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##################################################################################################
# Adapted from https://github.com/TheAgentCompany/TheAgentCompany/blob/main/evaluation/run_eval.py
##################################################################################################
import asyncio
import base64
import json
import os
import shutil
import tempfile
from typing import List
import yaml
from browsing import pre_login
from openhands.controller.state.state import State
from openhands.core.config import (
AppConfig,
LLMConfig,
SandboxConfig,
get_llm_config_arg,
get_parser,
)
from openhands.core.logger import openhands_logger as logger
from openhands.core.main import create_runtime, run_controller
from openhands.events.action import CmdRunAction, MessageAction
from openhands.events.observation import BrowserOutputObservation, CmdOutputObservation
from openhands.runtime.base import Runtime
from openhands.utils.async_utils import call_async_from_sync
def get_config(
base_container_image: str,
task_short_name: str,
mount_path_on_host: str,
llm_config: LLMConfig,
) -> AppConfig:
config = AppConfig(
run_as_openhands=False,
max_budget_per_task=4,
max_iterations=100,
save_trajectory_path=os.path.join(
mount_path_on_host, f'traj_{task_short_name}.json'
),
sandbox=SandboxConfig(
base_container_image=base_container_image,
enable_auto_lint=True,
# using host network to access the host machine from the container
use_host_network=True,
# large enough timeout, since some testcases take very long to run
timeout=300,
api_key=os.environ.get('ALLHANDS_API_KEY', None),
),
# we mount trajectories path so that trajectories, generated by OpenHands
# controller, can be accessible to the evaluator file in the runtime container
workspace_mount_path=mount_path_on_host,
workspace_mount_path_in_sandbox='/outputs',
)
config.set_llm_config(llm_config)
return config
def load_dependencies(runtime: Runtime) -> List[str]:
"""
Every task has a dependencies.yml file, which lists all the services that the
task depends on. This function loads the file and returns all dependent service names.
"""
command = 'cat /utils/dependencies.yml'
action = CmdRunAction(command=command)
logger.info(action, extra={'msg_type': 'ACTION'})
obs: CmdOutputObservation = runtime.run_action(action)
logger.info(obs, extra={'msg_type': 'OBSERVATION'})
assert obs.exit_code == 0
dependencies = yaml.safe_load(obs.content)
if dependencies is None:
dependencies = []
return dependencies
def init_task_env(runtime: Runtime, hostname: str, env_llm_config: LLMConfig):
command = (
f'SERVER_HOSTNAME={hostname} '
f'LITELLM_API_KEY={env_llm_config.api_key.get_secret_value() if env_llm_config.api_key else None} '
f'LITELLM_BASE_URL={env_llm_config.base_url} '
f'LITELLM_MODEL={env_llm_config.model} '
'bash /utils/init.sh'
)
action = CmdRunAction(command=command)
action.set_hard_timeout(900)
logger.info(action, extra={'msg_type': 'ACTION'})
obs = runtime.run_action(action)
logger.info(obs, extra={'msg_type': 'OBSERVATION'})
assert obs.exit_code == 0
def codeact_user_response(state: State) -> str:
msg = (
'Please continue working on the task on whatever approach you think is suitable.\n'
'If you think you have solved the task, please finish the interaction.\n'
'IMPORTANT: YOU SHOULD NEVER ASK FOR HUMAN HELP.\n'
)
if state.history:
# check if the agent has tried to talk to the user 3 times, if so, let the agent know it can give up
user_msgs = [
event
for event in state.history
if isinstance(event, MessageAction) and event.source == 'user'
]
if len(user_msgs) >= 2:
# let the agent know that it can give up when it has tried 3 times
return (
msg
+ 'If you want to give up, run: <execute_bash> exit </execute_bash>.\n'
)
return msg
def run_solver(
runtime: Runtime,
task_name: str,
config: AppConfig,
dependencies: List[str],
save_final_state: bool,
state_dir: str,
save_screenshots: bool,
screenshots_dir: str,
) -> State:
instruction = 'Complete the task in /instruction/task.md'
if 'gitlab' in dependencies:
instruction += "\n\nGitlab username is 'root' and password is 'theagentcompany'"
state: State | None = asyncio.run(
run_controller(
config=config,
sid=task_name,
initial_user_action=MessageAction(content=instruction),
runtime=runtime,
fake_user_response_fn=codeact_user_response,
)
)
logger.info(state)
if save_screenshots:
screenshots_dir = os.path.join(screenshots_dir, task_name)
os.makedirs(screenshots_dir, exist_ok=True)
for image_id, obs in enumerate(state.history):
if isinstance(obs, BrowserOutputObservation):
image_data = base64.b64decode(obs.screenshot)
with open(
os.path.join(screenshots_dir, f'{image_id}.png'), 'wb'
) as file:
file.write(image_data)
if save_final_state:
os.makedirs(state_dir, exist_ok=True)
with open(os.path.join(state_dir, f'state_{task_name}.json'), 'w') as file:
json.dump(str(state), file)
return state
def run_evaluator(
runtime: Runtime, env_llm_config: LLMConfig, trajectory_path: str, result_path: str
):
command = (
f'LITELLM_API_KEY={env_llm_config.api_key.get_secret_value() if env_llm_config.api_key else None} '
f'LITELLM_BASE_URL={env_llm_config.base_url} '
f'LITELLM_MODEL={env_llm_config.model} '
f"DECRYPTION_KEY='theagentcompany is all you need' " # Hardcoded Key
f'python_default /utils/eval.py --trajectory_path {trajectory_path} --result_path {result_path}'
)
action = CmdRunAction(command=command)
action.set_hard_timeout(600)
logger.info(action, extra={'msg_type': 'ACTION'})
obs = runtime.run_action(action)
logger.info(obs, extra={'msg_type': 'OBSERVATION'})
assert obs.exit_code == 0
if __name__ == '__main__':
parser = get_parser()
parser.add_argument(
'--task-image-name',
type=str,
default='ghcr.io/theagentcompany/example-image:1.0.0',
help='Task image name',
)
parser.add_argument(
'--outputs-path',
type=str,
default='./outputs',
help='Folder path to save trajectories and evaluation results',
)
parser.add_argument(
'--server-hostname',
type=str,
default='localhost',
help='Server hostname, e.g. localhost to access the host machine from the container, '
'assuming the task docker container is run with `--network host` flag',
)
parser.add_argument(
'--agent-llm-config',
type=str,
default=None,
help='LLM config for agent',
)
parser.add_argument(
'--env-llm-config',
type=str,
default=None,
help='LLM config for evaluation environment (NPC & llm-based evaluator)',
)
args, _ = parser.parse_known_args()
agent_llm_config: LLMConfig | None = None
if args.agent_llm_config:
agent_llm_config = get_llm_config_arg(args.agent_llm_config)
if agent_llm_config is None:
raise ValueError(
f'Could not find LLM config for agent: --agent-llm-config {args.agent_llm_config}'
)
if agent_llm_config.api_key is None:
raise ValueError('LLM API key is not set for agent')
env_llm_config: LLMConfig | None = None
if args.env_llm_config:
env_llm_config = get_llm_config_arg(args.env_llm_config)
if env_llm_config is None:
raise ValueError(
f'Could not find LLM config for evaluation environment: --env-llm-config {args.env_llm_config}'
)
if env_llm_config.api_key is None:
raise ValueError('LLM API key is not set for evaluation environment')
task_short_name = args.task_image_name.split('/')[-1].split(':')[0]
logger.info(
f'Task image name is {args.task_image_name}, short name is {task_short_name}'
)
# mount a temporary directory to pass trajectory from host to container, and to
# pass the evaluation result from container to host
# 1) trajectory is dumped by OpenHands library (on host machine), but it's needed by
# evaluator (in container), so we mount a temporary directory to pass it in
# 2) evaluation result is written by evaluator (in container), but we need to persist
# it on host machine, so we mount a temporary directory to pass it out
if os.getenv('TMPDIR') and os.path.exists(os.getenv('TMPDIR')):
temp_dir = os.path.abspath(os.getenv('TMPDIR'))
else:
temp_dir = tempfile.mkdtemp()
config: AppConfig = get_config(
args.task_image_name, task_short_name, temp_dir, agent_llm_config
)
runtime: Runtime = create_runtime(config)
call_async_from_sync(runtime.connect)
init_task_env(runtime, args.server_hostname, env_llm_config)
dependencies = load_dependencies(runtime)
logger.info(f'Service dependencies: {dependencies}')
try:
pre_login(
runtime,
dependencies,
save_screenshots=True,
screenshots_dir=os.path.join(
os.path.abspath(args.outputs_path), 'screenshots'
),
)
except Exception as e:
logger.error(f'Failed to pre-login: {e}')
# before giving up, let's try to init and login again
init_task_env(runtime, args.server_hostname, env_llm_config)
pre_login(
runtime,
dependencies,
save_screenshots=True,
screenshots_dir=os.path.join(
os.path.abspath(args.outputs_path), 'screenshots'
),
)
state = run_solver(
runtime,
task_short_name,
config,
dependencies,
save_final_state=True,
state_dir=os.path.abspath(args.outputs_path),
save_screenshots=True,
screenshots_dir=os.path.join(os.path.abspath(args.outputs_path), 'screenshots'),
)
# this path is the absolute path in the runtime container
trajectory_path = f'/outputs/traj_{task_short_name}.json'
result_path = f'/outputs/eval_{task_short_name}.json'
run_evaluator(runtime, env_llm_config, trajectory_path, result_path)
# finally, move trajectory file and evaluation result from mount path on host (temp dir) to outputs path
shutil.move(
os.path.join(temp_dir, f'traj_{task_short_name}.json'),
os.path.join(
os.path.abspath(args.outputs_path), f'traj_{task_short_name}.json'
),
)
shutil.move(
os.path.join(temp_dir, f'eval_{task_short_name}.json'),
os.path.join(
os.path.abspath(args.outputs_path), f'eval_{task_short_name}.json'
),
)
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