from __future__ import annotations

import hashlib
import json
import logging
import re
import traceback
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Generic, Literal

from pydantic import BaseModel, ConfigDict, Field, ValidationError, create_model, model_validator
from typing_extensions import TypeVar
from uuid_extensions import uuid7str

from noesium.subagents.bu.adapters.llm_adapter import BaseChatModel
from noesium.subagents.bu.agent.message_manager.views import MessageManagerState
from noesium.subagents.bu.browser.views import BrowserStateHistory
from noesium.subagents.bu.dom.views import DEFAULT_INCLUDE_ATTRIBUTES, DOMInteractedElement, DOMSelectorMap

# from noesium.subagents.bu.dom.history_tree_processor.service import (
# 	DOMElementNode,
# 	DOMHistoryElement,
# 	HistoryTreeProcessor,
# )
# from noesium.subagents.bu.dom.views import SelectorMap
from noesium.subagents.bu.filesystem.file_system import FileSystemState
from noesium.subagents.bu.tokens.views import UsageSummary
from noesium.subagents.bu.tools.registry.views import ActionModel

logger = logging.getLogger(__name__)


class MessageCompactionSettings(BaseModel):
    """Summarizes older history into a compact memory block to reduce prompt size."""

    model_config = ConfigDict(arbitrary_types_allowed=True)

    enabled: bool = True
    compact_every_n_steps: int = 15
    trigger_char_count: int | None = None  # Min char floor; set via trigger_token_count if preferred
    trigger_token_count: int | None = None  # Alternative to trigger_char_count (~4 chars/token)
    chars_per_token: float = 4.0
    keep_last_items: int = 6
    summary_max_chars: int = 6000
    include_read_state: bool = False
    compaction_llm: BaseChatModel | None = None

    @model_validator(mode="after")
    def _resolve_trigger_threshold(self) -> MessageCompactionSettings:
        if self.trigger_char_count is not None and self.trigger_token_count is not None:
            raise ValueError("Set trigger_char_count or trigger_token_count, not both.")
        if self.trigger_token_count is not None:
            self.trigger_char_count = int(self.trigger_token_count * self.chars_per_token)
        elif self.trigger_char_count is None:
            self.trigger_char_count = 40000  # ~10k tokens
        return self


class AgentSettings(BaseModel):
    """Configuration options for the Agent"""

    model_config = ConfigDict(arbitrary_types_allowed=True)

    use_vision: bool | Literal["auto"] = True
    vision_detail_level: Literal["auto", "low", "high"] = "auto"
    save_conversation_path: str | Path | None = None
    save_conversation_path_encoding: str | None = "utf-8"
    max_failures: int = 5
    generate_gif: bool | str = False
    override_system_message: str | None = None
    extend_system_message: str | None = None
    include_attributes: list[str] | None = DEFAULT_INCLUDE_ATTRIBUTES
    max_actions_per_step: int = 5
    use_thinking: bool = True
    flash_mode: bool = (
        False  # If enabled, disables evaluation_previous_goal and next_goal, and sets use_thinking = False
    )
    use_judge: bool = True
    ground_truth: str | None = None  # Ground truth answer or criteria for judge validation
    max_history_items: int | None = None
    message_compaction: MessageCompactionSettings | None = None
    enable_planning: bool = True
    planning_replan_on_stall: int = 3  # consecutive failures before replan nudge; 0 = disabled
    planning_exploration_limit: int = 5  # steps without a plan before nudge; 0 = disabled

    page_extraction_llm: BaseChatModel | None = None
    calculate_cost: bool = False
    include_tool_call_examples: bool = False
    llm_timeout: int = 60  # Timeout in seconds for LLM calls (auto-detected: 30s for gemini, 90s for o3, 60s default)
    step_timeout: int = 180  # Timeout in seconds for each step
    final_response_after_failure: bool = True  # If True, attempt one final recovery call after max_failures

    # Loop detection settings
    loop_detection_window: int = 20  # Rolling window size for action similarity tracking
    loop_detection_enabled: bool = True  # Whether to enable loop detection nudges
    max_clickable_elements_length: int = 40000  # Max characters for clickable elements in prompt


class PageFingerprint(BaseModel):
    """Lightweight fingerprint of the browser page state."""

    model_config = ConfigDict(frozen=True)

    url: str
    element_count: int
    text_hash: str  # First 16 chars of SHA-256 of the DOM text representation

    @staticmethod
    def from_browser_state(url: str, dom_text: str, element_count: int) -> PageFingerprint:
        text_hash = hashlib.sha256(dom_text.encode("utf-8", errors="replace")).hexdigest()[:16]
        return PageFingerprint(url=url, element_count=element_count, text_hash=text_hash)


def _normalize_action_for_hash(action_name: str, params: dict[str, Any]) -> str:
    """Normalize action parameters for similarity hashing.

    For search actions: strip minor keyword variations by sorting tokens.
    For click actions: hash by element type + rough text content, ignoring index.
    For navigate: hash by URL domain only.
    For others: hash by action_name + sorted params.
    """
    if action_name == "search":
        query = str(params.get("query", ""))
        # Normalize search: lowercase, sort tokens, collapse whitespace
        tokens = sorted(set(re.sub(r"[^\w\s]", " ", query.lower()).split()))
        engine = params.get("engine", "google")
        return f'search|{engine}|{"|".join(tokens)}'

    if action_name in ("click", "input"):
        # For element-interaction actions, we only use the index (element identity).
        # Two clicks on the same element index are the same action.
        index = params.get("index")
        if action_name == "input":
            text = str(params.get("text", ""))
            # Normalize input text: lowercase, strip whitespace
            return f"input|{index}|{text.strip().lower()}"
        return f"click|{index}"

    if action_name == "navigate":
        url = str(params.get("url", ""))
        # Hash by full URL — navigating to different paths is genuine exploration,
        # only repeated navigation to the exact same URL is a loop signal.
        return f"navigate|{url}"

    if action_name == "scroll":
        direction = "down" if params.get("down", True) else "up"
        index = params.get("index")
        return f"scroll|{direction}|{index}"

    # Default: hash by action name + sorted params (excluding None values)
    filtered = {k: v for k, v in sorted(params.items()) if v is not None}
    return f"{action_name}|{json.dumps(filtered, sort_keys=True, default=str)}"


def compute_action_hash(action_name: str, params: dict[str, Any]) -> str:
    """Compute a stable hash string for an action based on type + normalized parameters."""
    normalized = _normalize_action_for_hash(action_name, params)
    return hashlib.sha256(normalized.encode("utf-8")).hexdigest()[:12]


class ActionLoopDetector(BaseModel):
    """Tracks action repetition and page stagnation to detect behavioral loops.

    This is a soft detection system — it generates context messages for the LLM
    but never blocks actions. The agent can still repeat if it wants to.
    """

    model_config = ConfigDict(arbitrary_types_allowed=True)

    # Rolling window of recent action hashes
    window_size: int = 20
    recent_action_hashes: list[str] = Field(default_factory=list)

    # Page fingerprint tracking for stagnation detection
    recent_page_fingerprints: list[PageFingerprint] = Field(default_factory=list)

    # Current repetition state
    max_repetition_count: int = 0  # Highest count of any single hash in the window
    most_repeated_hash: str | None = None
    consecutive_stagnant_pages: int = 0  # How many consecutive steps had the same page fingerprint

    def record_action(self, action_name: str, params: dict[str, Any]) -> None:
        """Record an action and update repetition statistics."""
        h = compute_action_hash(action_name, params)
        self.recent_action_hashes.append(h)
        # Trim to window size
        if len(self.recent_action_hashes) > self.window_size:
            self.recent_action_hashes = self.recent_action_hashes[-self.window_size :]
        self._update_repetition_stats()

    def record_page_state(self, url: str, dom_text: str, element_count: int) -> None:
        """Record the current page fingerprint and update stagnation count."""
        fp = PageFingerprint.from_browser_state(url, dom_text, element_count)
        if self.recent_page_fingerprints and self.recent_page_fingerprints[-1] == fp:
            self.consecutive_stagnant_pages += 1
        else:
            self.consecutive_stagnant_pages = 0
        self.recent_page_fingerprints.append(fp)
        # Keep only last few fingerprints (no need for a large window)
        if len(self.recent_page_fingerprints) > 5:
            self.recent_page_fingerprints = self.recent_page_fingerprints[-5:]

    def _update_repetition_stats(self) -> None:
        """Recompute max_repetition_count from the current window."""
        if not self.recent_action_hashes:
            self.max_repetition_count = 0
            self.most_repeated_hash = None
            return
        counts: dict[str, int] = {}
        for h in self.recent_action_hashes:
            counts[h] = counts.get(h, 0) + 1
        self.most_repeated_hash = max(counts, key=lambda k: counts[k])
        self.max_repetition_count = counts[self.most_repeated_hash]

    def get_nudge_message(self) -> str | None:
        """Return an escalating awareness nudge based on repetition severity, or None if no loop detected."""
        messages: list[str] = []

        # Action repetition nudges (escalating at 5, 8, 12)
        if self.max_repetition_count >= 12:
            messages.append(
                f"Heads up: you have repeated a similar action {self.max_repetition_count} times "
                f"in the last {len(self.recent_action_hashes)} actions. "
                "If you are making progress with each repetition, keep going. "
                "If not, a different approach might get you there faster."
            )
        elif self.max_repetition_count >= 8:
            messages.append(
                f"Heads up: you have repeated a similar action {self.max_repetition_count} times "
                f"in the last {len(self.recent_action_hashes)} actions. "
                "Are you still making progress with each attempt? "
                "If so, carry on. Otherwise, it might be worth trying a different approach."
            )
        elif self.max_repetition_count >= 5:
            messages.append(
                f"Heads up: you have repeated a similar action {self.max_repetition_count} times "
                f"in the last {len(self.recent_action_hashes)} actions. "
                "If this is intentional and making progress, carry on. "
                "If not, it might be worth reconsidering your approach."
            )

        # Page stagnation nudge
        if self.consecutive_stagnant_pages >= 5:
            messages.append(
                f"The page content has not changed across {self.consecutive_stagnant_pages} consecutive actions. "
                "Your actions might not be having the intended effect. "
                "It could be worth trying a different element or approach."
            )

        if messages:
            return "\n\n".join(messages)
        return None


class AgentState(BaseModel):
    """Holds all state information for an Agent"""

    model_config = ConfigDict(arbitrary_types_allowed=True)

    agent_id: str = Field(default_factory=uuid7str)
    n_steps: int = 1
    consecutive_failures: int = 0
    last_result: list[ActionResult] | None = None
    plan: list[PlanItem] | None = None
    current_plan_item_index: int = 0
    plan_generation_step: int | None = None
    last_model_output: AgentOutput | None = None

    # Pause/resume state (kept serialisable for checkpointing)
    paused: bool = False
    stopped: bool = False
    session_initialized: bool = False  # Track if session events have been dispatched
    follow_up_task: bool = False  # Track if the agent is a follow-up task

    message_manager_state: MessageManagerState = Field(default_factory=MessageManagerState)
    file_system_state: FileSystemState | None = None

    # Loop detection state
    loop_detector: ActionLoopDetector = Field(default_factory=ActionLoopDetector)


@dataclass
class AgentStepInfo:
    step_number: int
    max_steps: int

    def is_last_step(self) -> bool:
        """Check if this is the last step"""
        return self.step_number >= self.max_steps - 1


class JudgementResult(BaseModel):
    """LLM judgement of agent trace"""

    reasoning: str | None = Field(default=None, description="Explanation of the judgement")
    verdict: bool = Field(description="Whether the trace was successful or not")
    failure_reason: str | None = Field(
        default=None,
        description="Max 5 sentences explanation of why the task was not completed successfully in case of failure. If verdict is true, use an empty string.",
    )
    impossible_task: bool = Field(
        default=False,
        description="True if the task was impossible to complete due to vague instructions, broken website, inaccessible links, missing login credentials, or other insurmountable obstacles",
    )
    reached_captcha: bool = Field(
        default=False,
        description="True if the agent encountered captcha challenges during task execution",
    )


class ActionResult(BaseModel):
    """Result of executing an action"""

    # For done action
    is_done: bool | None = False
    success: bool | None = None

    # For trace judgement
    judgement: JudgementResult | None = None

    # Error handling - always include in long term memory
    error: str | None = None

    # Files
    attachments: list[str] | None = None  # Files to display in the done message

    # Images (base64 encoded) - separate from text content for efficient handling
    images: list[dict[str, Any]] | None = None  # [{"name": "file.jpg", "data": "base64_string"}]

    # Always include in long term memory
    long_term_memory: str | None = None  # Memory of this action

    # if update_only_read_state is True we add the extracted_content to the agent context only once for the next step
    # if update_only_read_state is False we add the extracted_content to the agent long term memory if no long_term_memory is provided
    extracted_content: str | None = None
    include_extracted_content_only_once: bool = (
        False  # Whether the extracted content should be used to update the read_state
    )

    # Metadata for observability (e.g., click coordinates)
    metadata: dict | None = None

    # Deprecated
    include_in_memory: bool = False  # whether to include in extracted_content inside long_term_memory

    @model_validator(mode="after")
    def validate_success_requires_done(self):
        """Ensure success=True can only be set when is_done=True"""
        if self.success is True and self.is_done is not True:
            raise ValueError(
                "success=True can only be set when is_done=True. "
                "For regular actions that succeed, leave success as None. "
                "Use success=False only for actions that fail."
            )
        return self


class RerunSummaryAction(BaseModel):
    """AI-generated summary for rerun completion"""

    summary: str = Field(description="Summary of what happened during the rerun")
    success: bool = Field(description="Whether the rerun completed successfully based on visual inspection")
    completion_status: Literal["complete", "partial", "failed"] = Field(
        description="Status of rerun completion: complete (all steps succeeded), partial (some steps succeeded), failed (task did not complete)"
    )


class StepMetadata(BaseModel):
    """Metadata for a single step including timing and token information"""

    step_start_time: float
    step_end_time: float
    step_number: int
    step_interval: float | None = None

    @property
    def duration_seconds(self) -> float:
        """Calculate step duration in seconds"""
        return self.step_end_time - self.step_start_time


class PlanItem(BaseModel):
    text: str
    status: Literal["pending", "current", "done", "skipped"] = "pending"


class AgentBrain(BaseModel):
    thinking: str | None = None
    evaluation_previous_goal: str
    memory: str
    next_goal: str


class AgentOutput(BaseModel):
    model_config = ConfigDict(arbitrary_types_allowed=True, extra="forbid")

    thinking: str | None = None
    evaluation_previous_goal: str | None = None
    memory: str | None = None
    next_goal: str | None = None
    current_plan_item: int | None = None
    plan_update: list[str] | None = None
    action: list[ActionModel] = Field(
        ...,
        json_schema_extra={"min_items": 1},  # Ensure at least one action is provided
    )

    @classmethod
    def model_json_schema(cls, **kwargs):
        schema = super().model_json_schema(**kwargs)
        schema["required"] = ["evaluation_previous_goal", "memory", "next_goal", "action"]
        return schema

    @property
    def current_state(self) -> AgentBrain:
        """For backward compatibility - returns an AgentBrain with the flattened properties"""
        return AgentBrain(
            thinking=self.thinking,
            evaluation_previous_goal=self.evaluation_previous_goal if self.evaluation_previous_goal else "",
            memory=self.memory if self.memory else "",
            next_goal=self.next_goal if self.next_goal else "",
        )

    @staticmethod
    def type_with_custom_actions(custom_actions: type[ActionModel]) -> type[AgentOutput]:
        """Extend actions with custom actions"""

        model_ = create_model(
            "AgentOutput",
            __base__=AgentOutput,
            action=(
                list[custom_actions],  # type: ignore
                Field(..., description="List of actions to execute", json_schema_extra={"min_items": 1}),
            ),
            __module__=AgentOutput.__module__,
        )
        return model_

    @staticmethod
    def type_with_custom_actions_no_thinking(custom_actions: type[ActionModel]) -> type[AgentOutput]:
        """Extend actions with custom actions and exclude thinking field"""

        class AgentOutputNoThinking(AgentOutput):
            @classmethod
            def model_json_schema(cls, **kwargs):
                schema = super().model_json_schema(**kwargs)
                del schema["properties"]["thinking"]
                schema["required"] = ["evaluation_previous_goal", "memory", "next_goal", "action"]
                return schema

        model = create_model(
            "AgentOutput",
            __base__=AgentOutputNoThinking,
            action=(
                list[custom_actions],  # type: ignore
                Field(..., json_schema_extra={"min_items": 1}),
            ),
            __module__=AgentOutputNoThinking.__module__,
        )

        return model

    @staticmethod
    def type_with_custom_actions_flash_mode(custom_actions: type[ActionModel]) -> type[AgentOutput]:
        """Extend actions with custom actions for flash mode - memory and action fields only"""

        class AgentOutputFlashMode(AgentOutput):
            @classmethod
            def model_json_schema(cls, **kwargs):
                schema = super().model_json_schema(**kwargs)
                # Remove thinking, evaluation_previous_goal, next_goal, and plan fields
                del schema["properties"]["thinking"]
                del schema["properties"]["evaluation_previous_goal"]
                del schema["properties"]["next_goal"]
                schema["properties"].pop("current_plan_item", None)
                schema["properties"].pop("plan_update", None)
                # Update required fields to only include remaining properties
                schema["required"] = ["memory", "action"]
                return schema

        model = create_model(
            "AgentOutput",
            __base__=AgentOutputFlashMode,
            action=(
                list[custom_actions],  # type: ignore
                Field(..., json_schema_extra={"min_items": 1}),
            ),
            __module__=AgentOutputFlashMode.__module__,
        )

        return model


class AgentHistory(BaseModel):
    """History item for agent actions"""

    model_output: AgentOutput | None
    result: list[ActionResult]
    state: BrowserStateHistory
    metadata: StepMetadata | None = None
    state_message: str | None = None

    model_config = ConfigDict(arbitrary_types_allowed=True, protected_namespaces=())

    @staticmethod
    def get_interacted_element(
        model_output: AgentOutput, selector_map: DOMSelectorMap
    ) -> list[DOMInteractedElement | None]:
        elements = []
        for action in model_output.action:
            index = action.get_index()
            if index is not None and index in selector_map:
                el = selector_map[index]
                elements.append(DOMInteractedElement.load_from_enhanced_dom_tree(el))
            else:
                elements.append(None)
        return elements

    def _filter_sensitive_data_from_string(
        self, value: str, sensitive_data: dict[str, str | dict[str, str]] | None
    ) -> str:
        """Filter out sensitive data from a string value"""
        if not sensitive_data:
            return value

        # Collect all sensitive values, immediately converting old format to new format
        sensitive_values: dict[str, str] = {}

        # Process all sensitive data entries
        for key_or_domain, content in sensitive_data.items():
            if isinstance(content, dict):
                # Already in new format: {domain: {key: value}}
                for key, val in content.items():
                    if val:  # Skip empty values
                        sensitive_values[key] = val
            elif content:  # Old format: {key: value} - convert to new format internally
                # We treat this as if it was {'http*://*': {key_or_domain: content}}
                sensitive_values[key_or_domain] = content

        # If there are no valid sensitive data entries, just return the original value
        if not sensitive_values:
            return value

        # Replace all valid sensitive data values with their placeholder tags
        for key, val in sensitive_values.items():
            value = value.replace(val, f"<secret>{key}</secret>")

        return value

    def _filter_sensitive_data_from_dict(
        self, data: dict[str, Any], sensitive_data: dict[str, str | dict[str, str]] | None
    ) -> dict[str, Any]:
        """Recursively filter sensitive data from a dictionary"""
        if not sensitive_data:
            return data

        filtered_data = {}
        for key, value in data.items():
            if isinstance(value, str):
                filtered_data[key] = self._filter_sensitive_data_from_string(value, sensitive_data)
            elif isinstance(value, dict):
                filtered_data[key] = self._filter_sensitive_data_from_dict(value, sensitive_data)
            elif isinstance(value, list):
                filtered_data[key] = [
                    (
                        self._filter_sensitive_data_from_string(item, sensitive_data)
                        if isinstance(item, str)
                        else (
                            self._filter_sensitive_data_from_dict(item, sensitive_data)
                            if isinstance(item, dict)
                            else item
                        )
                    )
                    for item in value
                ]
            else:
                filtered_data[key] = value
        return filtered_data

    def model_dump(self, sensitive_data: dict[str, str | dict[str, str]] | None = None, **kwargs) -> dict[str, Any]:
        """Custom serialization handling circular references and filtering sensitive data"""

        # Handle action serialization
        model_output_dump = None
        if self.model_output:
            action_dump = [action.model_dump(exclude_none=True, mode="json") for action in self.model_output.action]

            # Filter sensitive data only from input action parameters if sensitive_data is provided
            if sensitive_data:
                action_dump = [
                    self._filter_sensitive_data_from_dict(action, sensitive_data) if "input" in action else action
                    for action in action_dump
                ]

            model_output_dump = {
                "evaluation_previous_goal": self.model_output.evaluation_previous_goal,
                "memory": self.model_output.memory,
                "next_goal": self.model_output.next_goal,
                "action": action_dump,  # This preserves the actual action data
            }
            # Only include thinking if it's present
            if self.model_output.thinking is not None:
                model_output_dump["thinking"] = self.model_output.thinking
            if self.model_output.current_plan_item is not None:
                model_output_dump["current_plan_item"] = self.model_output.current_plan_item
            if self.model_output.plan_update is not None:
                model_output_dump["plan_update"] = self.model_output.plan_update

        # Handle result serialization - don't filter ActionResult data
        # as it should contain meaningful information for the agent
        result_dump = [r.model_dump(exclude_none=True, mode="json") for r in self.result]

        return {
            "model_output": model_output_dump,
            "result": result_dump,
            "state": self.state.to_dict(),
            "metadata": self.metadata.model_dump() if self.metadata else None,
            "state_message": self.state_message,
        }


AgentStructuredOutput = TypeVar("AgentStructuredOutput", bound=BaseModel)


class AgentHistoryList(BaseModel, Generic[AgentStructuredOutput]):
    """List of AgentHistory messages, i.e. the history of the agent's actions and thoughts."""

    history: list[AgentHistory]
    usage: UsageSummary | None = None

    _output_model_schema: type[AgentStructuredOutput] | None = None

    def total_duration_seconds(self) -> float:
        """Get total duration of all steps in seconds"""
        total = 0.0
        for h in self.history:
            if h.metadata:
                total += h.metadata.duration_seconds
        return total

    def __len__(self) -> int:
        """Return the number of history items"""
        return len(self.history)

    def __str__(self) -> str:
        """Representation of the AgentHistoryList object"""
        return f"AgentHistoryList(all_results={self.action_results()}, all_model_outputs={self.model_actions()})"

    def add_item(self, history_item: AgentHistory) -> None:
        """Add a history item to the list"""
        self.history.append(history_item)

    def __repr__(self) -> str:
        """Representation of the AgentHistoryList object"""
        return self.__str__()

    def save_to_file(self, filepath: str | Path, sensitive_data: dict[str, str | dict[str, str]] | None = None) -> None:
        """Save history to JSON file with proper serialization and optional sensitive data filtering"""
        try:
            Path(filepath).parent.mkdir(parents=True, exist_ok=True)
            data = self.model_dump(sensitive_data=sensitive_data)
            with open(filepath, "w", encoding="utf-8") as f:
                json.dump(data, f, indent=2)
        except Exception as e:
            raise e

    # def save_as_playwright_script(
    # 	self,
    # 	output_path: str | Path,
    # 	sensitive_data_keys: list[str] | None = None,
    # 	browser_config: BrowserConfig | None = None,
    # 	context_config: BrowserContextConfig | None = None,
    # ) -> None:
    # 	"""
    # 	Generates a Playwright script based on the agent's history and saves it to a file.
    # 	Args:
    # 		output_path: The path where the generated Python script will be saved.
    # 		sensitive_data_keys: A list of keys used as placeholders for sensitive data
    # 							 (e.g., ['username_placeholder', 'password_placeholder']).
    # 							 These will be loaded from environment variables in the
    # 							 generated script.
    # 		browser_config: Configuration of the original Browser instance.
    # 		context_config: Configuration of the original BrowserContext instance.
    # 	"""
    # 	from noesium.subagents.bu.agent.playwright_script_generator import PlaywrightScriptGenerator

    # 	try:
    # 		serialized_history = self.model_dump()['history']
    # 		generator = PlaywrightScriptGenerator(serialized_history, sensitive_data_keys, browser_config, context_config)

    # 		script_content = generator.generate_script_content()
    # 		path_obj = Path(output_path)
    # 		path_obj.parent.mkdir(parents=True, exist_ok=True)
    # 		with open(path_obj, 'w', encoding='utf-8') as f:
    # 			f.write(script_content)
    # 	except Exception as e:
    # 		raise e

    def model_dump(self, **kwargs) -> dict[str, Any]:
        """Custom serialization that properly uses AgentHistory's model_dump"""
        return {
            "history": [h.model_dump(**kwargs) for h in self.history],
        }

    @classmethod
    def load_from_dict(cls, data: dict[str, Any], output_model: type[AgentOutput]) -> AgentHistoryList:
        # loop through history and validate output_model actions to enrich with custom actions
        for h in data["history"]:
            if h["model_output"]:
                if isinstance(h["model_output"], dict):
                    h["model_output"] = output_model.model_validate(h["model_output"])
                else:
                    h["model_output"] = None
            if "interacted_element" not in h["state"]:
                h["state"]["interacted_element"] = None

        history = cls.model_validate(data)
        return history

    @classmethod
    def load_from_file(cls, filepath: str | Path, output_model: type[AgentOutput]) -> AgentHistoryList:
        """Load history from JSON file"""
        with open(filepath, encoding="utf-8") as f:
            data = json.load(f)
        return cls.load_from_dict(data, output_model)

    def last_action(self) -> None | dict:
        """Last action in history"""
        if self.history and self.history[-1].model_output:
            return self.history[-1].model_output.action[-1].model_dump(exclude_none=True, mode="json")
        return None

    def errors(self) -> list[str | None]:
        """Get all errors from history, with None for steps without errors"""
        errors = []
        for h in self.history:
            step_errors = [r.error for r in h.result if r.error]

            # each step can have only one error
            errors.append(step_errors[0] if step_errors else None)
        return errors

    def final_result(self) -> None | str:
        """Final result from history"""
        if self.history and self.history[-1].result[-1].extracted_content:
            return self.history[-1].result[-1].extracted_content
        return None

    def is_done(self) -> bool:
        """Check if the agent is done"""
        if self.history and len(self.history[-1].result) > 0:
            last_result = self.history[-1].result[-1]
            return last_result.is_done is True
        return False

    def is_successful(self) -> bool | None:
        """Check if the agent completed successfully - the agent decides in the last step if it was successful or not. None if not done yet."""
        if self.history and len(self.history[-1].result) > 0:
            last_result = self.history[-1].result[-1]
            if last_result.is_done is True:
                return last_result.success
        return None

    def has_errors(self) -> bool:
        """Check if the agent has any non-None errors"""
        return any(error is not None for error in self.errors())

    def judgement(self) -> dict | None:
        """Get the judgement result as a dictionary if it exists"""
        if self.history and len(self.history[-1].result) > 0:
            last_result = self.history[-1].result[-1]
            if last_result.judgement:
                return last_result.judgement.model_dump()
        return None

    def is_judged(self) -> bool:
        """Check if the agent trace has been judged"""
        if self.history and len(self.history[-1].result) > 0:
            last_result = self.history[-1].result[-1]
            return last_result.judgement is not None
        return False

    def is_validated(self) -> bool | None:
        """Check if the judge validated the agent execution (verdict is True). Returns None if not judged yet."""
        if self.history and len(self.history[-1].result) > 0:
            last_result = self.history[-1].result[-1]
            if last_result.judgement:
                return last_result.judgement.verdict
        return None

    def urls(self) -> list[str | None]:
        """Get all unique URLs from history"""
        return [h.state.url if h.state.url is not None else None for h in self.history]

    def screenshot_paths(
        self, n_last: int | None = None, return_none_if_not_screenshot: bool = True
    ) -> list[str | None]:
        """Get all screenshot paths from history"""
        if n_last == 0:
            return []
        if n_last is None:
            if return_none_if_not_screenshot:
                return [h.state.screenshot_path if h.state.screenshot_path is not None else None for h in self.history]
            else:
                return [h.state.screenshot_path for h in self.history if h.state.screenshot_path is not None]
        else:
            if return_none_if_not_screenshot:
                return [
                    h.state.screenshot_path if h.state.screenshot_path is not None else None
                    for h in self.history[-n_last:]
                ]
            else:
                return [h.state.screenshot_path for h in self.history[-n_last:] if h.state.screenshot_path is not None]

    def screenshots(self, n_last: int | None = None, return_none_if_not_screenshot: bool = True) -> list[str | None]:
        """Get all screenshots from history as base64 strings"""
        if n_last == 0:
            return []

        history_items = self.history if n_last is None else self.history[-n_last:]
        screenshots = []

        for item in history_items:
            screenshot_b64 = item.state.get_screenshot()
            if screenshot_b64:
                screenshots.append(screenshot_b64)
            else:
                if return_none_if_not_screenshot:
                    screenshots.append(None)
                # If return_none_if_not_screenshot is False, we skip None values

        return screenshots

    def action_names(self) -> list[str]:
        """Get all action names from history"""
        action_names = []
        for action in self.model_actions():
            actions = list(action.keys())
            if actions:
                action_names.append(actions[0])
        return action_names

    def model_thoughts(self) -> list[AgentBrain]:
        """Get all thoughts from history"""
        return [h.model_output.current_state for h in self.history if h.model_output]

    def model_outputs(self) -> list[AgentOutput]:
        """Get all model outputs from history"""
        return [h.model_output for h in self.history if h.model_output]

    # get all actions with params
    def model_actions(self) -> list[dict]:
        """Get all actions from history"""
        outputs = []

        for h in self.history:
            if h.model_output:
                # Guard against None interacted_element before zipping
                interacted_elements = h.state.interacted_element or [None] * len(h.model_output.action)
                for action, interacted_element in zip(h.model_output.action, interacted_elements):
                    output = action.model_dump(exclude_none=True, mode="json")
                    output["interacted_element"] = interacted_element
                    outputs.append(output)
        return outputs

    def action_history(self) -> list[list[dict]]:
        """Get truncated action history with only essential fields"""
        step_outputs = []

        for h in self.history:
            step_actions = []
            if h.model_output:
                # Guard against None interacted_element before zipping
                interacted_elements = h.state.interacted_element or [None] * len(h.model_output.action)
                # Zip actions with interacted elements and results
                for action, interacted_element, result in zip(h.model_output.action, interacted_elements, h.result):
                    action_output = action.model_dump(exclude_none=True, mode="json")
                    action_output["interacted_element"] = interacted_element
                    # Only keep long_term_memory from result
                    action_output["result"] = result.long_term_memory if result and result.long_term_memory else None
                    step_actions.append(action_output)
            step_outputs.append(step_actions)

        return step_outputs

    def action_results(self) -> list[ActionResult]:
        """Get all results from history"""
        results = []
        for h in self.history:
            results.extend([r for r in h.result if r])
        return results

    def extracted_content(self) -> list[str]:
        """Get all extracted content from history"""
        content = []
        for h in self.history:
            content.extend([r.extracted_content for r in h.result if r.extracted_content])
        return content

    def model_actions_filtered(self, include: list[str] | None = None) -> list[dict]:
        """Get all model actions from history as JSON"""
        if include is None:
            include = []
        outputs = self.model_actions()
        result = []
        for o in outputs:
            for i in include:
                if i == list(o.keys())[0]:
                    result.append(o)
        return result

    def number_of_steps(self) -> int:
        """Get the number of steps in the history"""
        return len(self.history)

    def agent_steps(self) -> list[str]:
        """Format agent history as readable step descriptions for judge evaluation."""
        steps = []

        # Iterate through history items (each is an AgentHistory)
        for i, h in enumerate(self.history):
            step_text = f"Step {i + 1}:\n"

            # Get actions from model_output
            if h.model_output and h.model_output.action:
                # Use model_dump with mode='json' to serialize enums properly
                actions_list = [action.model_dump(exclude_none=True, mode="json") for action in h.model_output.action]
                action_json = json.dumps(actions_list, indent=1)
                step_text += f"Actions: {action_json}\n"

            # Get results (already a list[ActionResult] in h.result)
            if h.result:
                for j, result in enumerate(h.result):
                    if result.extracted_content:
                        content = str(result.extracted_content)
                        step_text += f"Result {j + 1}: {content}\n"

                    if result.error:
                        error = str(result.error)
                        step_text += f"Error {j + 1}: {error}\n"

            steps.append(step_text)

        return steps

    @property
    def structured_output(self) -> AgentStructuredOutput | None:
        """Get the structured output from the history

        Returns:
                The structured output if both final_result and _output_model_schema are available,
                otherwise None
        """
        final_result = self.final_result()
        if final_result is not None and self._output_model_schema is not None:
            return self._output_model_schema.model_validate_json(final_result)

        return None

    def get_structured_output(self, output_model: type[AgentStructuredOutput]) -> AgentStructuredOutput | None:
        """Get the structured output from history, parsing with the provided schema.

        Use this method when accessing structured output from sandbox execution,
        since the _output_model_schema private attribute is not preserved during serialization.

        Args:
                output_model: The Pydantic model class to parse the output with

        Returns:
                The parsed structured output, or None if no final result exists
        """
        final_result = self.final_result()
        if final_result is not None:
            return output_model.model_validate_json(final_result)
        return None


class AgentError:
    """Container for agent error handling"""

    VALIDATION_ERROR = "Invalid model output format. Please follow the correct schema."
    RATE_LIMIT_ERROR = "Rate limit reached. Waiting before retry."
    NO_VALID_ACTION = "No valid action found"

    @staticmethod
    def format_error(error: Exception, include_trace: bool = False) -> str:
        """Format error message based on error type and optionally include trace"""
        if isinstance(error, ValidationError):
            return f"{AgentError.VALIDATION_ERROR}\nDetails: {str(error)}"
        # Lazy import to avoid loading openai SDK (~800ms) at module level
        from openai import RateLimitError

        if isinstance(error, RateLimitError):
            return AgentError.RATE_LIMIT_ERROR

        # Handle LLM response validation errors from llm_use
        error_str = str(error)
        if "LLM response missing required fields" in error_str or "Expected format: AgentOutput" in error_str:
            # Extract the main error message without the huge stacktrace
            lines = error_str.split("\n")
            main_error = lines[0] if lines else error_str

            # Provide a clearer error message
            helpful_msg = f"{main_error}\n\nThe previous response had an invalid output structure. Please stick to the required output format. \n\n"

            if include_trace:
                helpful_msg += f"\n\nFull stacktrace:\n{traceback.format_exc()}"

            return helpful_msg

        if include_trace:
            return f"{str(error)}\nStacktrace:\n{traceback.format_exc()}"
        return f"{str(error)}"


class DetectedVariable(BaseModel):
    """A detected variable in agent history"""

    name: str
    original_value: str
    type: str = "string"
    format: str | None = None


class VariableMetadata(BaseModel):
    """Metadata about detected variables in history"""

    detected_variables: dict[str, DetectedVariable] = Field(default_factory=dict)
