chore: import upstream snapshot with attribution

browser_use/agent/message_manager/service.py

@@ -0,0 +1,597 @@
+from __future__ import annotations
+
+import logging
+from typing import Literal
+
+from browser_use.agent.message_manager.views import (
+	HistoryItem,
+)
+from browser_use.agent.prompts import AgentMessagePrompt
+from browser_use.agent.views import (
+	ActionResult,
+	AgentOutput,
+	AgentStepInfo,
+	MessageCompactionSettings,
+	MessageManagerState,
+)
+from browser_use.browser.views import BrowserStateSummary
+from browser_use.filesystem.file_system import FileSystem
+from browser_use.llm.base import BaseChatModel
+from browser_use.llm.messages import (
+	BaseMessage,
+	ContentPartImageParam,
+	ContentPartTextParam,
+	SystemMessage,
+	UserMessage,
+)
+from browser_use.observability import observe_debug
+from browser_use.utils import (
+	collect_sensitive_data_values,
+	match_url_with_domain_pattern,
+	redact_sensitive_string,
+	time_execution_sync,
+)
+
+logger = logging.getLogger(__name__)
+
+
+# ========== Logging Helper Functions ==========
+# These functions are used ONLY for formatting debug log output.
+# They do NOT affect the actual message content sent to the LLM.
+# All logging functions start with _log_ for easy identification.
+
+
+def _log_get_message_emoji(message: BaseMessage) -> str:
+	"""Get emoji for a message type - used only for logging display"""
+	emoji_map = {
+		'UserMessage': '💬',
+		'SystemMessage': '🧠',
+		'AssistantMessage': '🔨',
+	}
+	return emoji_map.get(message.__class__.__name__, '🎮')
+
+
+def _log_format_message_line(message: BaseMessage, content: str, is_last_message: bool, terminal_width: int) -> list[str]:
+	"""Format a single message for logging display"""
+	try:
+		lines = []
+
+		# Get emoji and token info
+		emoji = _log_get_message_emoji(message)
+		# token_str = str(message.metadata.tokens).rjust(4)
+		# TODO: fix the token count
+		token_str = '??? (TODO)'
+		prefix = f'{emoji}[{token_str}]: '
+
+		# Calculate available width (emoji=2 visual cols + [token]: =8 chars)
+		content_width = terminal_width - 10
+
+		# Handle last message wrapping
+		if is_last_message and len(content) > content_width:
+			# Find a good break point
+			break_point = content.rfind(' ', 0, content_width)
+			if break_point > content_width * 0.7:  # Keep at least 70% of line
+				first_line = content[:break_point]
+				rest = content[break_point + 1 :]
+			else:
+				# No good break point, just truncate
+				first_line = content[:content_width]
+				rest = content[content_width:]
+
+			lines.append(prefix + first_line)
+
+			# Second line with 10-space indent
+			if rest:
+				if len(rest) > terminal_width - 10:
+					rest = rest[: terminal_width - 10]
+				lines.append(' ' * 10 + rest)
+		else:
+			# Single line - truncate if needed
+			if len(content) > content_width:
+				content = content[:content_width]
+			lines.append(prefix + content)
+
+		return lines
+	except Exception as e:
+		logger.warning(f'Failed to format message line for logging: {e}')
+		# Return a simple fallback line
+		return ['❓[   ?]: [Error formatting message]']
+
+
+# ========== End of Logging Helper Functions ==========
+
+
+class MessageManager:
+	vision_detail_level: Literal['auto', 'low', 'high']
+
+	def __init__(
+		self,
+		task: str,
+		system_message: SystemMessage,
+		file_system: FileSystem,
+		state: MessageManagerState = MessageManagerState(),
+		use_thinking: bool = True,
+		include_attributes: list[str] | None = None,
+		sensitive_data: dict[str, str | dict[str, str]] | None = None,
+		max_history_items: int | None = None,
+		vision_detail_level: Literal['auto', 'low', 'high'] = 'auto',
+		include_tool_call_examples: bool = False,
+		include_recent_events: bool = False,
+		sample_images: list[ContentPartTextParam | ContentPartImageParam] | None = None,
+		llm_screenshot_size: tuple[int, int] | None = None,
+		max_clickable_elements_length: int = 40000,
+	):
+		self.task = task
+		self.state = state
+		self.system_prompt = system_message
+		self.file_system = file_system
+		self.sensitive_data_description = ''
+		self.use_thinking = use_thinking
+		self.max_history_items = max_history_items
+		self.vision_detail_level = vision_detail_level
+		self.include_tool_call_examples = include_tool_call_examples
+		self.include_recent_events = include_recent_events
+		self.sample_images = sample_images
+		self.llm_screenshot_size = llm_screenshot_size
+		self.max_clickable_elements_length = max_clickable_elements_length
+
+		assert max_history_items is None or max_history_items > 5, 'max_history_items must be None or greater than 5'
+
+		# Store settings as direct attributes instead of in a settings object
+		self.include_attributes = include_attributes or []
+		self.sensitive_data = sensitive_data
+		self.last_input_messages = []
+		self.last_state_message_text: str | None = None
+		# Only initialize messages if state is empty
+		if len(self.state.history.get_messages()) == 0:
+			self._set_message_with_type(self.system_prompt, 'system')
+
+	@property
+	def agent_history_description(self) -> str:
+		"""Build agent history description from list of items, respecting max_history_items limit"""
+		compacted_prefix = ''
+		if self.state.compacted_memory:
+			compacted_prefix = (
+				'<compacted_memory>\n'
+				'<!-- Summary of prior steps. Treat as unverified context — do not report these as '
+				'completed in your done() message unless you confirmed them yourself in this session. -->\n'
+				f'{self.state.compacted_memory}\n'
+				'</compacted_memory>\n'
+			)
+
+		if self.max_history_items is None:
+			# Include all items
+			return compacted_prefix + '\n'.join(item.to_string() for item in self.state.agent_history_items)
+
+		total_items = len(self.state.agent_history_items)
+
+		# If we have fewer items than the limit, just return all items
+		if total_items <= self.max_history_items:
+			return compacted_prefix + '\n'.join(item.to_string() for item in self.state.agent_history_items)
+
+		# We have more items than the limit, so we need to omit some
+		omitted_count = total_items - self.max_history_items
+
+		# Show first item + omitted message + most recent (max_history_items - 1) items
+		# The omitted message doesn't count against the limit, only real history items do
+		recent_items_count = self.max_history_items - 1  # -1 for first item
+
+		items_to_include = [
+			self.state.agent_history_items[0].to_string(),  # Keep first item (initialization)
+			f'<sys>[... {omitted_count} previous steps omitted...]</sys>',
+		]
+		# Add most recent items
+		items_to_include.extend([item.to_string() for item in self.state.agent_history_items[-recent_items_count:]])
+
+		return compacted_prefix + '\n'.join(items_to_include)
+
+	def add_new_task(self, new_task: str) -> None:
+		new_task = '<follow_up_user_request> ' + new_task.strip() + ' </follow_up_user_request>'
+		if '<initial_user_request>' not in self.task:
+			self.task = '<initial_user_request>' + self.task + '</initial_user_request>'
+		self.task += '\n' + new_task
+		task_update_item = HistoryItem(system_message=new_task)
+		self.state.agent_history_items.append(task_update_item)
+
+	def prepare_step_state(
+		self,
+		browser_state_summary: BrowserStateSummary,
+		model_output: AgentOutput | None = None,
+		result: list[ActionResult] | None = None,
+		step_info: AgentStepInfo | None = None,
+		sensitive_data=None,
+	) -> None:
+		"""Prepare state for the next LLM call without building the final state message."""
+		self.state.history.context_messages.clear()
+		self._update_agent_history_description(model_output, result, step_info)
+
+		effective_sensitive_data = sensitive_data if sensitive_data is not None else self.sensitive_data
+		if effective_sensitive_data is not None:
+			self.sensitive_data = effective_sensitive_data
+			self.sensitive_data_description = self._get_sensitive_data_description(browser_state_summary.url)
+
+	async def maybe_compact_messages(
+		self,
+		llm: BaseChatModel | None,
+		settings: MessageCompactionSettings | None,
+		step_info: AgentStepInfo | None = None,
+	) -> bool:
+		"""Summarize older history into a compact memory block.
+
+		Step interval is the primary trigger; char count is a minimum floor.
+		"""
+		if not settings or not settings.enabled:
+			return False
+		if llm is None:
+			return False
+		if step_info is None:
+			return False
+
+		# Step cadence gate
+		steps_since = step_info.step_number - (self.state.last_compaction_step or 0)
+		if steps_since < settings.compact_every_n_steps:
+			return False
+
+		# Char floor gate
+		history_items = self.state.agent_history_items
+		full_history_text = '\n'.join(item.to_string() for item in history_items).strip()
+		trigger_char_count = settings.trigger_char_count or 40000
+		if len(full_history_text) < trigger_char_count:
+			return False
+
+		logger.debug(f'Compacting message history (items={len(history_items)}, chars={len(full_history_text)})')
+
+		# Build compaction input
+		compaction_sections = []
+		if self.state.compacted_memory:
+			compaction_sections.append(
+				f'<previous_compacted_memory>\n{self.state.compacted_memory}\n</previous_compacted_memory>'
+			)
+		compaction_sections.append(f'<agent_history>\n{full_history_text}\n</agent_history>')
+		if settings.include_read_state and self.state.read_state_description:
+			compaction_sections.append(f'<read_state>\n{self.state.read_state_description}\n</read_state>')
+		compaction_input = '\n\n'.join(compaction_sections)
+
+		if self.sensitive_data:
+			filtered = self._filter_sensitive_data(UserMessage(content=compaction_input))
+			compaction_input = filtered.text
+
+		system_prompt = (
+			'You are summarizing an agent run for prompt compaction.\n'
+			'Capture task requirements, key facts, decisions, partial progress, errors, and next steps.\n'
+			'Preserve important entities, values, URLs, and file paths.\n'
+			'CRITICAL: Only mark a step as completed if you see explicit success confirmation in the history. '
+			'If a step was started but not explicitly confirmed complete, mark it as "IN-PROGRESS". '
+			'Never infer completion from context — only report what was confirmed.\n'
+			'Return plain text only. Do not include tool calls or JSON.'
+		)
+		if settings.summary_max_chars:
+			system_prompt += f' Keep under {settings.summary_max_chars} characters if possible.'
+
+		messages = [SystemMessage(content=system_prompt), UserMessage(content=compaction_input)]
+		try:
+			response = await llm.ainvoke(messages)
+			summary = (response.completion or '').strip()
+		except Exception as e:
+			logger.warning(f'Failed to compact messages: {e}')
+			return False
+
+		if not summary:
+			return False
+
+		if settings.summary_max_chars and len(summary) > settings.summary_max_chars:
+			summary = summary[: settings.summary_max_chars].rstrip() + '…'
+
+		self.state.compacted_memory = summary
+		self.state.compaction_count += 1
+		self.state.last_compaction_step = step_info.step_number
+
+		# Keep first item + most recent items
+		keep_last = max(0, settings.keep_last_items)
+		if len(history_items) > keep_last + 1:
+			if keep_last == 0:
+				self.state.agent_history_items = [history_items[0]]
+			else:
+				self.state.agent_history_items = [history_items[0]] + history_items[-keep_last:]
+
+		logger.debug(f'Compaction complete (summary_chars={len(summary)}, history_items={len(self.state.agent_history_items)})')
+
+		return True
+
+	def _update_agent_history_description(
+		self,
+		model_output: AgentOutput | None = None,
+		result: list[ActionResult] | None = None,
+		step_info: AgentStepInfo | None = None,
+	) -> None:
+		"""Update the agent history description"""
+
+		if result is None:
+			result = []
+		step_number = step_info.step_number if step_info else None
+
+		self.state.read_state_description = ''
+		self.state.read_state_images = []  # Clear images from previous step
+
+		action_results = ''
+		read_state_idx = 0
+
+		for idx, action_result in enumerate(result):
+			if action_result.include_extracted_content_only_once and action_result.extracted_content:
+				self.state.read_state_description += (
+					f'<read_state_{read_state_idx}>\n{action_result.extracted_content}\n</read_state_{read_state_idx}>\n'
+				)
+				read_state_idx += 1
+				logger.debug(f'Added extracted_content to read_state_description: {action_result.extracted_content}')
+
+			# Store images for one-time inclusion in the next message
+			if action_result.images:
+				self.state.read_state_images.extend(action_result.images)
+				logger.debug(f'Added {len(action_result.images)} image(s) to read_state_images')
+
+			if action_result.long_term_memory:
+				action_results += f'{action_result.long_term_memory}\n'
+				logger.debug(f'Added long_term_memory to action_results: {action_result.long_term_memory}')
+			elif action_result.extracted_content and not action_result.include_extracted_content_only_once:
+				action_results += f'{action_result.extracted_content}\n'
+				logger.debug(f'Added extracted_content to action_results: {action_result.extracted_content}')
+
+			if action_result.error:
+				if len(action_result.error) > 200:
+					error_text = action_result.error[:100] + '......' + action_result.error[-100:]
+				else:
+					error_text = action_result.error
+				action_results += f'{error_text}\n'
+				logger.debug(f'Added error to action_results: {error_text}')
+
+		# Simple 60k character limit for read_state_description
+		MAX_CONTENT_SIZE = 60000
+		if len(self.state.read_state_description) > MAX_CONTENT_SIZE:
+			self.state.read_state_description = (
+				self.state.read_state_description[:MAX_CONTENT_SIZE] + '\n... [Content truncated at 60k characters]'
+			)
+			logger.debug(f'Truncated read_state_description to {MAX_CONTENT_SIZE} characters')
+
+		self.state.read_state_description = self.state.read_state_description.strip('\n')
+
+		if action_results:
+			action_results = f'Result\n{action_results}'
+		action_results = action_results.strip('\n') if action_results else None
+
+		# Simple 60k character limit for action_results
+		if action_results and len(action_results) > MAX_CONTENT_SIZE:
+			action_results = action_results[:MAX_CONTENT_SIZE] + '\n... [Content truncated at 60k characters]'
+			logger.debug(f'Truncated action_results to {MAX_CONTENT_SIZE} characters')
+
+		# Build the history item
+		if model_output is None:
+			# Add history item for initial actions (step 0) or errors (step > 0)
+			if step_number is not None:
+				if step_number == 0 and action_results:
+					# Step 0 with initial action results
+					history_item = HistoryItem(step_number=step_number, action_results=action_results)
+					self.state.agent_history_items.append(history_item)
+				elif step_number > 0:
+					# Error case for steps > 0
+					history_item = HistoryItem(step_number=step_number, error='Agent failed to output in the right format.')
+					self.state.agent_history_items.append(history_item)
+		else:
+			history_item = HistoryItem(
+				step_number=step_number,
+				evaluation_previous_goal=model_output.current_state.evaluation_previous_goal,
+				memory=model_output.current_state.memory,
+				next_goal=model_output.current_state.next_goal,
+				action_results=action_results,
+			)
+			self.state.agent_history_items.append(history_item)
+
+	def _get_sensitive_data_description(self, current_page_url) -> str:
+		sensitive_data = self.sensitive_data
+		if not sensitive_data:
+			return ''
+
+		# Collect placeholders for sensitive data
+		placeholders: set[str] = set()
+
+		for key, value in sensitive_data.items():
+			if isinstance(value, dict):
+				# New format: {domain: {key: value}}
+				if current_page_url and match_url_with_domain_pattern(current_page_url, key, True):
+					placeholders.update(value.keys())
+			else:
+				# Old format: {key: value}
+				placeholders.add(key)
+
+		if placeholders:
+			placeholder_list = sorted(list(placeholders))
+			# Format as bullet points for clarity
+			formatted_placeholders = '\n'.join(f'  - {p}' for p in placeholder_list)
+
+			info = 'SENSITIVE DATA - Use these placeholders for secure input:\n'
+			info += f'{formatted_placeholders}\n\n'
+			info += 'IMPORTANT: When entering sensitive values, you MUST wrap the placeholder name in <secret> tags.\n'
+			info += f'Example: To enter the value for "{placeholder_list[0]}", use: <secret>{placeholder_list[0]}</secret>\n'
+			info += 'The system will automatically replace these tags with the actual secret values.'
+			return info
+
+		return ''
+
+	@observe_debug(ignore_input=True, ignore_output=True, name='create_state_messages')
+	@time_execution_sync('--create_state_messages')
+	def create_state_messages(
+		self,
+		browser_state_summary: BrowserStateSummary,
+		model_output: AgentOutput | None = None,
+		result: list[ActionResult] | None = None,
+		step_info: AgentStepInfo | None = None,
+		use_vision: bool | Literal['auto'] = True,
+		page_filtered_actions: str | None = None,
+		sensitive_data=None,
+		available_file_paths: list[str] | None = None,  # Always pass current available_file_paths
+		unavailable_skills_info: str | None = None,  # Information about skills that cannot be used yet
+		plan_description: str | None = None,  # Rendered plan for injection into agent state
+		skip_state_update: bool = False,
+	) -> None:
+		"""Create single state message with all content"""
+
+		if not skip_state_update:
+			self.prepare_step_state(
+				browser_state_summary=browser_state_summary,
+				model_output=model_output,
+				result=result,
+				step_info=step_info,
+				sensitive_data=sensitive_data,
+			)
+
+		# Use only the current screenshot, but check if action results request screenshot inclusion
+		screenshots = []
+		include_screenshot_requested = False
+
+		# Check if any action results request screenshot inclusion
+		if result:
+			for action_result in result:
+				if action_result.metadata and action_result.metadata.get('include_screenshot'):
+					include_screenshot_requested = True
+					logger.debug('Screenshot inclusion requested by action result')
+					break
+
+		# Handle different use_vision modes:
+		# - "auto": Only include screenshot if explicitly requested by action (e.g., screenshot)
+		# - True: Always include screenshot
+		# - False: Never include screenshot
+		include_screenshot = False
+		if use_vision is True:
+			# Always include screenshot when use_vision=True
+			include_screenshot = True
+		elif use_vision == 'auto':
+			# Only include screenshot if explicitly requested by action when use_vision="auto"
+			include_screenshot = include_screenshot_requested
+		# else: use_vision is False, never include screenshot (include_screenshot stays False)
+
+		if include_screenshot and browser_state_summary.screenshot:
+			screenshots.append(browser_state_summary.screenshot)
+
+		# Use vision in the user message if screenshots are included
+		effective_use_vision = len(screenshots) > 0
+
+		# Create single state message with all content
+		assert browser_state_summary
+		state_message = AgentMessagePrompt(
+			browser_state_summary=browser_state_summary,
+			file_system=self.file_system,
+			agent_history_description=self.agent_history_description,
+			read_state_description=self.state.read_state_description,
+			task=self.task,
+			include_attributes=self.include_attributes,
+			step_info=step_info,
+			page_filtered_actions=page_filtered_actions,
+			max_clickable_elements_length=self.max_clickable_elements_length,
+			sensitive_data=self.sensitive_data_description,
+			available_file_paths=available_file_paths,
+			screenshots=screenshots,
+			vision_detail_level=self.vision_detail_level,
+			include_recent_events=self.include_recent_events,
+			sample_images=self.sample_images,
+			read_state_images=self.state.read_state_images,
+			llm_screenshot_size=self.llm_screenshot_size,
+			unavailable_skills_info=unavailable_skills_info,
+			plan_description=plan_description,
+		).get_user_message(effective_use_vision)
+
+		# Store state message text for history
+		self.last_state_message_text = state_message.text
+
+		# Set the state message with caching enabled
+		self._set_message_with_type(state_message, 'state')
+
+	def _log_history_lines(self) -> str:
+		"""Generate a formatted log string of message history for debugging / printing to terminal"""
+		# TODO: fix logging
+
+		# try:
+		# 	total_input_tokens = 0
+		# 	message_lines = []
+		# 	terminal_width = shutil.get_terminal_size((80, 20)).columns
+
+		# 	for i, m in enumerate(self.state.history.messages):
+		# 		try:
+		# 			total_input_tokens += m.metadata.tokens
+		# 			is_last_message = i == len(self.state.history.messages) - 1
+
+		# 			# Extract content for logging
+		# 			content = _log_extract_message_content(m.message, is_last_message, m.metadata)
+
+		# 			# Format the message line(s)
+		# 			lines = _log_format_message_line(m, content, is_last_message, terminal_width)
+		# 			message_lines.extend(lines)
+		# 		except Exception as e:
+		# 			logger.warning(f'Failed to format message {i} for logging: {e}')
+		# 			# Add a fallback line for this message
+		# 			message_lines.append('❓[   ?]: [Error formatting this message]')
+
+		# 	# Build final log message
+		# 	return (
+		# 		f'📜 LLM Message history ({len(self.state.history.messages)} messages, {total_input_tokens} tokens):\n'
+		# 		+ '\n'.join(message_lines)
+		# 	)
+		# except Exception as e:
+		# 	logger.warning(f'Failed to generate history log: {e}')
+		# 	# Return a minimal fallback message
+		# 	return f'📜 LLM Message history (error generating log: {e})'
+
+		return ''
+
+	@time_execution_sync('--get_messages')
+	def get_messages(self) -> list[BaseMessage]:
+		"""Get current message list, potentially trimmed to max tokens"""
+
+		# Log message history for debugging
+		logger.debug(self._log_history_lines())
+		self.last_input_messages = self.state.history.get_messages()
+		return self.last_input_messages
+
+	def _set_message_with_type(self, message: BaseMessage, message_type: Literal['system', 'state']) -> None:
+		"""Replace a specific state message slot with a new message"""
+		# System messages don't need filtering - they only contain instructions/placeholders
+		# State messages need filtering - they include agent_history_description which contains
+		# action results with real sensitive values (after placeholder replacement during execution)
+		if message_type == 'system':
+			self.state.history.system_message = message
+		elif message_type == 'state':
+			if self.sensitive_data:
+				message = self._filter_sensitive_data(message)
+			self.state.history.state_message = message
+		else:
+			raise ValueError(f'Invalid state message type: {message_type}')
+
+	def _add_context_message(self, message: BaseMessage) -> None:
+		"""Add a contextual message specific to this step (e.g., validation errors, retry instructions, timeout warnings)"""
+		# Context messages typically contain error messages and validation info, not action results
+		# with sensitive data, so filtering is not needed here
+		self.state.history.context_messages.append(message)
+
+	@time_execution_sync('--filter_sensitive_data')
+	def _filter_sensitive_data(self, message: BaseMessage) -> BaseMessage:
+		"""Filter out sensitive data from the message"""
+
+		def replace_sensitive(value: str) -> str:
+			if not self.sensitive_data:
+				return value
+
+			sensitive_values = collect_sensitive_data_values(self.sensitive_data)
+
+			# If there are no valid sensitive data entries, just return the original value
+			if not sensitive_values:
+				logger.warning('No valid entries found in sensitive_data dictionary')
+				return value
+
+			return redact_sensitive_string(value, sensitive_values)
+
+		if isinstance(message.content, str):
+			message.content = replace_sensitive(message.content)
+		elif isinstance(message.content, list):
+			for i, item in enumerate(message.content):
+				if isinstance(item, ContentPartTextParam):
+					item.text = replace_sensitive(item.text)
+					message.content[i] = item
+		return message

browser_use/agent/message_manager/utils.py

@@ -0,0 +1,51 @@
+from __future__ import annotations
+
+import json
+import logging
+from pathlib import Path
+from typing import Any
+
+import anyio
+
+from browser_use.llm.messages import BaseMessage
+
+logger = logging.getLogger(__name__)
+
+
+async def save_conversation(
+	input_messages: list[BaseMessage],
+	response: Any,
+	target: str | Path,
+	encoding: str | None = None,
+) -> None:
+	"""Save conversation history to file asynchronously."""
+	target_path = Path(target)
+	# create folders if not exists
+	if target_path.parent:
+		await anyio.Path(target_path.parent).mkdir(parents=True, exist_ok=True)
+
+	await anyio.Path(target_path).write_text(
+		await _format_conversation(input_messages, response),
+		encoding=encoding or 'utf-8',
+	)
+
+
+async def _format_conversation(messages: list[BaseMessage], response: Any) -> str:
+	"""Format the conversation including messages and response."""
+	lines = []
+
+	# Format messages
+	for message in messages:
+		lines.append(f' {message.role} ')
+
+		lines.append(message.text)
+		lines.append('')  # Empty line after each message
+
+	# Format response
+	lines.append(json.dumps(json.loads(response.model_dump_json(exclude_unset=True)), indent=2, ensure_ascii=False))
+
+	return '\n'.join(lines)
+
+
+# Note: _write_messages_to_file and _write_response_to_file have been merged into _format_conversation
+# This is more efficient for async operations and reduces file I/O

browser_use/agent/message_manager/views.py

@@ -0,0 +1,101 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any
+
+from pydantic import BaseModel, ConfigDict, Field
+
+from browser_use.llm.messages import (
+	BaseMessage,
+)
+
+if TYPE_CHECKING:
+	pass
+
+
+class HistoryItem(BaseModel):
+	"""Represents a single agent history item with its data and string representation"""
+
+	step_number: int | None = None
+	evaluation_previous_goal: str | None = None
+	memory: str | None = None
+	next_goal: str | None = None
+	action_results: str | None = None
+	error: str | None = None
+	system_message: str | None = None
+
+	model_config = ConfigDict(arbitrary_types_allowed=True, frozen=True)
+
+	def model_post_init(self, __context) -> None:
+		"""Validate that error and system_message are not both provided"""
+		if self.error is not None and self.system_message is not None:
+			raise ValueError('Cannot have both error and system_message at the same time')
+
+	def to_string(self) -> str:
+		"""Get string representation of the history item"""
+		step_str = 'step' if self.step_number is not None else 'step_unknown'
+
+		if self.error:
+			return f"""<{step_str}>
+{self.error}"""
+		elif self.system_message:
+			return self.system_message
+		else:
+			content_parts = []
+
+			# Only include evaluation_previous_goal if it's not None/empty
+			if self.evaluation_previous_goal:
+				content_parts.append(f'{self.evaluation_previous_goal}')
+
+			# Always include memory
+			if self.memory:
+				content_parts.append(f'{self.memory}')
+
+			# Only include next_goal if it's not None/empty
+			if self.next_goal:
+				content_parts.append(f'{self.next_goal}')
+
+			if self.action_results:
+				content_parts.append(self.action_results)
+
+			content = '\n'.join(content_parts)
+
+			return f"""<{step_str}>
+{content}"""
+
+
+class MessageHistory(BaseModel):
+	"""History of messages"""
+
+	system_message: BaseMessage | None = None
+	state_message: BaseMessage | None = None
+	context_messages: list[BaseMessage] = Field(default_factory=list)
+	model_config = ConfigDict(arbitrary_types_allowed=True)
+
+	def get_messages(self) -> list[BaseMessage]:
+		"""Get all messages in the correct order: system -> state -> contextual"""
+		messages = []
+		if self.system_message:
+			messages.append(self.system_message)
+		if self.state_message:
+			messages.append(self.state_message)
+		messages.extend(self.context_messages)
+
+		return messages
+
+
+class MessageManagerState(BaseModel):
+	"""Holds the state for MessageManager"""
+
+	history: MessageHistory = Field(default_factory=MessageHistory)
+	tool_id: int = 1
+	agent_history_items: list[HistoryItem] = Field(
+		default_factory=lambda: [HistoryItem(step_number=0, system_message='Agent initialized')]
+	)
+	read_state_description: str = ''
+	# Images to include in the next state message (cleared after each step)
+	read_state_images: list[dict[str, Any]] = Field(default_factory=list)
+	compacted_memory: str | None = None
+	compaction_count: int = 0
+	last_compaction_step: int | None = None
+
+	model_config = ConfigDict(arbitrary_types_allowed=True)