chatgpt-on-wechat/bridge/agent_bridge.py

"""
Agent Bridge - Integrates Agent system with existing COW bridge
"""

import os
from typing import Optional, List

from agent.protocol import Agent, LLMModel, LLMRequest
from bridge.agent_event_handler import AgentEventHandler
from bridge.agent_initializer import AgentInitializer
from bridge.bridge import Bridge
from bridge.context import Context
from bridge.reply import Reply, ReplyType
from common import const
from common.log import logger
from common.utils import expand_path
from config import conf
from models.openai_compatible_bot import OpenAICompatibleBot


def add_openai_compatible_support(bot_instance):
    """
    Dynamically add OpenAI-compatible tool calling support to a bot instance.
    
    This allows any bot to gain tool calling capability without modifying its code,
    as long as it uses OpenAI-compatible API format.
    
    Note: Some bots like ZHIPUAIBot have native tool calling support and don't need enhancement.
    """
    if hasattr(bot_instance, 'call_with_tools'):
        # Bot already has tool calling support (e.g., ZHIPUAIBot)
        logger.debug(f"[AgentBridge] {type(bot_instance).__name__} already has native tool calling support")
        return bot_instance

    # Create a temporary mixin class that combines the bot with OpenAI compatibility
    class EnhancedBot(bot_instance.__class__, OpenAICompatibleBot):
        """Dynamically enhanced bot with OpenAI-compatible tool calling"""

        def get_api_config(self):
            """
            Infer API config from common configuration patterns.
            Most OpenAI-compatible bots use similar configuration.
            """
            from config import conf

            return {
                'api_key': conf().get("open_ai_api_key"),
                'api_base': conf().get("open_ai_api_base"),
                'model': conf().get("model", "gpt-3.5-turbo"),
                'default_temperature': conf().get("temperature", 0.9),
                'default_top_p': conf().get("top_p", 1.0),
                'default_frequency_penalty': conf().get("frequency_penalty", 0.0),
                'default_presence_penalty': conf().get("presence_penalty", 0.0),
            }

    # Change the bot's class to the enhanced version
    bot_instance.__class__ = EnhancedBot
    logger.info(
        f"[AgentBridge] Enhanced {bot_instance.__class__.__bases__[0].__name__} with OpenAI-compatible tool calling")

    return bot_instance


class AgentLLMModel(LLMModel):
    """
    LLM Model adapter that uses COW's existing bot infrastructure
    """

    _MODEL_BOT_TYPE_MAP = {
        "wenxin": const.BAIDU, "wenxin-4": const.BAIDU,
        "xunfei": const.XUNFEI, const.QWEN: const.QWEN_DASHSCOPE,
        const.QIANFAN: const.QIANFAN,
        const.MODELSCOPE: const.MODELSCOPE,
    }
    _MODEL_PREFIX_MAP = [
        ("qwen", const.QWEN_DASHSCOPE), ("qwq", const.QWEN_DASHSCOPE), ("qvq", const.QWEN_DASHSCOPE),
        ("gemini", const.GEMINI), ("glm", const.ZHIPU_AI), ("claude", const.CLAUDEAPI),
        ("moonshot", const.MOONSHOT), ("kimi", const.MOONSHOT),
        ("doubao", const.DOUBAO), ("deepseek", const.DEEPSEEK),
        ("ernie", const.QIANFAN),
    ]

    def __init__(self, bridge: Bridge, bot_type: str = "chat"):
        super().__init__(model=conf().get("model", const.GPT_41))
        self.bridge = bridge
        self.bot_type = bot_type
        self._bot = None
        self._bot_model = None

    @property
    def model(self):
        return conf().get("model", const.GPT_41)

    @model.setter
    def model(self, value):
        pass

    def _resolve_bot_type(self, model_name: str) -> str:
        """Resolve bot type from model name, matching Bridge.__init__ logic."""
        if conf().get("use_linkai", False) and conf().get("linkai_api_key"):
            return const.LINKAI
        # Support custom bot type configuration
        configured_bot_type = conf().get("bot_type")
        if configured_bot_type:
            return configured_bot_type
       
        if not model_name or not isinstance(model_name, str):
            return const.OPENAI
        if model_name in self._MODEL_BOT_TYPE_MAP:
            return self._MODEL_BOT_TYPE_MAP[model_name]
        if model_name.lower().startswith("minimax") or model_name in ["abab6.5-chat"]:
            return const.MiniMax
        if model_name in [const.QWEN_TURBO, const.QWEN_PLUS, const.QWEN_MAX]:
            return const.QWEN_DASHSCOPE
        if model_name in [const.MOONSHOT, "moonshot-v1-8k", "moonshot-v1-32k", "moonshot-v1-128k"]:
            return const.MOONSHOT
        if conf().get("bot_type") == "modelscope":
            return const.MODELSCOPE
        lowered_model = model_name.lower()
        for prefix, btype in self._MODEL_PREFIX_MAP:
            if lowered_model.startswith(prefix):
                return btype
        return const.OPENAI

    @property
    def bot(self):
        """Lazy load the bot, re-create when model or bot_type changes"""
        from models.bot_factory import create_bot
        cur_model = self.model
        cur_bot_type = self._resolve_bot_type(cur_model)
        if self._bot is None or self._bot_model != cur_model or getattr(self, '_bot_type', None) != cur_bot_type:
            self._bot = create_bot(cur_bot_type)
            self._bot = add_openai_compatible_support(self._bot)
            self._bot_model = cur_model
            self._bot_type = cur_bot_type
        return self._bot

    def call(self, request: LLMRequest):
        """
        Call the model using COW's bot infrastructure
        """
        try:
            # For non-streaming calls, we'll use the existing reply method
            # This is a simplified implementation
            if hasattr(self.bot, 'call_with_tools'):
                # Use tool-enabled call if available
                kwargs = {
                    'messages': request.messages,
                    'tools': getattr(request, 'tools', None),
                    'stream': False,
                    'model': self.model  # Pass model parameter
                }
                # Only pass max_tokens if it's explicitly set
                if request.max_tokens is not None:
                    kwargs['max_tokens'] = request.max_tokens

                # Extract system prompt if present
                system_prompt = getattr(request, 'system', None)
                if system_prompt:
                    kwargs['system'] = system_prompt

                # Pass context metadata to bot
                channel_type = getattr(self, 'channel_type', None) or ''
                if channel_type:
                    kwargs['channel_type'] = channel_type
                session_id = getattr(self, 'session_id', None)
                if session_id:
                    kwargs['session_id'] = session_id

                # Thinking mode is a global toggle independent of the channel.
                # IM channels (WeChat/WeCom/DingTalk/Feishu) won't render the
                # reasoning trace, but still benefit from the higher answer
                # quality the thinking pass produces.
                from config import conf
                thinking_enabled = bool(conf().get("enable_thinking", False))
                kwargs['thinking'] = (
                    {"type": "enabled"} if thinking_enabled
                    else {"type": "disabled"}
                )
                # Reasoning effort is only meaningful when thinking is on.
                # Bots that don't understand the kwarg drop it silently.
                if thinking_enabled:
                    effort = conf().get("reasoning_effort", "high")
                    if effort in ("high", "max"):
                        kwargs['reasoning_effort'] = effort

                response = self.bot.call_with_tools(**kwargs)
                return self._format_response(response)
            else:
                # Fallback to regular call
                # This would need to be implemented based on your specific needs
                raise NotImplementedError("Regular call not implemented yet")
                
        except Exception as e:
            logger.error(f"AgentLLMModel call error: {e}")
            raise
    
    def call_stream(self, request: LLMRequest):
        """
        Call the model with streaming using COW's bot infrastructure
        """
        try:
            if hasattr(self.bot, 'call_with_tools'):
                # Use tool-enabled streaming call if available
                # Extract system prompt if present
                system_prompt = getattr(request, 'system', None)

                # Build kwargs for call_with_tools
                kwargs = {
                    'messages': request.messages,
                    'tools': getattr(request, 'tools', None),
                    'stream': True,
                    'model': self.model  # Pass model parameter
                }

                # Only pass max_tokens if explicitly set, let the bot use its default
                if request.max_tokens is not None:
                    kwargs['max_tokens'] = request.max_tokens

                # Add system prompt if present
                if system_prompt:
                    kwargs['system'] = system_prompt

                # Pass context metadata to bot
                channel_type = getattr(self, 'channel_type', None) or ''
                if channel_type:
                    kwargs['channel_type'] = channel_type
                session_id = getattr(self, 'session_id', None)
                if session_id:
                    kwargs['session_id'] = session_id

                # Thinking mode is a global toggle independent of the channel.
                # IM channels (WeChat/WeCom/DingTalk/Feishu) won't render the
                # reasoning trace, but still benefit from the higher answer
                # quality the thinking pass produces.
                from config import conf
                thinking_enabled = bool(conf().get("enable_thinking", False))
                kwargs['thinking'] = (
                    {"type": "enabled"} if thinking_enabled
                    else {"type": "disabled"}
                )
                # Reasoning effort is only meaningful when thinking is on.
                # Bots that don't understand the kwarg drop it silently.
                if thinking_enabled:
                    effort = conf().get("reasoning_effort", "high")
                    if effort in ("high", "max"):
                        kwargs['reasoning_effort'] = effort

                stream = self.bot.call_with_tools(**kwargs)
                
                # Convert stream format to our expected format
                for chunk in stream:
                    yield self._format_stream_chunk(chunk)
            else:
                bot_type = type(self.bot).__name__
                raise NotImplementedError(f"Bot {bot_type} does not support call_with_tools. Please add the method.")
                
        except Exception as e:
            logger.error(f"AgentLLMModel call_stream error: {e}", exc_info=True)
            raise
    
    def _format_response(self, response):
        """Format Claude response to our expected format"""
        # This would need to be implemented based on Claude's response format
        return response
    
    def _format_stream_chunk(self, chunk):
        """Format Claude stream chunk to our expected format"""
        # This would need to be implemented based on Claude's stream format
        return chunk


class AgentBridge:
    """
    Bridge class that integrates super Agent with COW
    Manages multiple agent instances per session for conversation isolation
    """
    
    def __init__(self, bridge: Bridge):
        self.bridge = bridge
        self.agents = {}  # session_id -> Agent instance mapping
        self.default_agent = None  # For backward compatibility (no session_id)
        self.agent: Optional[Agent] = None
        self.scheduler_initialized = False
        
        # Create helper instances
        self.initializer = AgentInitializer(bridge, self)

        # Eager-start the scheduler so cron tasks fire without waiting
        # for the first user message. init_scheduler is idempotent.
        try:
            from agent.tools.scheduler.integration import init_scheduler
            if init_scheduler(self):
                self.scheduler_initialized = True
        except Exception as e:
            logger.warning(f"[AgentBridge] Eager scheduler init failed: {e}")
    def create_agent(self, system_prompt: str, tools: List = None, **kwargs) -> Agent:
        """
        Create the super agent with COW integration
        
        Args:
            system_prompt: System prompt
            tools: List of tools (optional)
            **kwargs: Additional agent parameters
            
        Returns:
            Agent instance
        """
        # Create LLM model that uses COW's bot infrastructure
        model = AgentLLMModel(self.bridge)
        
        # Default tools if none provided
        if tools is None:
            # Use ToolManager to load all available tools
            from agent.tools import ToolManager
            tool_manager = ToolManager()
            tool_manager.load_tools()
            
            tools = []
            workspace_dir = kwargs.get("workspace_dir")
            for tool_name in tool_manager.tool_classes.keys():
                try:
                    tool = tool_manager.create_tool(tool_name)
                    if tool:
                        if workspace_dir and hasattr(tool, 'cwd'):
                            tool.cwd = workspace_dir
                        tools.append(tool)
                except Exception as e:
                    logger.warning(f"[AgentBridge] Failed to load tool {tool_name}: {e}")
        
        # Create agent instance
        agent = Agent(
            system_prompt=system_prompt,
            description=kwargs.get("description", "AI Super Agent"),
            model=model,
            tools=tools,
            max_steps=kwargs.get("max_steps", 15),
            output_mode=kwargs.get("output_mode", "logger"),
            workspace_dir=kwargs.get("workspace_dir"),
            skill_manager=kwargs.get("skill_manager"),
            enable_skills=kwargs.get("enable_skills", True),
            memory_manager=kwargs.get("memory_manager"),
            max_context_tokens=kwargs.get("max_context_tokens"),
            context_reserve_tokens=kwargs.get("context_reserve_tokens"),
            runtime_info=kwargs.get("runtime_info"),
        )

        # Log skill loading details
        if agent.skill_manager:
            logger.debug(f"[AgentBridge] SkillManager initialized with {len(agent.skill_manager.skills)} skills")

        return agent
    
    def get_agent(self, session_id: str = None) -> Optional[Agent]:
        """
        Get agent instance for the given session
        
        Args:
            session_id: Session identifier (e.g., user_id). If None, returns default agent.
        
        Returns:
            Agent instance for this session
        """
        # If no session_id, use default agent (backward compatibility)
        if session_id is None:
            if self.default_agent is None:
                self._init_default_agent()
            return self.default_agent
        
        # Check if agent exists for this session
        if session_id not in self.agents:
            self._init_agent_for_session(session_id)
        
        return self.agents[session_id]
    
    def _init_default_agent(self):
        """Initialize default super agent"""
        agent = self.initializer.initialize_agent(session_id=None)
        self.default_agent = agent
    
    def _init_agent_for_session(self, session_id: str):
        """Initialize agent for a specific session"""
        agent = self.initializer.initialize_agent(session_id=session_id)
        self.agents[session_id] = agent
    
    def agent_reply(self, query: str, context: Context = None, 
                   on_event=None, clear_history: bool = False) -> Reply:
        """
        Use super agent to reply to a query
        
        Args:
            query: User query
            context: COW context (optional, contains session_id for user isolation)
            on_event: Event callback (optional)
            clear_history: Whether to clear conversation history
            
        Returns:
            Reply object
        """
        session_id = None
        agent = None
        try:
            # Extract session_id from context for user isolation
            if context:
                session_id = context.kwargs.get("session_id") or context.get("session_id")
            
            # Get agent for this session (will auto-initialize if needed)
            agent = self.get_agent(session_id=session_id)
            if not agent:
                return Reply(ReplyType.ERROR, "Failed to initialize super agent")
            
            # Create event handler for logging and channel communication
            event_handler = AgentEventHandler(context=context, original_callback=on_event)
            
            # Filter tools based on context
            original_tools = agent.tools
            filtered_tools = original_tools
            
            # If this is a scheduled task execution, exclude scheduler tool to prevent recursion
            if context and context.get("is_scheduled_task"):
                filtered_tools = [tool for tool in agent.tools if tool.name != "scheduler"]
                agent.tools = filtered_tools
                logger.info(f"[AgentBridge] Scheduled task execution: excluded scheduler tool ({len(filtered_tools)}/{len(original_tools)} tools)")
            else:
                # Attach context to scheduler tool if present
                if context and agent.tools:
                    for tool in agent.tools:
                        if tool.name == "scheduler":
                            try:
                                from agent.tools.scheduler.integration import attach_scheduler_to_tool
                                attach_scheduler_to_tool(tool, context)
                            except Exception as e:
                                logger.warning(f"[AgentBridge] Failed to attach context to scheduler: {e}")
                            break
            
            # Pass context metadata to model for downstream API requests
            if context and hasattr(agent, 'model'):
                agent.model.channel_type = context.get("channel_type", "")
                agent.model.session_id = session_id or ""

            # Store session_id on agent so executor can clear DB on fatal errors
            agent._current_session_id = session_id

            # Bound the in-memory context for scheduler sessions before each run.
            # Scheduler sessions are stable per-task and append every trigger,
            # so without trimming they would grow unbounded across runs and
            # blow up prompt cost. Regular user chats are not touched here —
            # the agent's own context manager handles that path.
            if session_id and session_id.startswith("scheduler_"):
                from config import conf
                scheduler_keep_turns = max(
                    1, int(conf().get("agent_max_context_turns", 20)) // 5
                )
                self._trim_in_memory_to_turns(agent, scheduler_keep_turns)

            try:
                # Use agent's run_stream method with event handler
                response = agent.run_stream(
                    user_message=query,
                    on_event=event_handler.handle_event,
                    clear_history=clear_history
                )
            finally:
                # Restore original tools
                if context and context.get("is_scheduled_task"):
                    agent.tools = original_tools

                # Log execution summary
                event_handler.log_summary()

            # Persist new messages generated during this run
            if session_id:
                channel_type = (context.get("channel_type") or "") if context else ""
                new_messages = getattr(agent, '_last_run_new_messages', [])
                if new_messages:
                    self._persist_messages(session_id, list(new_messages), channel_type)
                else:
                    with agent.messages_lock:
                        msg_count = len(agent.messages)
                    if msg_count == 0:
                        try:
                            from agent.memory import get_conversation_store
                            get_conversation_store().clear_session(session_id)
                            logger.info(f"[AgentBridge] Cleared DB for recovered session: {session_id}")
                        except Exception as e:
                            logger.warning(f"[AgentBridge] Failed to clear DB after recovery: {e}")
            
            # Post-message hot-reload: detect edits to ~/cow/mcp.json and
            # sync any new/removed MCP tools into the live agent in the
            # background. Off the critical path so user latency is unaffected;
            # changes take effect on the user's next message.
            self._schedule_mcp_hot_reload(agent)

            # Check if there are files to send (from send/read tool)
            if hasattr(agent, 'stream_executor') and hasattr(agent.stream_executor, 'files_to_send'):
                files_to_send = agent.stream_executor.files_to_send
                if files_to_send:
                    # Send the first file (for now, handle one file at a time)
                    file_info = files_to_send[0]
                    logger.info(f"[AgentBridge] Sending file: {file_info.get('path')}")
                    
                    # Clear files_to_send for next request
                    agent.stream_executor.files_to_send = []
                    
                    # Return file reply based on file type
                    return self._create_file_reply(file_info, response, context)
            
            return Reply(ReplyType.TEXT, response)
            
        except Exception as e:
            logger.error(f"Agent reply error: {e}")
            # If the agent cleared its messages due to format error / overflow,
            # also purge the DB so the next request starts clean.
            if session_id and agent:
                try:
                    with agent.messages_lock:
                        msg_count = len(agent.messages)
                    if msg_count == 0:
                        from agent.memory import get_conversation_store
                        get_conversation_store().clear_session(session_id)
                        logger.info(f"[AgentBridge] Cleared DB for session after error: {session_id}")
                except Exception as db_err:
                    logger.warning(f"[AgentBridge] Failed to clear DB after error: {db_err}")
            return Reply(ReplyType.ERROR, f"Agent error: {str(e)}")
    
    def _schedule_mcp_hot_reload(self, agent):
        """
        Fire-and-forget: detect mcp.json edits and reconcile the agent's
        tool dict in the background. Runs after the user's reply is sent,
        so any cost (file stat, hash, server boot) never adds to user latency.
        Failures are isolated and never raise into the message pipeline.
        """
        import threading
        from agent.tools import ToolManager

        def _run():
            try:
                tm = ToolManager()
                tm.refresh_mcp_if_changed()
                added, removed = tm.sync_mcp_into_agent(agent)
                if added or removed:
                    logger.info(
                        f"[AgentBridge] Agent tools synced — "
                        f"added={added}, removed={removed}"
                    )
            except Exception as e:
                logger.warning(f"[AgentBridge] MCP hot-reload failed (non-fatal): {e}")

        threading.Thread(target=_run, daemon=True, name="mcp-hot-reload").start()

    def _create_file_reply(self, file_info: dict, text_response: str, context: Context = None) -> Reply:
        """
        Create a reply for sending files
        
        Args:
            file_info: File metadata from read tool
            text_response: Text response from agent
            context: Context object
            
        Returns:
            Reply object for file sending
        """
        file_type = file_info.get("file_type", "file")
        file_path = file_info.get("path")
        
        # For images, use IMAGE_URL type (channel will handle upload)
        if file_type == "image":
            # Convert local path to file:// URL for channel processing
            file_url = f"file://{file_path}"
            logger.info(f"[AgentBridge] Sending image: {file_url}")
            reply = Reply(ReplyType.IMAGE_URL, file_url)
            # Attach text message if present (for channels that support text+image)
            if text_response:
                reply.text_content = text_response  # Store accompanying text
            return reply
        
        # For all file types (document, video, audio), use FILE type
        if file_type in ["document", "video", "audio"]:
            file_url = f"file://{file_path}"
            logger.info(f"[AgentBridge] Sending {file_type}: {file_url}")
            reply = Reply(ReplyType.FILE, file_url)
            reply.file_name = file_info.get("file_name", os.path.basename(file_path))
            # Attach text message if present
            if text_response:
                reply.text_content = text_response
            return reply
        
        # For all other file types (tar.gz, zip, etc.), also use FILE type
        file_url = f"file://{file_path}"
        logger.info(f"[AgentBridge] Sending generic file: {file_url}")
        reply = Reply(ReplyType.FILE, file_url)
        reply.file_name = file_info.get("file_name", os.path.basename(file_path))
        if text_response:
            reply.text_content = text_response
        return reply
    
    def _migrate_config_to_env(self, workspace_root: str):
        """
        Sync API keys from config.json to .env file.
        Adds new keys and updates changed values on each startup.

        Args:
            workspace_root: Workspace directory path (not used, kept for compatibility)
        """
        from config import conf
        import os
        
        key_mapping = {
            "open_ai_api_key": "OPENAI_API_KEY",
            "open_ai_api_base": "OPENAI_API_BASE",
            "gemini_api_key": "GEMINI_API_KEY",
            "claude_api_key": "CLAUDE_API_KEY",
            "linkai_api_key": "LINKAI_API_KEY",
        }
        
        env_file = expand_path("~/.cow/.env")
        
        # Read existing env vars (key -> value)
        existing_env_vars = {}
        if os.path.exists(env_file):
            try:
                with open(env_file, 'r', encoding='utf-8') as f:
                    for line in f:
                        line = line.strip()
                        if line and not line.startswith('#') and '=' in line:
                            key, val = line.split('=', 1)
                            existing_env_vars[key.strip()] = val.strip()
            except Exception as e:
                logger.warning(f"[AgentBridge] Failed to read .env file: {e}")
        
        # Sync config.json values into .env (add/update/remove)
        updated = False
        for config_key, env_key in key_mapping.items():
            raw = conf().get(config_key, "")
            value = raw.strip() if raw else ""
            old_value = existing_env_vars.get(env_key)

            if value:
                if old_value == value:
                    continue
                existing_env_vars[env_key] = value
                os.environ[env_key] = value
                updated = True
            else:
                if old_value is None:
                    continue
                existing_env_vars.pop(env_key, None)
                os.environ.pop(env_key, None)
                updated = True
            updated = True

        if updated:
            try:
                env_dir = os.path.dirname(env_file)
                os.makedirs(env_dir, exist_ok=True)

                with open(env_file, 'w', encoding='utf-8') as f:
                    f.write('# Environment variables for agent\n')
                    f.write('# Auto-managed - synced from config.json on startup\n\n')
                    for key, value in sorted(existing_env_vars.items()):
                        f.write(f'{key}={value}\n')

                logger.info(f"[AgentBridge] Synced API keys from config.json to .env")
            except Exception as e:
                logger.warning(f"[AgentBridge] Failed to sync API keys: {e}")
    
    def _persist_messages(
        self, session_id: str, new_messages: list, channel_type: str = ""
    ) -> None:
        """
        Persist new messages to the conversation store after each agent run.

        Failures are logged but never propagate — they must not interrupt replies.
        """
        if not new_messages:
            return
        try:
            from config import conf
            if not conf().get("conversation_persistence", True):
                return
            # When deep-thinking display is disabled, strip "thinking" content
            # blocks before persisting so they don't resurface on history reload.
            # The in-memory message list keeps them intact for this run's
            # multi-turn LLM context.
            thinking_enabled = bool(conf().get("enable_thinking", False))
        except Exception:
            thinking_enabled = False

        messages_to_store = new_messages
        if not thinking_enabled:
            messages_to_store = self._strip_thinking_blocks(new_messages)

        try:
            from agent.memory import get_conversation_store
            get_conversation_store().append_messages(
                session_id, messages_to_store, channel_type=channel_type
            )
        except Exception as e:
            logger.warning(
                f"[AgentBridge] Failed to persist messages for session={session_id}: {e}"
            )

    # Marker used to identify scheduler-injected user messages so we can apply
    # a sliding window without touching real user turns. The legacy prefix
    # "Scheduled task" (written by the v2 PR) is also recognised when pruning,
    # so old data can be aged out instead of leaking forever.
    _SCHEDULED_MARKER = "[SCHEDULED]"
    _SCHEDULED_LEGACY_MARKERS = ("Scheduled task",)

    def remember_scheduled_output(
        self,
        session_id: str,
        content: str,
        channel_type: str = "",
        task_description: str = "",
    ) -> None:
        """Add the visible output of a scheduled task to the receiver's session.

        Scheduled task execution uses an isolated session so internal planning and
        tool calls do not leak into the user's chat. The final message is still
        part of the conversation from the user's point of view, so keep a small
        visible turn in the receiver session for follow-up questions.

        Configuration:
            scheduler_inject_to_session (bool, default True):
                Master switch. When False, this method is a no-op.
            scheduler_inject_max_per_session (int, default 3):
                Maximum scheduler-injected user/assistant pairs retained per
                session. Older injections are pruned automatically.

        Content is truncated to 2000 chars to prevent a single high-volume task
        from bloating one entry.
        """
        from config import conf
        if not conf().get("scheduler_inject_to_session", True):
            return
        if not session_id or not content:
            return

        max_len = 2000
        if len(content) > max_len:
            content = content[:max_len] + "..."

        user_text = self._SCHEDULED_MARKER
        if task_description:
            user_text = f"{self._SCHEDULED_MARKER} {task_description}"

        messages = [
            {"role": "user", "content": [{"type": "text", "text": user_text}]},
            {"role": "assistant", "content": [{"type": "text", "text": content}]},
        ]

        # Persist first so the new pair gets a stable seq, then prune old
        # scheduler pairs in DB, then sync the in-memory agent.messages buffer.
        self._persist_messages(session_id, messages, channel_type)

        keep_last_n = max(int(conf().get("scheduler_inject_max_per_session", 3) or 0), 0)
        try:
            from agent.memory import get_conversation_store
            deleted = get_conversation_store().prune_scheduled_messages(
                session_id, keep_last_n=keep_last_n
            )
            if deleted:
                logger.debug(
                    f"[AgentBridge] Pruned {deleted} old scheduler messages "
                    f"for session={session_id} (keep_last_n={keep_last_n})"
                )
        except Exception as e:
            logger.warning(
                f"[AgentBridge] Failed to prune scheduled messages "
                f"for session={session_id}: {e}"
            )

        agent = self.agents.get(session_id)
        if agent:
            try:
                with agent.messages_lock:
                    agent.messages.extend(messages)
                    self._prune_scheduled_in_memory(agent, keep_last_n)
            except Exception as e:
                logger.warning(
                    f"[AgentBridge] Failed to update in-memory scheduled output "
                    f"for session={session_id}: {e}"
                )

    @staticmethod
    def _trim_in_memory_to_turns(agent, keep_turns: int) -> None:
        """Bound ``agent.messages`` to the most recent ``keep_turns`` real
        user/assistant turns, dropping older history together with any
        intermediate tool_use/tool_result blocks that belonged to it.

        A "real" user message is any user message whose content is not solely a
        tool_result block — matches the heuristic used elsewhere when filtering
        history (see ``AgentInitializer._filter_text_only_messages``).

        No-op when the session is already within budget. Caller does not need
        to hold the lock; this method acquires it itself.
        """
        if keep_turns <= 0:
            return

        def _is_real_user(msg) -> bool:
            if not isinstance(msg, dict) or msg.get("role") != "user":
                return False
            content = msg.get("content")
            if isinstance(content, list):
                if any(
                    isinstance(b, dict) and b.get("type") == "tool_result"
                    for b in content
                ):
                    return False
                return any(
                    isinstance(b, dict) and b.get("type") == "text" and b.get("text")
                    for b in content
                )
            if isinstance(content, str):
                return bool(content.strip())
            return False

        with agent.messages_lock:
            msgs = agent.messages
            real_user_indices = [i for i, m in enumerate(msgs) if _is_real_user(m)]
            if len(real_user_indices) <= keep_turns:
                return

            # Cut at the (k-th from the end) real user message; keep everything
            # from there onwards so the surviving slice is still a valid
            # user/assistant sequence.
            cut_idx = real_user_indices[-keep_turns]
            if cut_idx == 0:
                return

            kept = msgs[cut_idx:]
            msgs.clear()
            msgs.extend(kept)
            logger.debug(
                f"[AgentBridge] Trimmed in-memory messages to last "
                f"{keep_turns} turns ({len(kept)} messages remain)"
            )

    @classmethod
    def _prune_scheduled_in_memory(cls, agent, keep_last_n: int) -> None:
        """Mirror conversation_store.prune_scheduled_messages on agent.messages.

        Caller must hold ``agent.messages_lock``.
        """
        if keep_last_n < 0:
            keep_last_n = 0

        markers = (cls._SCHEDULED_MARKER,) + cls._SCHEDULED_LEGACY_MARKERS

        def _is_marker_user(msg) -> bool:
            if not isinstance(msg, dict) or msg.get("role") != "user":
                return False
            content = msg.get("content")
            text = ""
            if isinstance(content, str):
                text = content
            elif isinstance(content, list):
                for block in content:
                    if isinstance(block, dict) and block.get("type") == "text":
                        text = block.get("text", "")
                        break
            return any(text.startswith(m) for m in markers)

        msgs = agent.messages
        pair_indices = []  # list of (user_idx, assistant_idx_or_None)
        for idx, msg in enumerate(msgs):
            if not _is_marker_user(msg):
                continue
            assistant_idx = None
            if idx + 1 < len(msgs):
                nxt = msgs[idx + 1]
                if isinstance(nxt, dict) and nxt.get("role") == "assistant":
                    assistant_idx = idx + 1
            pair_indices.append((idx, assistant_idx))

        if len(pair_indices) <= keep_last_n:
            return

        to_drop = pair_indices[: len(pair_indices) - keep_last_n]
        drop_set = set()
        for u_idx, a_idx in to_drop:
            drop_set.add(u_idx)
            if a_idx is not None:
                drop_set.add(a_idx)

        # Rebuild the list in place to keep external references stable.
        kept = [m for i, m in enumerate(msgs) if i not in drop_set]
        msgs.clear()
        msgs.extend(kept)

    @staticmethod
    def _strip_thinking_blocks(messages: list) -> list:
        """Return a shallow copy of messages with assistant "thinking" blocks removed."""
        cleaned = []
        for msg in messages:
            if not isinstance(msg, dict):
                cleaned.append(msg)
                continue
            if msg.get("role") != "assistant":
                cleaned.append(msg)
                continue
            content = msg.get("content")
            if not isinstance(content, list):
                cleaned.append(msg)
                continue
            filtered_blocks = [
                b for b in content
                if not (isinstance(b, dict) and b.get("type") == "thinking")
            ]
            if len(filtered_blocks) == len(content):
                cleaned.append(msg)
            else:
                new_msg = dict(msg)
                new_msg["content"] = filtered_blocks
                cleaned.append(new_msg)
        return cleaned

    def clear_session(self, session_id: str):
        """
        Clear a specific session's agent and conversation history
        
        Args:
            session_id: Session identifier to clear
        """
        if session_id in self.agents:
            logger.info(f"[AgentBridge] Clearing session: {session_id}")
            del self.agents[session_id]
    
    def clear_all_sessions(self):
        """Clear all agent sessions"""
        logger.info(f"[AgentBridge] Clearing all sessions ({len(self.agents)} total)")
        self.agents.clear()
        self.default_agent = None
    
    def refresh_all_skills(self) -> int:
        """
        Refresh skills and conditional tools in all agent instances after
        environment variable changes. This allows hot-reload without restarting.

        Returns:
            Number of agent instances refreshed
        """
        import os
        from dotenv import load_dotenv
        from config import conf

        # Reload environment variables from .env file
        workspace_root = expand_path(conf().get("agent_workspace", "~/cow"))
        env_file = os.path.join(workspace_root, '.env')

        if os.path.exists(env_file):
            load_dotenv(env_file, override=True)
            logger.info(f"[AgentBridge] Reloaded environment variables from {env_file}")

        refreshed_count = 0

        # Collect all agent instances to refresh
        agents_to_refresh = []
        if self.default_agent:
            agents_to_refresh.append(("default", self.default_agent))
        for session_id, agent in self.agents.items():
            agents_to_refresh.append((session_id, agent))

        for label, agent in agents_to_refresh:
            # Refresh skills
            if hasattr(agent, 'skill_manager') and agent.skill_manager:
                agent.skill_manager.refresh_skills()

            # Refresh conditional tools (e.g. web_search depends on API keys)
            self._refresh_conditional_tools(agent)

            refreshed_count += 1

        if refreshed_count > 0:
            logger.info(f"[AgentBridge] Refreshed skills & tools in {refreshed_count} agent instance(s)")

        return refreshed_count

    @staticmethod
    def _refresh_conditional_tools(agent):
        """
        Add or remove conditional tools based on current environment variables.
        For example, web_search should only be present when BOCHA_API_KEY or
        LINKAI_API_KEY is set.
        """
        try:
            from agent.tools.web_search.web_search import WebSearch

            has_tool = any(t.name == "web_search" for t in agent.tools)
            available = WebSearch.is_available()

            if available and not has_tool:
                # API key was added - inject the tool
                tool = WebSearch()
                tool.model = agent.model
                agent.tools.append(tool)
                logger.info("[AgentBridge] web_search tool added (API key now available)")
            elif not available and has_tool:
                # API key was removed - remove the tool
                agent.tools = [t for t in agent.tools if t.name != "web_search"]
                logger.info("[AgentBridge] web_search tool removed (API key no longer available)")
        except Exception as e:
            logger.debug(f"[AgentBridge] Failed to refresh conditional tools: {e}")