mirror of
https://github.com/OpenHands/OpenHands.git
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240 lines
9.5 KiB
Python
240 lines
9.5 KiB
Python
from agenthub.codeact_agent.action_parser import CodeActResponseParser
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from agenthub.codeact_agent.prompt import (
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COMMAND_DOCS,
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EXAMPLES,
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GITHUB_MESSAGE,
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SYSTEM_PREFIX,
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SYSTEM_SUFFIX,
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)
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from opendevin.controller.agent import Agent
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from opendevin.controller.state.state import State
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from opendevin.events.action import (
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Action,
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AgentDelegateAction,
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AgentFinishAction,
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CmdRunAction,
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IPythonRunCellAction,
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MessageAction,
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)
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from opendevin.events.observation import (
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AgentDelegateObservation,
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CmdOutputObservation,
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IPythonRunCellObservation,
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)
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from opendevin.events.observation.observation import Observation
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from opendevin.events.serialization.event import truncate_content
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from opendevin.llm.llm import LLM
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from opendevin.runtime.plugins import (
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AgentSkillsRequirement,
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JupyterRequirement,
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PluginRequirement,
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)
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from opendevin.runtime.tools import RuntimeTool
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ENABLE_GITHUB = True
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# FIXME: We can tweak these two settings to create MicroAgents specialized toward different area
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def get_system_message() -> str:
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if ENABLE_GITHUB:
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return f'{SYSTEM_PREFIX}\n{GITHUB_MESSAGE}\n\n{COMMAND_DOCS}\n\n{SYSTEM_SUFFIX}'
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else:
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return f'{SYSTEM_PREFIX}\n\n{COMMAND_DOCS}\n\n{SYSTEM_SUFFIX}'
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def get_in_context_example() -> str:
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return EXAMPLES
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class CodeActAgent(Agent):
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VERSION = '1.8'
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"""
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The Code Act Agent is a minimalist agent.
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The agent works by passing the model a list of action-observation pairs and prompting the model to take the next step.
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### Overview
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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).
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The conceptual idea is illustrated below. At each turn, the agent can:
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1. **Converse**: Communicate with humans in natural language to ask for clarification, confirmation, etc.
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2. **CodeAct**: Choose to perform the task by executing code
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- Execute any valid Linux `bash` command
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- 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.
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### Plugin System
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To make the CodeAct agent more powerful with only access to `bash` action space, CodeAct agent leverages OpenDevin's plugin system:
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- [Jupyter plugin](https://github.com/OpenDevin/OpenDevin/tree/main/opendevin/runtime/plugins/jupyter): for IPython execution via bash command
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- [SWE-agent tool plugin](https://github.com/OpenDevin/OpenDevin/tree/main/opendevin/runtime/plugins/swe_agent_commands): Powerful bash command line tools for software development tasks introduced by [swe-agent](https://github.com/princeton-nlp/swe-agent).
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### Demo
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https://github.com/OpenDevin/OpenDevin/assets/38853559/f592a192-e86c-4f48-ad31-d69282d5f6ac
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*Example of CodeActAgent with `gpt-4-turbo-2024-04-09` performing a data science task (linear regression)*
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### Work-in-progress & Next step
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[] Support web-browsing
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[] Complete the workflow for CodeAct agent to submit Github PRs
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"""
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sandbox_plugins: list[PluginRequirement] = [
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# NOTE: AgentSkillsRequirement need to go before JupyterRequirement, since
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# AgentSkillsRequirement provides a lot of Python functions,
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# and it needs to be initialized before Jupyter for Jupyter to use those functions.
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AgentSkillsRequirement(),
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JupyterRequirement(),
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]
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runtime_tools: list[RuntimeTool] = [RuntimeTool.BROWSER]
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system_message: str = get_system_message()
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in_context_example: str = f"Here is an example of how you can interact with the environment for task solving:\n{get_in_context_example()}\n\nNOW, LET'S START!"
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action_parser = CodeActResponseParser()
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def __init__(
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self,
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llm: LLM,
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) -> None:
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"""Initializes a new instance of the CodeActAgent class.
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Parameters:
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- llm (LLM): The llm to be used by this agent
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"""
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super().__init__(llm)
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self.reset()
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def action_to_str(self, action: Action) -> str:
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if isinstance(action, CmdRunAction):
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return (
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f'{action.thought}\n<execute_bash>\n{action.command}\n</execute_bash>'
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)
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elif isinstance(action, IPythonRunCellAction):
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return f'{action.thought}\n<execute_ipython>\n{action.code}\n</execute_ipython>'
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elif isinstance(action, AgentDelegateAction):
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return f'{action.thought}\n<execute_browse>\n{action.inputs["task"]}\n</execute_browse>'
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elif isinstance(action, MessageAction):
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return action.content
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elif isinstance(action, AgentFinishAction) and action.source == 'agent':
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return action.thought
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return ''
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def get_action_message(self, action: Action) -> dict[str, str] | None:
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if (
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isinstance(action, AgentDelegateAction)
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or isinstance(action, CmdRunAction)
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or isinstance(action, IPythonRunCellAction)
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or isinstance(action, MessageAction)
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or (isinstance(action, AgentFinishAction) and action.source == 'agent')
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):
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return {
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'role': 'user' if action.source == 'user' else 'assistant',
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'content': self.action_to_str(action),
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}
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return None
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def get_observation_message(self, obs: Observation) -> dict[str, str] | None:
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max_message_chars = self.llm.config.max_message_chars
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if isinstance(obs, CmdOutputObservation):
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content = 'OBSERVATION:\n' + truncate_content(
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obs.content, max_message_chars
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)
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content += (
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f'\n[Command {obs.command_id} finished with exit code {obs.exit_code}]'
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)
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return {'role': 'user', 'content': content}
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elif isinstance(obs, IPythonRunCellObservation):
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content = 'OBSERVATION:\n' + obs.content
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# replace base64 images with a placeholder
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splitted = content.split('\n')
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for i, line in enumerate(splitted):
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if ' already displayed to user'
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)
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content = '\n'.join(splitted)
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content = truncate_content(content, max_message_chars)
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return {'role': 'user', 'content': content}
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elif isinstance(obs, AgentDelegateObservation):
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content = 'OBSERVATION:\n' + truncate_content(
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str(obs.outputs), max_message_chars
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)
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return {'role': 'user', 'content': content}
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return None
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def reset(self) -> None:
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"""Resets the CodeAct Agent."""
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super().reset()
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def step(self, state: State) -> Action:
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"""Performs one step using the CodeAct Agent.
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This includes gathering info on previous steps and prompting the model to make a command to execute.
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Parameters:
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- state (State): used to get updated info
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Returns:
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- CmdRunAction(command) - bash command to run
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- IPythonRunCellAction(code) - IPython code to run
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- AgentDelegateAction(agent, inputs) - delegate action for (sub)task
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- MessageAction(content) - Message action to run (e.g. ask for clarification)
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- AgentFinishAction() - end the interaction
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"""
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# if we're done, go back
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latest_user_message = state.history.get_last_user_message()
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if latest_user_message and latest_user_message.strip() == '/exit':
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return AgentFinishAction()
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# prepare what we want to send to the LLM
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messages: list[dict[str, str]] = self._get_messages(state)
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response = self.llm.completion(
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messages=messages,
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stop=[
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'</execute_ipython>',
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'</execute_bash>',
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'</execute_browse>',
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],
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temperature=0.0,
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)
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return self.action_parser.parse(response)
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def _get_messages(self, state: State) -> list[dict[str, str]]:
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messages = [
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{'role': 'system', 'content': self.system_message},
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{'role': 'user', 'content': self.in_context_example},
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]
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for event in state.history.get_events():
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# create a regular message from an event
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if isinstance(event, Action):
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message = self.get_action_message(event)
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elif isinstance(event, Observation):
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message = self.get_observation_message(event)
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else:
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raise ValueError(f'Unknown event type: {type(event)}')
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# add regular message
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if message:
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messages.append(message)
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# the latest user message is important:
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# we want to remind the agent of the environment constraints
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latest_user_message = next(
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(m for m in reversed(messages) if m['role'] == 'user'), None
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)
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# add a reminder to the prompt
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if latest_user_message:
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latest_user_message['content'] += (
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f'\n\nENVIRONMENT REMINDER: You have {state.max_iterations - state.iteration} turns left to complete the task. When finished reply with <finish></finish>'
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)
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return messages
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