mirror of
https://github.com/OpenHands/OpenHands.git
synced 2025-12-26 05:48:36 +08:00
2bc3e8d584
* Catch exception and return finish action with an exception message in case of exception in llm completion * Remove exception logs * Raise llm response error for any exception in llm completion * Raise LLMResponseError from async completion and async streaming completion as well
282 lines
12 KiB
Python
282 lines
12 KiB
Python
import os
|
||
|
||
from agenthub.codeact_agent.action_parser import CodeActResponseParser
|
||
from openhands.controller.agent import Agent
|
||
from openhands.controller.state.state import State
|
||
from openhands.core.config import AgentConfig
|
||
from openhands.core.message import ImageContent, Message, TextContent
|
||
from openhands.events.action import (
|
||
Action,
|
||
AgentDelegateAction,
|
||
AgentFinishAction,
|
||
CmdRunAction,
|
||
IPythonRunCellAction,
|
||
MessageAction,
|
||
)
|
||
from openhands.events.observation import (
|
||
AgentDelegateObservation,
|
||
CmdOutputObservation,
|
||
IPythonRunCellObservation,
|
||
UserRejectObservation,
|
||
)
|
||
from openhands.events.observation.error import ErrorObservation
|
||
from openhands.events.observation.observation import Observation
|
||
from openhands.events.serialization.event import truncate_content
|
||
from openhands.llm.llm import LLM
|
||
from openhands.runtime.plugins import (
|
||
AgentSkillsRequirement,
|
||
JupyterRequirement,
|
||
PluginRequirement,
|
||
)
|
||
from openhands.utils.microagent import MicroAgent
|
||
from openhands.utils.prompt import PromptManager
|
||
|
||
|
||
class CodeActAgent(Agent):
|
||
VERSION = '1.9'
|
||
"""
|
||
The Code Act Agent is a minimalist agent.
|
||
The agent works by passing the model a list of action-observation pairs and prompting the model to take the next step.
|
||
|
||
### Overview
|
||
|
||
This agent implements the CodeAct idea ([paper](https://arxiv.org/abs/2402.01030), [tweet](https://twitter.com/xingyaow_/status/1754556835703751087)) that consolidates LLM agents’ **act**ions into a unified **code** action space for both *simplicity* and *performance* (see paper for more details).
|
||
|
||
The conceptual idea is illustrated below. At each turn, the agent can:
|
||
|
||
1. **Converse**: Communicate with humans in natural language to ask for clarification, confirmation, etc.
|
||
2. **CodeAct**: Choose to perform the task by executing code
|
||
- Execute any valid Linux `bash` command
|
||
- Execute any valid `Python` code with [an interactive Python interpreter](https://ipython.org/). This is simulated through `bash` command, see plugin system below for more details.
|
||
|
||
|
||
|
||
"""
|
||
|
||
sandbox_plugins: list[PluginRequirement] = [
|
||
# NOTE: AgentSkillsRequirement need to go before JupyterRequirement, since
|
||
# AgentSkillsRequirement provides a lot of Python functions,
|
||
# and it needs to be initialized before Jupyter for Jupyter to use those functions.
|
||
AgentSkillsRequirement(),
|
||
JupyterRequirement(),
|
||
]
|
||
|
||
action_parser = CodeActResponseParser()
|
||
|
||
def __init__(
|
||
self,
|
||
llm: LLM,
|
||
config: AgentConfig,
|
||
) -> None:
|
||
"""Initializes a new instance of the CodeActAgent class.
|
||
|
||
Parameters:
|
||
- llm (LLM): The llm to be used by this agent
|
||
"""
|
||
super().__init__(llm, config)
|
||
self.reset()
|
||
|
||
self.micro_agent = (
|
||
MicroAgent(
|
||
os.path.join(
|
||
os.path.dirname(__file__), 'micro', f'{config.micro_agent_name}.md'
|
||
)
|
||
)
|
||
if config.micro_agent_name
|
||
else None
|
||
)
|
||
|
||
self.prompt_manager = PromptManager(
|
||
prompt_dir=os.path.join(os.path.dirname(__file__)),
|
||
agent_skills_docs=AgentSkillsRequirement.documentation,
|
||
micro_agent=self.micro_agent,
|
||
)
|
||
|
||
def action_to_str(self, action: Action) -> str:
|
||
if isinstance(action, CmdRunAction):
|
||
return (
|
||
f'{action.thought}\n<execute_bash>\n{action.command}\n</execute_bash>'
|
||
)
|
||
elif isinstance(action, IPythonRunCellAction):
|
||
return f'{action.thought}\n<execute_ipython>\n{action.code}\n</execute_ipython>'
|
||
elif isinstance(action, AgentDelegateAction):
|
||
return f'{action.thought}\n<execute_browse>\n{action.inputs["task"]}\n</execute_browse>'
|
||
elif isinstance(action, MessageAction):
|
||
return action.content
|
||
elif isinstance(action, AgentFinishAction) and action.source == 'agent':
|
||
return action.thought
|
||
return ''
|
||
|
||
def get_action_message(self, action: Action) -> Message | None:
|
||
if (
|
||
isinstance(action, AgentDelegateAction)
|
||
or isinstance(action, CmdRunAction)
|
||
or isinstance(action, IPythonRunCellAction)
|
||
or isinstance(action, MessageAction)
|
||
or (isinstance(action, AgentFinishAction) and action.source == 'agent')
|
||
):
|
||
content = [TextContent(text=self.action_to_str(action))]
|
||
|
||
if isinstance(action, MessageAction) and action.images_urls:
|
||
content.append(ImageContent(image_urls=action.images_urls))
|
||
|
||
return Message(
|
||
role='user' if action.source == 'user' else 'assistant', content=content
|
||
)
|
||
return None
|
||
|
||
def get_observation_message(self, obs: Observation) -> Message | None:
|
||
max_message_chars = self.llm.config.max_message_chars
|
||
if isinstance(obs, CmdOutputObservation):
|
||
text = 'OBSERVATION:\n' + truncate_content(obs.content, max_message_chars)
|
||
text += (
|
||
f'\n[Command {obs.command_id} finished with exit code {obs.exit_code}]'
|
||
)
|
||
return Message(role='user', content=[TextContent(text=text)])
|
||
elif isinstance(obs, IPythonRunCellObservation):
|
||
text = 'OBSERVATION:\n' + obs.content
|
||
# replace base64 images with a placeholder
|
||
splitted = text.split('\n')
|
||
for i, line in enumerate(splitted):
|
||
if ' already displayed to user'
|
||
)
|
||
text = '\n'.join(splitted)
|
||
text = truncate_content(text, max_message_chars)
|
||
return Message(role='user', content=[TextContent(text=text)])
|
||
elif isinstance(obs, AgentDelegateObservation):
|
||
text = 'OBSERVATION:\n' + truncate_content(
|
||
str(obs.outputs), max_message_chars
|
||
)
|
||
return Message(role='user', content=[TextContent(text=text)])
|
||
elif isinstance(obs, ErrorObservation):
|
||
text = 'OBSERVATION:\n' + truncate_content(obs.content, max_message_chars)
|
||
text += '\n[Error occurred in processing last action]'
|
||
return Message(role='user', content=[TextContent(text=text)])
|
||
elif isinstance(obs, UserRejectObservation):
|
||
text = 'OBSERVATION:\n' + truncate_content(obs.content, max_message_chars)
|
||
text += '\n[Last action has been rejected by the user]'
|
||
return Message(role='user', content=[TextContent(text=text)])
|
||
else:
|
||
# If an observation message is not returned, it will cause an error
|
||
# when the LLM tries to return the next message
|
||
raise ValueError(f'Unknown observation type: {type(obs)}')
|
||
|
||
def reset(self) -> None:
|
||
"""Resets the CodeAct Agent."""
|
||
super().reset()
|
||
|
||
def step(self, state: State) -> Action:
|
||
"""Performs one step using the CodeAct Agent.
|
||
This includes gathering info on previous steps and prompting the model to make a command to execute.
|
||
|
||
Parameters:
|
||
- state (State): used to get updated info
|
||
|
||
Returns:
|
||
- CmdRunAction(command) - bash command to run
|
||
- IPythonRunCellAction(code) - IPython code to run
|
||
- AgentDelegateAction(agent, inputs) - delegate action for (sub)task
|
||
- MessageAction(content) - Message action to run (e.g. ask for clarification)
|
||
- AgentFinishAction() - end the interaction
|
||
"""
|
||
# if we're done, go back
|
||
latest_user_message = state.history.get_last_user_message()
|
||
if latest_user_message and latest_user_message.strip() == '/exit':
|
||
return AgentFinishAction()
|
||
|
||
# prepare what we want to send to the LLM
|
||
messages = self._get_messages(state)
|
||
|
||
params = {
|
||
'messages': [message.model_dump() for message in messages],
|
||
'stop': [
|
||
'</execute_ipython>',
|
||
'</execute_bash>',
|
||
'</execute_browse>',
|
||
],
|
||
'temperature': 0.0,
|
||
}
|
||
|
||
if self.llm.supports_prompt_caching:
|
||
params['extra_headers'] = {
|
||
'anthropic-beta': 'prompt-caching-2024-07-31',
|
||
}
|
||
try:
|
||
response = self.llm.completion(**params)
|
||
except Exception:
|
||
return AgentFinishAction(
|
||
thought='Agent encountered an error while processing the last action. Please try again.'
|
||
)
|
||
|
||
return self.action_parser.parse(response)
|
||
|
||
def _get_messages(self, state: State) -> list[Message]:
|
||
messages: list[Message] = [
|
||
Message(
|
||
role='system',
|
||
content=[
|
||
TextContent(
|
||
text=self.prompt_manager.system_message,
|
||
cache_prompt=self.llm.supports_prompt_caching, # Cache system prompt
|
||
)
|
||
],
|
||
),
|
||
Message(
|
||
role='user',
|
||
content=[
|
||
TextContent(
|
||
text=self.prompt_manager.initial_user_message,
|
||
cache_prompt=self.llm.supports_prompt_caching, # if the user asks the same query,
|
||
)
|
||
],
|
||
),
|
||
]
|
||
|
||
for event in state.history.get_events():
|
||
# create a regular message from an event
|
||
if isinstance(event, Action):
|
||
message = self.get_action_message(event)
|
||
elif isinstance(event, Observation):
|
||
message = self.get_observation_message(event)
|
||
else:
|
||
raise ValueError(f'Unknown event type: {type(event)}')
|
||
|
||
# add regular message
|
||
if message:
|
||
# handle error if the message is the SAME role as the previous message
|
||
# litellm.exceptions.BadRequestError: litellm.BadRequestError: OpenAIException - Error code: 400 - {'detail': 'Only supports u/a/u/a/u...'}
|
||
# there should not have two consecutive messages from the same role
|
||
if messages and messages[-1].role == message.role:
|
||
messages[-1].content.extend(message.content)
|
||
else:
|
||
messages.append(message)
|
||
|
||
# Add caching to the last 2 user messages
|
||
if self.llm.supports_prompt_caching:
|
||
user_turns_processed = 0
|
||
for message in reversed(messages):
|
||
if message.role == 'user' and user_turns_processed < 2:
|
||
message.content[
|
||
-1
|
||
].cache_prompt = True # Last item inside the message content
|
||
user_turns_processed += 1
|
||
|
||
# the latest user message is important:
|
||
# we want to remind the agent of the environment constraints
|
||
latest_user_message = next(
|
||
(
|
||
m
|
||
for m in reversed(messages)
|
||
if m.role == 'user'
|
||
and any(isinstance(c, TextContent) for c in m.content)
|
||
),
|
||
None,
|
||
)
|
||
if latest_user_message:
|
||
reminder_text = f'\n\nENVIRONMENT REMINDER: You have {state.max_iterations - state.iteration} turns left to complete the task. When finished reply with <finish></finish>.'
|
||
latest_user_message.content.append(TextContent(text=reminder_text))
|
||
|
||
return messages
|