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* Replace OpenDevin with OpenHands * Update CONTRIBUTING.md * Update README.md * Update README.md * update poetry lock; move opendevin folder to openhands * fix env var * revert image references in docs * revert permissions * revert permissions --------- Co-authored-by: Xingyao Wang <xingyao6@illinois.edu>
250 lines
10 KiB
Python
250 lines
10 KiB
Python
import os
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from agenthub.codeact_agent.action_parser import CodeActResponseParser
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from openhands.controller.agent import Agent
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from openhands.controller.state.state import State
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from openhands.core.config import AgentConfig
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from openhands.core.message import ImageContent, Message, TextContent
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from openhands.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 openhands.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 openhands.events.observation.error import ErrorObservation
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from openhands.events.observation.observation import Observation
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from openhands.events.serialization.event import truncate_content
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from openhands.llm.llm import LLM
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from openhands.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 openhands.utils.prompt import PromptManager
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class CodeActAgent(Agent):
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VERSION = '1.9'
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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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"""
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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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action_parser = CodeActResponseParser()
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def __init__(
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self,
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llm: LLM,
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config: AgentConfig,
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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, config)
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self.reset()
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self.prompt_manager = PromptManager(
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prompt_dir=os.path.join(os.path.dirname(__file__)),
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agent_skills_docs=AgentSkillsRequirement.documentation,
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micro_agent_name=None, # TODO: implement micro-agent
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)
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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) -> Message | 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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content = [TextContent(text=self.action_to_str(action))]
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if isinstance(action, MessageAction) and action.images_urls:
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content.append(ImageContent(image_urls=action.images_urls))
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return Message(
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role='user' if action.source == 'user' else 'assistant', content=content
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)
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return None
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def get_observation_message(self, obs: Observation) -> Message | 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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text = 'OBSERVATION:\n' + truncate_content(obs.content, max_message_chars)
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text += (
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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 Message(role='user', content=[TextContent(text=text)])
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elif isinstance(obs, IPythonRunCellObservation):
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text = 'OBSERVATION:\n' + obs.content
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# replace base64 images with a placeholder
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splitted = text.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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text = '\n'.join(splitted)
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text = truncate_content(text, max_message_chars)
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return Message(role='user', content=[TextContent(text=text)])
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elif isinstance(obs, AgentDelegateObservation):
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text = 'OBSERVATION:\n' + truncate_content(
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str(obs.outputs), max_message_chars
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)
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return Message(role='user', content=[TextContent(text=text)])
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elif isinstance(obs, ErrorObservation):
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text = 'OBSERVATION:\n' + truncate_content(obs.content, max_message_chars)
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text += '\n[Error occurred in processing last action]'
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return Message(role='user', content=[TextContent(text=text)])
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else:
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# If an observation message is not returned, it will cause an error
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# when the LLM tries to return the next message
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raise ValueError(f'Unknown observation type: {type(obs)}')
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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 = self._get_messages(state)
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response = self.llm.completion(
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messages=[message.model_dump() for message in 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[Message]:
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messages: list[Message] = [
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Message(
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role='system',
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content=[TextContent(text=self.prompt_manager.system_message)],
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),
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Message(
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role='user',
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content=[TextContent(text=self.prompt_manager.initial_user_message)],
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),
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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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# handle error if the message is the SAME role as the previous message
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# litellm.exceptions.BadRequestError: litellm.BadRequestError: OpenAIException - Error code: 400 - {'detail': 'Only supports u/a/u/a/u...'}
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# there should not have two consecutive messages from the same role
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if messages and messages[-1].role == message.role:
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messages[-1].content.extend(message.content)
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else:
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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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(
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m
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for m in reversed(messages)
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if m.role == 'user'
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and any(isinstance(c, TextContent) for c in m.content)
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),
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None,
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)
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# Get the last user text inside content
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if latest_user_message:
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latest_user_message_text = next(
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(
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t
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for t in reversed(latest_user_message.content)
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if isinstance(t, TextContent)
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)
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)
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# add a reminder to the prompt
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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>.'
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if latest_user_message_text:
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latest_user_message_text.text = (
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latest_user_message_text.text + reminder_text
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)
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else:
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latest_user_message_text = TextContent(text=reminder_text)
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latest_user_message.content.append(latest_user_message_text)
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return messages
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