Implement Planning (#267)

* add outline of agent

* add plan class

* add initial prompt

* plumb plan through a bit

* refactor state management

* move task into state

* fix errors

* add prompt parsing

* add task actions

* better serialization

* more serialization hacks

* fix fn

* fix recursion error

* refine prompt

* better description of run

* update prompt

* tighter planning mechanism

* prompt tweaks

* fix merge

* fix lint issues

* add error handling for tasks

* add graphic for plans

* remove base_path from file actions

* rename subtask to task

* better planning

* prompt updates for verification

* remove verify field

* ruff

* mypy

* fix actions
This commit is contained in:
Robert Brennan 2024-03-29 11:47:29 -04:00 committed by GitHub
parent 32a3a0259a
commit a6f0c066b5
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GPG Key ID: B5690EEEBB952194
12 changed files with 454 additions and 13 deletions

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@ -4,5 +4,6 @@ load_dotenv()
# Import agents after environment variables are loaded
from . import langchains_agent # noqa: E402
from . import codeact_agent # noqa: E402
from . import planner_agent # noqa: E402
__all__ = ['langchains_agent', 'codeact_agent']
__all__ = ['langchains_agent', 'codeact_agent', 'planner_agent']

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@ -70,12 +70,12 @@ class CodeActAgent(Agent):
def step(self, state: State) -> Action:
if len(self.messages) == 0:
assert state.task, "Expecting instruction to be set"
assert state.plan.main_goal, "Expecting instruction to be set"
self.messages = [
{"role": "system", "content": SYSTEM_MESSAGE},
{"role": "user", "content": state.task},
{"role": "user", "content": state.plan.main_goal},
]
print(colored("===USER:===\n" + state.task, "green"))
print(colored("===USER:===\n" + state.plan.main_goal, "green"))
updated_info = state.updated_info
if updated_info:
for prev_action, obs in updated_info:

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@ -119,7 +119,7 @@ class LangchainsAgent(Agent):
self._initialized = True
def step(self, state: State) -> Action:
self._initialize(state.task)
self._initialize(state.plan.main_goal)
# TODO: make langchains agent use Action & Observation
# completly from ground up
@ -163,7 +163,7 @@ class LangchainsAgent(Agent):
state.updated_info = []
prompt = prompts.get_request_action_prompt(
state.task,
state.plan.main_goal,
self.monologue.get_thoughts(),
state.background_commands_obs,
)

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@ -0,0 +1,4 @@
from opendevin.agent import Agent
from .agent import PlannerAgent
Agent.register("PlannerAgent", PlannerAgent)

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@ -0,0 +1,26 @@
from typing import List
from .prompt import get_prompt, parse_response
from opendevin.agent import Agent
from opendevin.action import AgentFinishAction
from opendevin.llm.llm import LLM
from opendevin.state import State
from opendevin.action import Action
class PlannerAgent(Agent):
def __init__(self, llm: LLM):
super().__init__(llm)
def step(self, state: State) -> Action:
if state.plan.task.state in ['completed', 'verified', 'abandoned']:
return AgentFinishAction()
prompt = get_prompt(state.plan, state.history)
messages = [{"content": prompt, "role": "user"}]
resp = self.llm.completion(messages=messages)
action_resp = resp['choices'][0]['message']['content']
action = parse_response(action_resp)
return action
def search_memory(self, query: str) -> List[str]:
return []

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@ -0,0 +1,242 @@
import json
from typing import List, Tuple, Dict, Type
from opendevin.controller.agent_controller import print_with_indent
from opendevin.plan import Plan
from opendevin.action import Action
from opendevin.observation import Observation
from opendevin.action import (
NullAction,
CmdRunAction,
CmdKillAction,
BrowseURLAction,
FileReadAction,
FileWriteAction,
AgentRecallAction,
AgentThinkAction,
AgentFinishAction,
AgentSummarizeAction,
AddTaskAction,
ModifyTaskAction,
)
from opendevin.observation import (
NullObservation,
)
ACTION_TYPE_TO_CLASS: Dict[str, Type[Action]] = {
"run": CmdRunAction,
"kill": CmdKillAction,
"browse": BrowseURLAction,
"read": FileReadAction,
"write": FileWriteAction,
"recall": AgentRecallAction,
"think": AgentThinkAction,
"summarize": AgentSummarizeAction,
"finish": AgentFinishAction,
"add_task": AddTaskAction,
"modify_task": ModifyTaskAction,
}
HISTORY_SIZE = 10
prompt = """
# Task
You're a diligent software engineer AI. You can't see, draw, or interact with a
browser, but you can read and write files, and you can run commands, and you can think.
You've been given the following task:
%(task)s
## Plan
As you complete this task, you're building a plan and keeping
track of your progress. Here's a JSON representation of your plan:
```json
%(plan)s
```
%(plan_status)s
You're responsible for managing this plan and the status of tasks in
it, by using the `add_task` and `modify_task` actions described below.
If the History below contradicts the state of any of these tasks, you
MUST modify the task using the `modify_task` action described below.
Be sure NOT to duplicate any tasks. Do NOT use the `add_task` action for
a task that's already represented. Every task must be represented only once.
Tasks that are sequential MUST be siblings. They must be added in order
to their parent task.
If you mark a task as 'completed', 'verified', or 'abandoned',
all non-abandoned subtasks will be marked the same way.
So before closing a task this way, you MUST not only be sure that it has
been completed successfully--you must ALSO be sure that all its subtasks
are ready to be marked the same way.
If, and only if, ALL tasks have already been marked verified,
you MUST respond with the `finish` action.
## History
Here is a recent history of actions you've taken in service of this plan,
as well as observations you've made. This only includes the MOST RECENT
ten actions--more happened before that.
```json
%(history)s
```
Your most recent action is at the bottom of that history.
## Action
What is your next thought or action? Your response must be in JSON format.
It must be an object, and it must contain two fields:
* `action`, which is one of the actions below
* `args`, which is a map of key-value pairs, specifying the arguments for that action
* `read` - reads the contents of a file. Arguments:
* `path` - the path of the file to read
* `write` - writes the contents to a file. Arguments:
* `path` - the path of the file to write
* `contents` - the contents to write to the file
* `run` - runs a command on the command line in a Linux shell. Arguments:
* `command` - the command to run
* `background` - if true, run the command in the background, so that other commands can be run concurrently. Useful for e.g. starting a server. You won't be able to see the logs. You don't need to end the command with `&`, just set this to true.
* `kill` - kills a background command
* `id` - the ID of the background command to kill
* `browse` - opens a web page. Arguments:
* `url` - the URL to open
* `think` - make a plan, set a goal, or record your thoughts. Arguments:
* `thought` - the thought to record
* `add_task` - add a task to your plan. Arguments:
* `parent` - the ID of the parent task
* `goal` - the goal of the task
* `subtasks` - a list of subtasks, each of which is a map with a `goal` key.
* `modify_task` - close a task. Arguments:
* `id` - the ID of the task to close
* `state` - set to 'in_progress' to start the task, 'completed' to finish it, 'verified' to assert that it was successful, 'abandoned' to give up on it permanently, or `open` to stop working on it for now.
* `finish` - if ALL of your tasks and subtasks have been verified or abanded, and you're absolutely certain that you've completed your task and have tested your work, use the finish action to stop working.
You MUST take time to think in between read, write, run, browse, and recall actions.
You should never act twice in a row without thinking. But if your last several
actions are all `think` actions, you should consider taking a different action.
What is your next thought or action? Again, you must reply with JSON, and only with JSON.
%(hint)s
"""
def get_prompt(plan: Plan, history: List[Tuple[Action, Observation]]):
plan_str = json.dumps(plan.task.to_dict(), indent=2)
sub_history = history[-HISTORY_SIZE:]
history_dicts = []
latest_action: Action = NullAction()
for action, observation in sub_history:
if not isinstance(action, NullAction):
#if not isinstance(action, ModifyTaskAction) and not isinstance(action, AddTaskAction):
action_dict = action.to_dict()
action_dict["action"] = convert_action(action_dict["action"])
history_dicts.append(action_dict)
latest_action = action
if not isinstance(observation, NullObservation):
observation_dict = observation.to_dict()
observation_dict["observation"] = convert_observation(observation_dict["observation"])
history_dicts.append(observation_dict)
history_str = json.dumps(history_dicts, indent=2)
hint = ""
current_task = plan.get_current_task()
if current_task is not None:
plan_status = f"You're currently working on this task:\n{current_task.goal}."
if len(current_task.subtasks) == 0:
plan_status += "\nIf it's not achievable AND verifiable with a SINGLE action, you MUST break it down into subtasks NOW."
else:
plan_status = "You're not currently working on any tasks. Your next action MUST be to mark a task as in_progress."
hint = plan_status
latest_action_id = convert_action(latest_action.to_dict()["action"])
if current_task is not None:
if latest_action_id == "":
hint = "You haven't taken any actions yet. Start by using `ls` to check out what files you're working with."
elif latest_action_id == "run":
hint = "You should think about the command you just ran, what output it gave, and how that affects your plan."
elif latest_action_id == "read":
hint = "You should think about the file you just read, what you learned from it, and how that affects your plan."
elif latest_action_id == "write":
hint = "You just changed a file. You should think about how it affects your plan."
elif latest_action_id == "browse":
hint = "You should think about the page you just visited, and what you learned from it."
elif latest_action_id == "think":
hint = "Look at your last thought in the history above. What does it suggest? Don't think anymore--take action."
elif latest_action_id == "recall":
hint = "You should think about the information you just recalled, and how it should affect your plan."
elif latest_action_id == "add_task":
hint = "You should think about the next action to take."
elif latest_action_id == "modify_task":
hint = "You should think about the next action to take."
elif latest_action_id == "summarize":
hint = ""
elif latest_action_id == "finish":
hint = ""
print_with_indent("HINT:\n" + hint)
return prompt % {
'task': plan.main_goal,
'plan': plan_str,
'history': history_str,
'hint': hint,
'plan_status': plan_status,
}
def parse_response(response: str) -> Action:
json_start = response.find("{")
json_end = response.rfind("}") + 1
response = response[json_start:json_end]
action_dict = json.loads(response)
if 'content' in action_dict:
# The LLM gets confused here. Might as well be robust
action_dict['contents'] = action_dict.pop('content')
args_dict = action_dict.get("args", {})
action = ACTION_TYPE_TO_CLASS[action_dict["action"]](**args_dict)
return action
def convert_action(action):
if action == "CmdRunAction":
action = "run"
elif action == "CmdKillAction":
action = "kill"
elif action == "BrowseURLAction":
action = "browse"
elif action == "FileReadAction":
action = "read"
elif action == "FileWriteAction":
action = "write"
elif action == "AgentFinishAction":
action = "finish"
elif action == "AgentRecallAction":
action = "recall"
elif action == "AgentThinkAction":
action = "think"
elif action == "AgentSummarizeAction":
action = "summarize"
elif action == "AddTaskAction":
action = "add_task"
elif action == "ModifyTaskAction":
action = "modify_task"
return action
def convert_observation(observation):
if observation == "UserMessageObservation":
observation = "chat"
elif observation == "AgentMessageObservation":
observation = "chat"
elif observation == "CmdOutputObservation":
observation = "run"
elif observation == "FileReadObservation":
observation = "read"
return observation

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@ -3,6 +3,7 @@ from .bash import CmdRunAction, CmdKillAction
from .browse import BrowseURLAction
from .fileop import FileReadAction, FileWriteAction
from .agent import AgentRecallAction, AgentThinkAction, AgentFinishAction, AgentEchoAction, AgentSummarizeAction
from .tasks import AddTaskAction, ModifyTaskAction
actions = (
CmdKillAction,
@ -38,4 +39,6 @@ __all__ = [
"AgentFinishAction",
"AgentEchoAction",
"AgentSummarizeAction",
"AddTaskAction",
"ModifyTaskAction"
]

25
opendevin/action/tasks.py Normal file
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@ -0,0 +1,25 @@
from dataclasses import dataclass, field
from .base import NotExecutableAction
@dataclass
class AddTaskAction(NotExecutableAction):
parent: str
goal: str
subtasks: list = field(default_factory=list)
action: str = "add_task"
@property
def message(self) -> str:
return f"Added task: {self.goal}"
@dataclass
class ModifyTaskAction(NotExecutableAction):
id: str
state: str
action: str = "modify_task"
@property
def message(self) -> str:
return f"Set task {self.id} to {self.state}"

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@ -4,4 +4,4 @@ from .command_manager import CommandManager
__all__ = [
'AgentController',
'CommandManager'
]
]

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@ -1,13 +1,17 @@
import asyncio
from typing import List, Callable
import traceback
from opendevin.plan import Plan
from opendevin.state import State
from opendevin.agent import Agent
from opendevin.action import (
Action,
NullAction,
AgentFinishAction,
AddTaskAction,
ModifyTaskAction
)
from opendevin.observation import (
Observation,
@ -15,10 +19,8 @@ from opendevin.observation import (
NullObservation
)
from .command_manager import CommandManager
def print_with_indent(text: str):
print("\t"+text.replace("\n","\n\t"), flush=True)
@ -51,10 +53,10 @@ class AgentController:
self.state.history.append((action, observation))
self.state.updated_info.append((action, observation))
async def start_loop(self, task: str):
finished = False
self.state = State(task)
plan = Plan(task)
self.state = State(plan)
for i in range(self.max_iterations):
try:
finished = await self.step(i)
@ -70,6 +72,9 @@ class AgentController:
async def step(self, i: int):
print("\n\n==============", flush=True)
print("STEP", i, flush=True)
print_with_indent("\nPLAN:\n")
print_with_indent(self.state.plan.__str__())
log_obs = self.command_manager.get_background_obs()
for obs in log_obs:
self.add_history(NullAction(), obs)
@ -92,10 +97,26 @@ class AgentController:
await self._run_callbacks(action)
if isinstance(action, AgentFinishAction):
finished = isinstance(action, AgentFinishAction)
if finished:
print_with_indent("\nFINISHED")
return True
if isinstance(action, AddTaskAction):
try:
self.state.plan.add_subtask(action.parent, action.goal, action.subtasks)
except Exception as e:
observation = AgentErrorObservation(str(e))
print_with_indent("\nADD TASK ERROR:\n%s" % observation)
traceback.print_exc()
elif isinstance(action, ModifyTaskAction):
try:
self.state.plan.set_subtask_state(action.id, action.state)
except Exception as e:
observation = AgentErrorObservation(str(e))
print_with_indent("\nMODIFY TASK ERROR:\n%s" % observation)
traceback.print_exc()
if action.executable:
try:
observation = action.run(self)

117
opendevin/plan.py Normal file
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@ -0,0 +1,117 @@
from typing import List
OPEN_STATE = 'open'
COMPLETED_STATE = 'completed'
ABANDONED_STATE = 'abandoned'
IN_PROGRESS_STATE = 'in_progress'
VERIFIED_STATE = 'verified'
STATES = [OPEN_STATE, COMPLETED_STATE, ABANDONED_STATE, IN_PROGRESS_STATE, VERIFIED_STATE]
class Task:
id: str
goal: str
parent: "Task | None"
subtasks: List["Task"]
def __init__(self, parent: "Task | None", goal: str, state: str=OPEN_STATE, subtasks: List = []):
if parent is None:
self.id = '0'
else:
self.id = parent.id + '.' + str(len(parent.subtasks))
self.parent = parent
self.goal = goal
self.subtasks = []
for subtask in (subtasks or []):
if isinstance(subtask, Task):
self.subtasks.append(subtask)
else:
goal = subtask.get('goal')
state = subtask.get('state')
subtasks = subtask.get('subtasks')
self.subtasks.append(Task(self, goal, state, subtasks))
self.state = OPEN_STATE
def to_string(self, indent=""):
emoji = ''
if self.state == VERIFIED_STATE:
emoji = ''
elif self.state == COMPLETED_STATE:
emoji = '🟢'
elif self.state == ABANDONED_STATE:
emoji = ''
elif self.state == IN_PROGRESS_STATE:
emoji = '💪'
elif self.state == OPEN_STATE:
emoji = '🔵'
result = indent + emoji + ' ' + self.id + ' ' + self.goal + '\n'
for subtask in self.subtasks:
result += subtask.to_string(indent + ' ')
return result
def to_dict(self):
return {
'id': self.id,
'goal': self.goal,
'state': self.state,
'subtasks': [t.to_dict() for t in self.subtasks]
}
def set_state(self, state):
if state not in STATES:
raise ValueError('Invalid state:' + state)
self.state = state
if state == COMPLETED_STATE or state == ABANDONED_STATE or state == VERIFIED_STATE:
for subtask in self.subtasks:
if subtask.state != ABANDONED_STATE:
subtask.set_state(state)
elif state == IN_PROGRESS_STATE:
if self.parent is not None:
self.parent.set_state(state)
def get_current_task(self) -> "Task | None":
for subtask in self.subtasks:
if subtask.state == IN_PROGRESS_STATE:
return subtask.get_current_task()
if self.state == IN_PROGRESS_STATE:
return self
return None
class Plan:
main_goal: str
task: Task
def __init__(self, task: str):
self.main_goal = task
self.task = Task(parent=None, goal=task, subtasks=[])
def __str__(self):
return self.task.to_string()
def get_task_by_id(self, id: str) -> Task:
try:
parts = [int(p) for p in id.split('.')]
except ValueError:
raise ValueError('Invalid task id, non-integer:' + id)
if parts[0] != 0:
raise ValueError('Invalid task id, must start with 0:' + id)
parts = parts[1:]
task = self.task
for part in parts:
if part >= len(task.subtasks):
raise ValueError('Task does not exist:' + id)
task = task.subtasks[part]
return task
def add_subtask(self, parent_id: str, goal: str, subtasks: List = []):
parent = self.get_task_by_id(parent_id)
child = Task(parent=parent, goal=goal, subtasks=subtasks)
parent.subtasks.append(child)
def set_subtask_state(self, id: str, state: str):
task = self.get_task_by_id(id)
task.set_state(state)
def get_current_task(self):
return self.task.get_current_task()

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@ -1,6 +1,8 @@
from dataclasses import dataclass, field
from typing import List, Tuple
from opendevin.plan import Plan
from opendevin.action import (
Action,
)
@ -11,7 +13,7 @@ from opendevin.observation import (
@dataclass
class State:
task: str
plan: Plan
iteration: int = 0
background_commands_obs: List[CmdOutputObservation] = field(default_factory=list)
history: List[Tuple[Action, Observation]] = field(default_factory=list)