mirror of
https://github.com/OpenHands/OpenHands.git
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225 lines
8.9 KiB
Python
225 lines
8.9 KiB
Python
import re
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from typing import List, Mapping
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from agenthub.codeact_agent.prompt import EXAMPLES, SYSTEM_MESSAGE
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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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AgentEchoAction,
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AgentFinishAction,
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AgentTalkAction,
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CmdRunAction,
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IPythonRunCellAction,
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NullAction,
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)
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from opendevin.events.observation import (
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AgentMessageObservation,
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CmdOutputObservation,
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IPythonRunCellObservation,
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UserMessageObservation,
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)
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from opendevin.llm.llm import LLM
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from opendevin.runtime.plugins import (
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JupyterRequirement,
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PluginRequirement,
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SWEAgentCommandsRequirement,
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)
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def parse_response(response) -> str:
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action = response.choices[0].message.content
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for lang in ['bash', 'ipython']:
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if f'<execute_{lang}>' in action and f'</execute_{lang}>' not in action:
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action += f'</execute_{lang}>'
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return action
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def truncate_observation(observation: str, max_chars: int = 5000) -> str:
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"""
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Truncate the middle of the observation if it is too long.
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"""
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if len(observation) <= max_chars:
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return observation
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half = max_chars // 2
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return (
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observation[:half]
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+ '\n[... Observation truncated due to length ...]\n'
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+ observation[-half:]
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)
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class CodeActAgent(Agent):
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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/2402.13463), [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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JupyterRequirement(),
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SWEAgentCommandsRequirement(),
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]
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SUPPORTED_ACTIONS = (
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CmdRunAction,
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IPythonRunCellAction,
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AgentEchoAction,
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AgentTalkAction,
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NullAction,
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)
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SUPPORTED_OBSERVATIONS = (
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AgentMessageObservation,
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UserMessageObservation,
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CmdOutputObservation,
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IPythonRunCellObservation,
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)
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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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"""
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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.messages: List[Mapping[str, str]] = []
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def step(self, state: State) -> Action:
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"""
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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 and background commands
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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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- AgentTalkAction(content) - Talk 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 len(self.messages) == 0:
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assert state.plan.main_goal, 'Expecting instruction to be set'
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self.messages = [
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{'role': 'system', 'content': SYSTEM_MESSAGE},
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{
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'role': 'user',
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'content': (
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f'Here is an example of how you can interact with the environment for task solving:\n{EXAMPLES}\n\n'
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f"NOW, LET'S START!\n\n{state.plan.main_goal}"
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),
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},
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]
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updated_info = state.updated_info
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if updated_info:
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for prev_action, obs in updated_info:
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assert isinstance(
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prev_action, self.SUPPORTED_ACTIONS
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), f'{prev_action.__class__} is not supported (supported: {self.SUPPORTED_ACTIONS})'
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# prev_action is already added to self.messages when returned
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# handle observations
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assert isinstance(
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obs, self.SUPPORTED_OBSERVATIONS
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), f'{obs.__class__} is not supported (supported: {self.SUPPORTED_OBSERVATIONS})'
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if isinstance(obs, (AgentMessageObservation, UserMessageObservation)):
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self.messages.append({'role': 'user', 'content': obs.content})
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# User wants to exit
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if obs.content.strip() == '/exit':
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return AgentFinishAction()
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elif isinstance(obs, CmdOutputObservation):
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content = 'OBSERVATION:\n' + truncate_observation(obs.content)
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content += f'\n[Command {obs.command_id} finished with exit code {obs.exit_code}]]'
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self.messages.append({'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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splited = content.split('\n')
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for i, line in enumerate(splited):
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if ' already displayed to user'
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)
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content = '\n'.join(splited)
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content = truncate_observation(content)
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self.messages.append({'role': 'user', 'content': content})
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else:
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raise NotImplementedError(
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f'Unknown observation type: {obs.__class__}'
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)
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response = self.llm.completion(
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messages=self.messages,
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stop=[
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'</execute_ipython>',
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'</execute_bash>',
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],
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temperature=0.0,
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)
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action_str: str = parse_response(response)
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state.num_of_chars += sum(
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len(message['content']) for message in self.messages
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) + len(action_str)
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self.messages.append({'role': 'assistant', 'content': action_str})
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if bash_command := re.search(
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r'<execute_bash>(.*)</execute_bash>', action_str, re.DOTALL
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):
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# remove the command from the action string to get thought
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thought = action_str.replace(bash_command.group(0), '').strip()
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# a command was found
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command_group = bash_command.group(1).strip()
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if command_group.strip() == 'exit':
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return AgentFinishAction()
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return CmdRunAction(command=command_group, thought=thought)
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elif python_code := re.search(
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r'<execute_ipython>(.*)</execute_ipython>', action_str, re.DOTALL
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):
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# a code block was found
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code_group = python_code.group(1).strip()
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thought = action_str.replace(python_code.group(0), '').strip()
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return IPythonRunCellAction(code=code_group, thought=thought)
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else:
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# We assume the LLM is GOOD enough that when it returns pure natural language
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# it want to talk to the user
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return AgentTalkAction(content=action_str)
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def search_memory(self, query: str) -> List[str]:
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raise NotImplementedError('Implement this abstract method')
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