[Add] browser-use and main.py

browser-use/browser_use/agent/prompts.py

@@ -0,0 +1,187 @@
+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 ActionResult, AgentStepInfo
+	from browser_use.browser.views import BrowserState
+
+
+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') 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,
+		state: 'BrowserState',
+		result: list['ActionResult'] | None = None,
+		include_attributes: list[str] | None = None,
+		step_info: Optional['AgentStepInfo'] = None,
+	):
+		self.state = state
+		self.result = result
+		self.include_attributes = include_attributes or []
+		self.step_info = step_info
+
+	def get_user_message(self, use_vision: bool = True) -> HumanMessage:
+		elements_text = self.state.element_tree.clickable_elements_to_string(include_attributes=self.include_attributes)
+
+		has_content_above = (self.state.pixels_above or 0) > 0
+		has_content_below = (self.state.pixels_below or 0) > 0
+
+		if elements_text != '':
+			if has_content_above:
+				elements_text = (
+					f'... {self.state.pixels_above} pixels above - scroll or extract content to see more ...\n{elements_text}'
+				)
+			else:
+				elements_text = f'[Start of page]\n{elements_text}'
+			if has_content_below:
+				elements_text = (
+					f'{elements_text}\n... {self.state.pixels_below} pixels below - scroll or extract content to see more ...'
+				)
+			else:
+				elements_text = f'{elements_text}\n[End of page]'
+		else:
+			elements_text = 'empty page'
+
+		if self.step_info:
+			step_info_description = f'Current step: {self.step_info.step_number + 1}/{self.step_info.max_steps}'
+		else:
+			step_info_description = ''
+		time_str = datetime.now().strftime('%Y-%m-%d %H:%M')
+		step_info_description += f'Current date and time: {time_str}'
+
+		state_description = f"""
+[Task history memory ends]
+[Current state starts here]
+The following is one-time information - if you need to remember it write it to memory:
+Current url: {self.state.url}
+Available tabs:
+{self.state.tabs}
+Interactive elements from top layer of the current page inside the viewport:
+{elements_text}
+{step_info_description}
+"""
+
+		if self.result:
+			for i, result in enumerate(self.result):
+				if result.extracted_content:
+					state_description += f'\nAction result {i + 1}/{len(self.result)}: {result.extracted_content}'
+				if result.error:
+					# only use last line of error
+					error = result.error.split('\n')[-1]
+					state_description += f'\nAction error {i + 1}/{len(self.result)}: ...{error}'
+
+		if self.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.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)