Files
browser-use/browser_use/agent/prompts.py
2025-06-22 17:52:14 +08:00

247 lines
8.7 KiB
Python

import importlib.resources
from datetime import datetime
from typing import TYPE_CHECKING, Optional
from langchain_core.messages import HumanMessage, SystemMessage
if TYPE_CHECKING:
from browser_use.agent.views import AgentStepInfo
from browser_use.browser.views import BrowserStateSummary
from browser_use.filesystem.file_system import FileSystem
class SystemPrompt:
def __init__(
self,
action_description: str,
max_actions_per_step: int = 10,
override_system_message: str | None = None,
extend_system_message: str | None = None,
):
self.default_action_description = action_description
self.max_actions_per_step = max_actions_per_step
prompt = ''
if override_system_message:
prompt = override_system_message
else:
self._load_prompt_template()
prompt = self.prompt_template.format(max_actions=self.max_actions_per_step)
if extend_system_message:
prompt += f'\n{extend_system_message}'
self.system_message = SystemMessage(content=prompt)
def _load_prompt_template(self) -> None:
"""Load the prompt template from the markdown file."""
try:
# This works both in development and when installed as a package
with importlib.resources.files('browser_use.agent').joinpath('system_prompt.md').open('r', encoding='utf-8') as f:
self.prompt_template = f.read()
except Exception as e:
raise RuntimeError(f'Failed to load system prompt template: {e}')
def get_system_message(self) -> SystemMessage:
"""
Get the system prompt for the agent.
Returns:
SystemMessage: Formatted system prompt
"""
return self.system_message
# Functions:
# {self.default_action_description}
# Example:
# {self.example_response()}
# Your AVAILABLE ACTIONS:
# {self.default_action_description}
class AgentMessagePrompt:
def __init__(
self,
browser_state_summary: 'BrowserStateSummary',
file_system: 'FileSystem',
agent_history_description: str | None = None,
read_state_description: str | None = None,
task: str | None = None,
include_attributes: list[str] | None = None,
step_info: Optional['AgentStepInfo'] = None,
page_filtered_actions: str | None = None,
max_clickable_elements_length: int = 40000,
sensitive_data: str | None = None,
):
self.browser_state: 'BrowserStateSummary' = browser_state_summary
self.file_system: 'FileSystem | None' = file_system
self.agent_history_description: str | None = agent_history_description
self.read_state_description: str | None = read_state_description
self.task: str | None = task
self.include_attributes = include_attributes or []
self.step_info = step_info
self.page_filtered_actions: str | None = page_filtered_actions
self.max_clickable_elements_length: int = max_clickable_elements_length
self.sensitive_data: str | None = sensitive_data
assert self.browser_state
def _get_browser_state_description(self) -> str:
elements_text = self.browser_state.element_tree.clickable_elements_to_string(include_attributes=self.include_attributes)
if len(elements_text) > self.max_clickable_elements_length:
elements_text = elements_text[: self.max_clickable_elements_length]
truncated_text = f' (truncated to {self.max_clickable_elements_length} characters)'
else:
truncated_text = ''
has_content_above = (self.browser_state.pixels_above or 0) > 0
has_content_below = (self.browser_state.pixels_below or 0) > 0
if elements_text != '':
if has_content_above:
elements_text = f'... {self.browser_state.pixels_above} pixels above - scroll to see more or extract structured data if you are looking for specific information ...\n{elements_text}'
else:
elements_text = f'[Start of page]\n{elements_text}'
if has_content_below:
elements_text = f'{elements_text}\n... {self.browser_state.pixels_below} pixels below - scroll to see more or extract structured data if you are looking for specific information ...'
else:
elements_text = f'{elements_text}\n[End of page]'
else:
elements_text = 'empty page'
tabs_text = ''
current_tab_candidates = []
# Find tabs that match both URL and title to identify current tab more reliably
for tab in self.browser_state.tabs:
if tab.url == self.browser_state.url and tab.title == self.browser_state.title:
current_tab_candidates.append(tab.page_id)
# If we have exactly one match, mark it as current
# Otherwise, don't mark any tab as current to avoid confusion
current_tab_id = current_tab_candidates[0] if len(current_tab_candidates) == 1 else None
for tab in self.browser_state.tabs:
tabs_text += f'Tab {tab.page_id}: {tab.url} - {tab.title[:30]}\n'
current_tab_text = f'Current tab: {current_tab_id}' if current_tab_id is not None else ''
browser_state = f"""{current_tab_text}
Available tabs:
{tabs_text}
Interactive elements from top layer of the current page inside the viewport{truncated_text}:
{elements_text}
"""
return browser_state
def _get_agent_state_description(self) -> str:
if self.step_info:
step_info_description = f'Step {self.step_info.step_number + 1} of {self.step_info.max_steps} max possible steps\n'
else:
step_info_description = ''
time_str = datetime.now().strftime('%Y-%m-%d %H:%M')
step_info_description += f'Current date and time: {time_str}'
todo_contents = self.file_system.get_todo_contents() if self.file_system else ''
if not len(todo_contents):
todo_contents = '[Current todo.md is empty, fill it with your plan when applicable]'
agent_state = f"""
<user_request>
{self.task}
</user_request>
<file_system>
{self.file_system.describe() if self.file_system else 'No file system available'}
</file_system>
<todo_contents>
{todo_contents}
</todo_contents>
"""
if self.sensitive_data:
agent_state += f'<sensitive_data>\n{self.sensitive_data}\n</sensitive_data>\n'
agent_state += f'<step_info>\n{step_info_description}\n</step_info>\n'
return agent_state
def get_user_message(self, use_vision: bool = True) -> HumanMessage:
state_description = (
'<agent_history>\n'
+ (self.agent_history_description.strip('\n') if self.agent_history_description else '')
+ '\n</agent_history>\n'
)
state_description += '<agent_state>\n' + self._get_agent_state_description().strip('\n') + '\n</agent_state>\n'
state_description += '<browser_state>\n' + self._get_browser_state_description().strip('\n') + '\n</browser_state>\n'
state_description += (
'<read_state>\n'
+ (self.read_state_description.strip('\n') if self.read_state_description else '')
+ '\n</read_state>\n'
)
if self.page_filtered_actions:
state_description += 'For this page, these additional actions are available:\n'
state_description += self.page_filtered_actions + '\n'
if self.browser_state.screenshot and use_vision is True:
# Format message for vision model
return HumanMessage(
content=[
{'type': 'text', 'text': state_description},
{
'type': 'image_url',
'image_url': {'url': f'data:image/png;base64,{self.browser_state.screenshot}'}, # , 'detail': 'low'
},
]
)
return HumanMessage(content=state_description)
class PlannerPrompt(SystemPrompt):
def __init__(self, available_actions: str):
self.available_actions = available_actions
def get_system_message(
self, is_planner_reasoning: bool, extended_planner_system_prompt: str | None = None
) -> SystemMessage | HumanMessage:
"""Get the system message for the planner.
Args:
is_planner_reasoning: If True, return as HumanMessage for chain-of-thought
extended_planner_system_prompt: Optional text to append to the base prompt
Returns:
SystemMessage or HumanMessage depending on is_planner_reasoning
"""
planner_prompt_text = """
You are a planning agent that helps break down tasks into smaller steps and reason about the current state.
Your role is to:
1. Analyze the current state and history
2. Evaluate progress towards the ultimate goal
3. Identify potential challenges or roadblocks
4. Suggest the next high-level steps to take
Inside your messages, there will be AI messages from different agents with different formats.
Your output format should be always a JSON object with the following fields:
{{
"state_analysis": "Brief analysis of the current state and what has been done so far",
"progress_evaluation": "Evaluation of progress towards the ultimate goal (as percentage and description)",
"challenges": "List any potential challenges or roadblocks",
"next_steps": "List 2-3 concrete next steps to take",
"reasoning": "Explain your reasoning for the suggested next steps"
}}
Ignore the other AI messages output structures.
Keep your responses concise and focused on actionable insights.
"""
if extended_planner_system_prompt:
planner_prompt_text += f'\n{extended_planner_system_prompt}'
if is_planner_reasoning:
return HumanMessage(content=planner_prompt_text)
else:
return SystemMessage(content=planner_prompt_text)