import re
from agenthub.codeact_agent.prompt import (
COMMAND_DOCS,
EXAMPLES,
GITHUB_MESSAGE,
SYSTEM_PREFIX,
SYSTEM_SUFFIX,
)
from opendevin.controller.agent import Agent
from opendevin.controller.state.state import State
from opendevin.events.action import (
Action,
AgentFinishAction,
BrowseInteractiveAction,
CmdRunAction,
IPythonRunCellAction,
MessageAction,
)
from opendevin.events.observation import (
BrowserOutputObservation,
CmdOutputObservation,
IPythonRunCellObservation,
)
from opendevin.llm.llm import LLM
from opendevin.runtime.plugins import (
AgentSkillsRequirement,
JupyterRequirement,
PluginRequirement,
)
from opendevin.runtime.tools import RuntimeTool
ENABLE_GITHUB = True
def parse_response(response) -> str:
action = response.choices[0].message.content
for lang in ['bash', 'ipython', 'browse']:
if f'' in action and f'' not in action:
action += f''
return action
def action_to_str(action: Action) -> str:
if isinstance(action, CmdRunAction):
return f'{action.thought}\n\n{action.command}\n'
elif isinstance(action, IPythonRunCellAction):
return f'{action.thought}\n\n{action.code}\n'
elif isinstance(action, BrowseInteractiveAction):
return f'{action.thought}\n\n{action.browser_actions}\n'
elif isinstance(action, MessageAction):
return action.content
return ''
def get_action_message(action: Action) -> dict[str, str] | None:
if (
isinstance(action, BrowseInteractiveAction)
or isinstance(action, CmdRunAction)
or isinstance(action, IPythonRunCellAction)
or isinstance(action, MessageAction)
):
return {
'role': 'user' if action.source == 'user' else 'assistant',
'content': action_to_str(action),
}
return None
def get_observation_message(obs) -> dict[str, str] | None:
if isinstance(obs, CmdOutputObservation):
content = 'OBSERVATION:\n' + truncate_observation(obs.content)
content += (
f'\n[Command {obs.command_id} finished with exit code {obs.exit_code}]]'
)
return {'role': 'user', 'content': content}
elif isinstance(obs, IPythonRunCellObservation):
content = 'OBSERVATION:\n' + obs.content
# replace base64 images with a placeholder
splitted = content.split('\n')
for i, line in enumerate(splitted):
if ' already displayed to user'
)
content = '\n'.join(splitted)
content = truncate_observation(content)
return {'role': 'user', 'content': content}
elif isinstance(obs, BrowserOutputObservation):
content = 'OBSERVATION:\n' + truncate_observation(obs.content)
return {'role': 'user', 'content': content}
return None
def truncate_observation(observation: str, max_chars: int = 10_000) -> str:
"""
Truncate the middle of the observation if it is too long.
"""
if len(observation) <= max_chars:
return observation
half = max_chars // 2
return (
observation[:half]
+ '\n[... Observation truncated due to length ...]\n'
+ observation[-half:]
)
# FIXME: We can tweak these two settings to create MicroAgents specialized toward different area
def get_system_message() -> str:
if ENABLE_GITHUB:
return f'{SYSTEM_PREFIX}\n{GITHUB_MESSAGE}\n\n{COMMAND_DOCS}\n\n{SYSTEM_SUFFIX}'
else:
return f'{SYSTEM_PREFIX}\n\n{COMMAND_DOCS}\n\n{SYSTEM_SUFFIX}'
def get_in_context_example() -> str:
return EXAMPLES
class CodeActAgent(Agent):
VERSION = '1.5'
"""
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/2402.13463), [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).
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.
### Plugin System
To make the CodeAct agent more powerful with only access to `bash` action space, CodeAct agent leverages OpenDevin's plugin system:
- [Jupyter plugin](https://github.com/OpenDevin/OpenDevin/tree/main/opendevin/runtime/plugins/jupyter): for IPython execution via bash command
- [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).
### Demo
https://github.com/OpenDevin/OpenDevin/assets/38853559/f592a192-e86c-4f48-ad31-d69282d5f6ac
*Example of CodeActAgent with `gpt-4-turbo-2024-04-09` performing a data science task (linear regression)*
### Work-in-progress & Next step
[] Support web-browsing
[] Complete the workflow for CodeAct agent to submit Github PRs
"""
sandbox_plugins: list[PluginRequirement] = [
# NOTE: AgentSkillsRequirement need to go before JupyterRequirement, since
# AgentSkillsRequirement provides a lot of Python functions
# and it need to be initialized before Jupyter for Jupyter to use those functions.
AgentSkillsRequirement(),
JupyterRequirement(),
]
runtime_tools: list[RuntimeTool] = [RuntimeTool.BROWSER]
jupyter_kernel_init_code: str = 'from agentskills import *'
system_message: str = get_system_message()
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!"
def __init__(
self,
llm: LLM,
) -> None:
"""
Initializes a new instance of the CodeActAgent class.
Parameters:
- llm (LLM): The llm to be used by this agent
"""
super().__init__(llm)
self.reset()
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 and background commands
Returns:
- CmdRunAction(command) - bash command to run
- IPythonRunCellAction(code) - IPython code to run
- BrowseInteractiveAction(browsergym_command) - BrowserGym commands to run
- MessageAction(content) - Message action to run (e.g. ask for clarification)
- AgentFinishAction() - end the interaction
"""
messages: list[dict[str, str]] = [
{'role': 'system', 'content': self.system_message},
{'role': 'user', 'content': self.in_context_example},
]
for prev_action, obs in state.history:
action_message = get_action_message(prev_action)
if action_message:
messages.append(action_message)
obs_message = get_observation_message(obs)
if obs_message:
messages.append(obs_message)
latest_user_message = [m for m in messages if m['role'] == 'user'][-1]
if latest_user_message:
if latest_user_message['content'].strip() == '/exit':
return AgentFinishAction()
latest_user_message['content'] += (
f'\n\nENVIRONMENT REMINDER: You have {state.max_iterations - state.iteration} turns left to complete the task.'
)
response = self.llm.do_completion(
messages=messages,
stop=[
'',
'',
'',
],
temperature=0.0,
)
action_str: str = parse_response(response)
state.num_of_chars += sum(
len(message['content']) for message in messages
) + len(action_str)
if finish_command := re.search(r'.*', action_str, re.DOTALL):
thought = action_str.replace(finish_command.group(0), '').strip()
return AgentFinishAction(thought=thought)
if bash_command := re.search(
r'(.*?)', action_str, re.DOTALL
):
# remove the command from the action string to get thought
thought = action_str.replace(bash_command.group(0), '').strip()
# a command was found
command_group = bash_command.group(1).strip()
if command_group.strip() == 'exit':
return AgentFinishAction()
return CmdRunAction(command=command_group, thought=thought)
elif python_code := re.search(
r'(.*?)', action_str, re.DOTALL
):
# a code block was found
code_group = python_code.group(1).strip()
thought = action_str.replace(python_code.group(0), '').strip()
return IPythonRunCellAction(
code=code_group,
thought=thought,
kernel_init_code=self.jupyter_kernel_init_code,
)
elif browse_command := re.search(
r'(.*)', action_str, re.DOTALL
):
# BrowserGym actions was found
browse_actions = browse_command.group(1).strip()
thought = action_str.replace(browse_command.group(0), '').strip()
return BrowseInteractiveAction(
browser_actions=browse_actions, thought=thought
)
else:
# We assume the LLM is GOOD enough that when it returns pure natural language
# it want to talk to the user
return MessageAction(content=action_str, wait_for_response=True)
def search_memory(self, query: str) -> list[str]:
raise NotImplementedError('Implement this abstract method')