, output_file, args.eval_n_limit)
run_evaluation(
instances, metadata, output_file, args.eval_num_workers, process_instance
)
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