Agent/agent/core/runner.py

"""
Agent Runner - Agent 执行引擎

核心职责：
1. 执行 Agent 任务（循环调用 LLM + 工具）
2. 记录执行轨迹（Trace + Messages + GoalTree）
3. 检索和注入记忆（Experience + Skill）
4. 管理执行计划（GoalTree）
5. 支持续跑（continue）和回溯重跑（rewind）

参数分层：
- Infrastructure: AgentRunner 构造时设置（trace_store, llm_call 等）
- RunConfig: 每次 run 时指定（model, trace_id, insert_after 等）
- Messages: OpenAI SDK 格式的任务消息
"""

import json
import logging
import uuid
from dataclasses import dataclass, field
from datetime import datetime
from typing import AsyncIterator, Optional, Dict, Any, List, Callable, Literal, Tuple, Union

from agent.trace.models import Trace, Message
from agent.trace.protocols import TraceStore
from agent.trace.goal_models import GoalTree
from agent.memory.models import Experience, Skill
from agent.memory.protocols import MemoryStore, StateStore
from agent.memory.skill_loader import load_skills_from_dir
from agent.tools import ToolRegistry, get_tool_registry

logger = logging.getLogger(__name__)


# ===== 运行配置 =====

@dataclass
class RunConfig:
    """
    运行参数 — 控制 Agent 如何执行

    分为模型层参数（由上游 agent 或用户决定）和框架层参数（由系统注入）。
    """
    # --- 模型层参数 ---
    model: str = "gpt-4o"
    temperature: float = 0.3
    max_iterations: int = 200
    tools: Optional[List[str]] = None          # None = 全部内置工具

    # --- 框架层参数 ---
    agent_type: str = "default"
    uid: Optional[str] = None
    system_prompt: Optional[str] = None        # None = 从 skills 自动构建
    enable_memory: bool = True
    auto_execute_tools: bool = True
    name: Optional[str] = None                 # 显示名称（空则由 utility_llm 自动生成）

    # --- Trace 控制 ---
    trace_id: Optional[str] = None             # None = 新建
    parent_trace_id: Optional[str] = None      # 子 Agent 专用
    parent_goal_id: Optional[str] = None

    # --- 续跑控制 ---
    insert_after: Optional[int] = None         # 回溯插入点（message sequence）

    # --- 额外 LLM 参数（传给 llm_call 的 **kwargs）---
    extra_llm_params: Dict[str, Any] = field(default_factory=dict)


# 内置工具列表（始终自动加载）
BUILTIN_TOOLS = [
    # 文件操作工具
    "read_file",
    "edit_file",
    "write_file",
    "glob_files",
    "grep_content",

    # 系统工具
    "bash_command",

    # 技能和目标管理
    "skill",
    "list_skills",
    "goal",
    "subagent",

    # 搜索工具
    "search_posts",
    "get_search_suggestions",

    # 沙箱工具
    "sandbox_create_environment",
    "sandbox_run_shell",
    "sandbox_rebuild_with_ports",
    "sandbox_destroy_environment",

    # 浏览器工具
    "browser_navigate_to_url",
    "browser_search_web",
    "browser_go_back",
    "browser_wait",
    "browser_click_element",
    "browser_input_text",
    "browser_send_keys",
    "browser_upload_file",
    "browser_scroll_page",
    "browser_find_text",
    "browser_screenshot",
    "browser_switch_tab",
    "browser_close_tab",
    "browser_get_dropdown_options",
    "browser_select_dropdown_option",
    "browser_extract_content",
    "browser_read_long_content",
    "browser_get_page_html",
    "browser_get_selector_map",
    "browser_get_visual_selector_map",
    "browser_evaluate",
    "browser_ensure_login_with_cookies",
    "browser_wait_for_user_action",
    "browser_done",
    "browser_export_cookies",
    "browser_load_cookies"
]


# ===== 向后兼容 =====

@dataclass
class AgentConfig:
    """[向后兼容] Agent 配置，新代码请使用 RunConfig"""
    agent_type: str = "default"
    max_iterations: int = 200
    enable_memory: bool = True
    auto_execute_tools: bool = True


@dataclass
class CallResult:
    """单次调用结果"""
    reply: str
    tool_calls: Optional[List[Dict]] = None
    trace_id: Optional[str] = None
    step_id: Optional[str] = None
    tokens: Optional[Dict[str, int]] = None
    cost: float = 0.0


# ===== 执行引擎 =====

CONTEXT_INJECTION_INTERVAL = 10  # 每 N 轮注入一次 GoalTree + Collaborators


class AgentRunner:
    """
    Agent 执行引擎

    支持三种运行模式（通过 RunConfig 区分）：
    1. 新建：trace_id=None
    2. 续跑：trace_id=已有ID, insert_after=None
    3. 回溯：trace_id=已有ID, insert_after=N
    """

    def __init__(
        self,
        trace_store: Optional[TraceStore] = None,
        memory_store: Optional[MemoryStore] = None,
        state_store: Optional[StateStore] = None,
        tool_registry: Optional[ToolRegistry] = None,
        llm_call: Optional[Callable] = None,
        utility_llm_call: Optional[Callable] = None,
        config: Optional[AgentConfig] = None,
        skills_dir: Optional[str] = None,
        goal_tree: Optional[GoalTree] = None,
        debug: bool = False,
    ):
        """
        初始化 AgentRunner

        Args:
            trace_store: Trace 存储
            memory_store: Memory 存储（可选）
            state_store: State 存储（可选）
            tool_registry: 工具注册表（默认使用全局注册表）
            llm_call: 主 LLM 调用函数
            utility_llm_call: 轻量 LLM（用于生成任务标题等），可选
            config: [向后兼容] AgentConfig
            skills_dir: Skills 目录路径
            goal_tree: 初始 GoalTree（可选）
            debug: 保留参数（已废弃）
        """
        self.trace_store = trace_store
        self.memory_store = memory_store
        self.state_store = state_store
        self.tools = tool_registry or get_tool_registry()
        self.llm_call = llm_call
        self.utility_llm_call = utility_llm_call
        self.config = config or AgentConfig()
        self.skills_dir = skills_dir
        self.goal_tree = goal_tree
        self.debug = debug

    # ===== 核心公开方法 =====

    async def run(
        self,
        messages: List[Dict],
        config: Optional[RunConfig] = None,
    ) -> AsyncIterator[Union[Trace, Message]]:
        """
        Agent 模式执行（核心方法）

        Args:
            messages: OpenAI SDK 格式的输入消息
                新建: 初始任务消息 [{"role": "user", "content": "..."}]
                续跑: 追加的新消息
                回溯: 在插入点之后追加的消息
            config: 运行配置

        Yields:
            Union[Trace, Message]: Trace 对象（状态变化）或 Message 对象（执行过程）
        """
        if not self.llm_call:
            raise ValueError("llm_call function not provided")

        config = config or RunConfig()
        trace = None

        try:
            # Phase 1: PREPARE TRACE
            trace, goal_tree, sequence = await self._prepare_trace(messages, config)
            yield trace

            # Phase 2: BUILD HISTORY
            history, sequence, created_messages = await self._build_history(
                trace.trace_id, messages, goal_tree, config, sequence
            )
            for msg in created_messages:
                yield msg

            # Phase 3: AGENT LOOP
            async for event in self._agent_loop(trace, history, goal_tree, config, sequence):
                yield event

        except Exception as e:
            logger.error(f"Agent run failed: {e}")
            tid = config.trace_id or (trace.trace_id if trace else None)
            if self.trace_store and tid:
                await self.trace_store.update_trace(
                    tid,
                    status="failed",
                    error_message=str(e),
                    completed_at=datetime.now()
                )
                trace_obj = await self.trace_store.get_trace(tid)
                if trace_obj:
                    yield trace_obj
            raise

    async def run_result(
        self,
        messages: List[Dict],
        config: Optional[RunConfig] = None,
    ) -> Dict[str, Any]:
        """
        结果模式 — 消费 run()，返回结构化结果。

        主要用于 subagent 工具内部。
        """
        last_assistant_text = ""
        final_trace: Optional[Trace] = None

        async for item in self.run(messages=messages, config=config):
            if isinstance(item, Message) and item.role == "assistant":
                content = item.content
                text = ""
                if isinstance(content, dict):
                    text = content.get("text", "") or ""
                elif isinstance(content, str):
                    text = content
                if text and text.strip():
                    last_assistant_text = text
            elif isinstance(item, Trace):
                final_trace = item

        config = config or RunConfig()
        if not final_trace and config.trace_id and self.trace_store:
            final_trace = await self.trace_store.get_trace(config.trace_id)

        status = final_trace.status if final_trace else "unknown"
        error = final_trace.error_message if final_trace else None
        summary = last_assistant_text

        if not summary:
            status = "failed"
            error = error or "Agent 没有产生 assistant 文本结果"

        return {
            "status": status,
            "summary": summary,
            "trace_id": final_trace.trace_id if final_trace else config.trace_id,
            "error": error,
            "stats": {
                "total_messages": final_trace.total_messages if final_trace else 0,
                "total_tokens": final_trace.total_tokens if final_trace else 0,
                "total_cost": final_trace.total_cost if final_trace else 0.0,
            },
        }

    # ===== 单次调用（保留）=====

    async def call(
        self,
        messages: List[Dict],
        model: str = "gpt-4o",
        tools: Optional[List[str]] = None,
        uid: Optional[str] = None,
        trace: bool = True,
        **kwargs
    ) -> CallResult:
        """
        单次 LLM 调用（无 Agent Loop）
        """
        if not self.llm_call:
            raise ValueError("llm_call function not provided")

        trace_id = None
        message_id = None

        tool_names = BUILTIN_TOOLS.copy()
        if tools:
            for tool in tools:
                if tool not in tool_names:
                    tool_names.append(tool)
        tool_schemas = self.tools.get_schemas(tool_names)

        if trace and self.trace_store:
            trace_obj = Trace.create(mode="call", uid=uid, model=model, tools=tool_schemas, llm_params=kwargs)
            trace_id = await self.trace_store.create_trace(trace_obj)

        result = await self.llm_call(messages=messages, model=model, tools=tool_schemas, **kwargs)

        if trace and self.trace_store and trace_id:
            msg = Message.create(
                trace_id=trace_id, role="assistant", sequence=1, goal_id=None,
                content={"text": result.get("content", ""), "tool_calls": result.get("tool_calls")},
                prompt_tokens=result.get("prompt_tokens", 0),
                completion_tokens=result.get("completion_tokens", 0),
                finish_reason=result.get("finish_reason"),
                cost=result.get("cost", 0),
            )
            message_id = await self.trace_store.add_message(msg)
            await self.trace_store.update_trace(trace_id, status="completed", completed_at=datetime.now())

        return CallResult(
            reply=result.get("content", ""),
            tool_calls=result.get("tool_calls"),
            trace_id=trace_id,
            step_id=message_id,
            tokens={"prompt": result.get("prompt_tokens", 0), "completion": result.get("completion_tokens", 0)},
            cost=result.get("cost", 0)
        )

    # ===== Phase 1: PREPARE TRACE =====

    async def _prepare_trace(
        self,
        messages: List[Dict],
        config: RunConfig,
    ) -> Tuple[Trace, Optional[GoalTree], int]:
        """
        准备 Trace：创建新的或加载已有的

        Returns:
            (trace, goal_tree, next_sequence)
        """
        if config.trace_id:
            return await self._prepare_existing_trace(config)
        else:
            return await self._prepare_new_trace(messages, config)

    async def _prepare_new_trace(
        self,
        messages: List[Dict],
        config: RunConfig,
    ) -> Tuple[Trace, Optional[GoalTree], int]:
        """创建新 Trace"""
        trace_id = str(uuid.uuid4())

        # 生成任务名称
        task_name = config.name or await self._generate_task_name(messages)

        # 准备工具 Schema
        tool_schemas = self._get_tool_schemas(config.tools)

        trace_obj = Trace(
            trace_id=trace_id,
            mode="agent",
            task=task_name,
            agent_type=config.agent_type,
            parent_trace_id=config.parent_trace_id,
            parent_goal_id=config.parent_goal_id,
            uid=config.uid,
            model=config.model,
            tools=tool_schemas,
            llm_params={"temperature": config.temperature, **config.extra_llm_params},
            status="running",
        )

        goal_tree = self.goal_tree or GoalTree(mission=task_name)

        if self.trace_store:
            await self.trace_store.create_trace(trace_obj)
            await self.trace_store.update_goal_tree(trace_id, goal_tree)

        return trace_obj, goal_tree, 1

    async def _prepare_existing_trace(
        self,
        config: RunConfig,
    ) -> Tuple[Trace, Optional[GoalTree], int]:
        """加载已有 Trace（续跑或回溯）"""
        if not self.trace_store:
            raise ValueError("trace_store required for continue/rewind")

        trace_obj = await self.trace_store.get_trace(config.trace_id)
        if not trace_obj:
            raise ValueError(f"Trace not found: {config.trace_id}")

        goal_tree = await self.trace_store.get_goal_tree(config.trace_id)

        if config.insert_after is not None:
            # 回溯模式
            sequence = await self._rewind(config.trace_id, config.insert_after, goal_tree)
        else:
            # 续跑模式：从最大 sequence + 1 开始
            all_messages = await self.trace_store.get_trace_messages(
                config.trace_id, include_abandoned=True
            )
            sequence = max((m.sequence for m in all_messages), default=0) + 1

        # 状态置为 running
        await self.trace_store.update_trace(
            config.trace_id,
            status="running",
            completed_at=None,
        )
        trace_obj.status = "running"

        return trace_obj, goal_tree, sequence

    # ===== Phase 2: BUILD HISTORY =====

    async def _build_history(
        self,
        trace_id: str,
        new_messages: List[Dict],
        goal_tree: Optional[GoalTree],
        config: RunConfig,
        sequence: int,
    ) -> Tuple[List[Dict], int, List[Message]]:
        """
        构建完整的 LLM 消息历史

        1. 加载已有 active messages（续跑/回溯场景）
        2. 构建 system prompt（新建时注入 skills/experiences）
        3. 追加 input messages

        Returns:
            (history, next_sequence, created_messages)
            created_messages: 本次新创建并持久化的 Message 列表，供 run() yield 给调用方
        """
        history: List[Dict] = []
        created_messages: List[Message] = []

        # 1. 加载已有 messages
        if config.trace_id and self.trace_store:
            existing_messages = await self.trace_store.get_trace_messages(trace_id)
            history = [msg.to_llm_dict() for msg in existing_messages]

        # 2. 构建 system prompt（如果历史中没有 system message）
        has_system = any(m.get("role") == "system" for m in history)
        has_system_in_new = any(m.get("role") == "system" for m in new_messages)

        if not has_system and not has_system_in_new:
            system_prompt = await self._build_system_prompt(config)
            if system_prompt:
                history = [{"role": "system", "content": system_prompt}] + history

                if self.trace_store:
                    system_msg = Message.create(
                        trace_id=trace_id, role="system", sequence=sequence,
                        goal_id=None, content=system_prompt,
                    )
                    await self.trace_store.add_message(system_msg)
                    created_messages.append(system_msg)
                    sequence += 1

        # 3. 追加新 messages
        for msg_dict in new_messages:
            history.append(msg_dict)

            if self.trace_store:
                stored_msg = Message.from_llm_dict(
                    msg_dict, trace_id=trace_id, sequence=sequence, goal_id=None
                )
                await self.trace_store.add_message(stored_msg)
                created_messages.append(stored_msg)
                sequence += 1

        return history, sequence, created_messages

    # ===== Phase 3: AGENT LOOP =====

    async def _agent_loop(
        self,
        trace: Trace,
        history: List[Dict],
        goal_tree: Optional[GoalTree],
        config: RunConfig,
        sequence: int,
    ) -> AsyncIterator[Union[Trace, Message]]:
        """ReAct 循环"""
        trace_id = trace.trace_id
        tool_schemas = self._get_tool_schemas(config.tools)

        # 设置 goal_tree 到 goal 工具
        if goal_tree and self.trace_store:
            from agent.trace.goal_tool import set_goal_tree
            set_goal_tree(goal_tree)

        for iteration in range(config.max_iterations):
            # 构建 LLM messages（注入上下文）
            llm_messages = list(history)

            # 周期性注入 GoalTree + Collaborators
            if iteration % CONTEXT_INJECTION_INTERVAL == 0:
                context_injection = self._build_context_injection(trace, goal_tree)
                if context_injection:
                    llm_messages.append({"role": "system", "content": context_injection})

            # 调用 LLM
            result = await self.llm_call(
                messages=llm_messages,
                model=config.model,
                tools=tool_schemas,
                temperature=config.temperature,
                **config.extra_llm_params,
            )

            response_content = result.get("content", "")
            tool_calls = result.get("tool_calls")
            finish_reason = result.get("finish_reason")
            prompt_tokens = result.get("prompt_tokens", 0)
            completion_tokens = result.get("completion_tokens", 0)
            step_cost = result.get("cost", 0)

            # 按需自动创建 root goal
            if goal_tree and not goal_tree.goals and tool_calls:
                has_goal_call = any(
                    tc.get("function", {}).get("name") == "goal"
                    for tc in tool_calls
                )
                if not has_goal_call:
                    mission = goal_tree.mission
                    root_desc = mission[:200] if len(mission) > 200 else mission
                    goal_tree.add_goals(
                        descriptions=[root_desc],
                        reasons=["系统自动创建：Agent 未显式创建目标"],
                        parent_id=None
                    )
                    goal_tree.focus(goal_tree.goals[0].id)
                    if self.trace_store:
                        await self.trace_store.update_goal_tree(trace_id, goal_tree)
                        await self.trace_store.add_goal(trace_id, goal_tree.goals[0])
                    logger.info(f"自动创建 root goal: {goal_tree.goals[0].id}")

            # 获取当前 goal_id
            current_goal_id = goal_tree.current_id if (goal_tree and goal_tree.current_id) else None

            # 记录 assistant Message
            assistant_msg = Message.create(
                trace_id=trace_id,
                role="assistant",
                sequence=sequence,
                goal_id=current_goal_id,
                content={"text": response_content, "tool_calls": tool_calls},
                prompt_tokens=prompt_tokens,
                completion_tokens=completion_tokens,
                finish_reason=finish_reason,
                cost=step_cost,
            )

            if self.trace_store:
                await self.trace_store.add_message(assistant_msg)

            yield assistant_msg
            sequence += 1

            # 处理工具调用
            if tool_calls and config.auto_execute_tools:
                history.append({
                    "role": "assistant",
                    "content": response_content,
                    "tool_calls": tool_calls,
                })

                for tc in tool_calls:
                    current_goal_id = goal_tree.current_id if (goal_tree and goal_tree.current_id) else None

                    tool_name = tc["function"]["name"]
                    tool_args = tc["function"]["arguments"]

                    if isinstance(tool_args, str):
                        tool_args = json.loads(tool_args) if tool_args.strip() else {}
                    elif tool_args is None:
                        tool_args = {}

                    tool_result = await self.tools.execute(
                        tool_name,
                        tool_args,
                        uid=config.uid or "",
                        context={
                            "store": self.trace_store,
                            "trace_id": trace_id,
                            "goal_id": current_goal_id,
                            "runner": self,
                        }
                    )

                    # --- 支持多模态工具反馈 ---
                    # execute() 返回 dict{"text","images"} 或 str
                    if isinstance(tool_result, dict) and tool_result.get("images"):
                        tool_result_text = tool_result["text"]
                        # 构建多模态消息格式
                        tool_content_for_llm = [{"type": "text", "text": tool_result_text}]
                        for img in tool_result["images"]:
                            if img.get("type") == "base64" and img.get("data"):
                                media_type = img.get("media_type", "image/png")
                                tool_content_for_llm.append({
                                    "type": "image_url",
                                    "image_url": {
                                        "url": f"data:{media_type};base64,{img['data']}"
                                    }
                                })
                        img_count = len(tool_content_for_llm) - 1  # 减去 text 块
                        print(f"[Runner] 多模态工具反馈: tool={tool_name}, images={img_count}, text_len={len(tool_result_text)}")
                    else:
                        tool_result_text = str(tool_result)
                        tool_content_for_llm = tool_result_text

                    tool_msg = Message.create(
                        trace_id=trace_id,
                        role="tool",
                        sequence=sequence,
                        goal_id=current_goal_id,
                        tool_call_id=tc["id"],
                        content={"tool_name": tool_name, "result": tool_result_text},
                    )

                    if self.trace_store:
                        await self.trace_store.add_message(tool_msg)
                        # 截图单独存为同名 PNG 文件
                        if isinstance(tool_result, dict) and tool_result.get("images"):
                            import base64 as b64mod
                            for img in tool_result["images"]:
                                if img.get("data"):
                                    png_path = self.trace_store._get_messages_dir(trace_id) / f"{tool_msg.message_id}.png"
                                    png_path.write_bytes(b64mod.b64decode(img["data"]))
                                    print(f"[Runner] 截图已保存: {png_path.name}")
                                    break  # 只存第一张

                    yield tool_msg
                    sequence += 1

                    history.append({
                        "role": "tool",
                        "tool_call_id": tc["id"],
                        "name": tool_name,
                        "content": tool_content_for_llm, # 这里传入 list 即可触发模型的视觉能力
                    })
                    # ------------------------------------------

                continue  # 继续循环

            # 无工具调用，任务完成
            break

        # 完成 Trace
        if self.trace_store:
            await self.trace_store.update_trace(
                trace_id,
                status="completed",
                completed_at=datetime.now(),
            )
            trace_obj = await self.trace_store.get_trace(trace_id)
            if trace_obj:
                yield trace_obj

    # ===== 回溯（Rewind）=====

    async def _rewind(
        self,
        trace_id: str,
        insert_after: int,
        goal_tree: Optional[GoalTree],
    ) -> int:
        """
        执行回溯：标记 insert_after 之后的 messages 和 goals 为 abandoned

        Returns:
            下一个可用的 sequence 号
        """
        if not self.trace_store:
            raise ValueError("trace_store required for rewind")

        # 1. 加载所有 messages（含已 abandoned 的）
        all_messages = await self.trace_store.get_trace_messages(
            trace_id, include_abandoned=True
        )

        if not all_messages:
            return 1

        # 2. 找到安全截断点（确保不截断在 tool_call 和 tool response 之间）
        cutoff = self._find_safe_cutoff(all_messages, insert_after)

        # 3. 批量标记 messages 为 abandoned
        abandoned_ids = await self.trace_store.abandon_messages_after(trace_id, cutoff)

        # 4. 处理 Goals
        if goal_tree:
            active_messages = [m for m in all_messages if m.sequence <= cutoff]
            active_goal_ids = {m.goal_id for m in active_messages if m.goal_id}

            for goal in goal_tree.goals:
                if goal.status == "abandoned":
                    continue  # 已 abandoned，跳过
                if goal.status == "completed" and goal.id in active_goal_ids:
                    continue  # 已完成且有截断点之前的 messages → 保留
                # 其余全部 abandon（含无 active messages 的 completed goal）
                goal.status = "abandoned"
                goal.summary = "回溯导致放弃"

            # 重置 current_id
            goal_tree._current_id = None

            await self.trace_store.update_goal_tree(trace_id, goal_tree)

        # 5. 记录 rewind 事件
        abandoned_sequences = [
            m.sequence for m in all_messages
            if m.sequence > cutoff and m.status != "abandoned"  # 本次新 abandon 的
        ]
        await self.trace_store.append_event(trace_id, "rewind", {
            "insert_after_sequence": cutoff,
            "abandoned_message_count": len(abandoned_ids),
            "abandoned_sequences": abandoned_sequences[:20],  # 只记前 20 条
        })

        # 6. 返回 next sequence
        max_seq = max((m.sequence for m in all_messages), default=0)
        return max_seq + 1

    def _find_safe_cutoff(self, messages: List[Message], insert_after: int) -> int:
        """
        找到安全的截断点。

        如果 insert_after 指向一条带 tool_calls 的 assistant message，
        则自动扩展到其所有对应的 tool response 之后。
        """
        cutoff = insert_after

        # 找到 insert_after 对应的 message
        target_msg = None
        for msg in messages:
            if msg.sequence == insert_after:
                target_msg = msg
                break

        if not target_msg:
            return cutoff

        # 如果是 assistant 且有 tool_calls，找到所有对应的 tool responses
        if target_msg.role == "assistant":
            content = target_msg.content
            if isinstance(content, dict) and content.get("tool_calls"):
                tool_call_ids = set()
                for tc in content["tool_calls"]:
                    if isinstance(tc, dict) and tc.get("id"):
                        tool_call_ids.add(tc["id"])

                # 找到这些 tool_call 对应的 tool messages
                for msg in messages:
                    if (msg.role == "tool" and msg.tool_call_id
                            and msg.tool_call_id in tool_call_ids):
                        cutoff = max(cutoff, msg.sequence)

        return cutoff

    # ===== 上下文注入 =====

    def _build_context_injection(
        self,
        trace: Trace,
        goal_tree: Optional[GoalTree],
    ) -> str:
        """构建周期性注入的上下文（GoalTree + Active Collaborators）"""
        parts = []

        # GoalTree
        if goal_tree and goal_tree.goals:
            parts.append(f"## Current Plan\n\n{goal_tree.to_prompt()}")

        # Active Collaborators
        collaborators = trace.context.get("collaborators", [])
        if collaborators:
            lines = ["## Active Collaborators"]
            for c in collaborators:
                status_str = c.get("status", "unknown")
                ctype = c.get("type", "agent")
                summary = c.get("summary", "")
                name = c.get("name", "unnamed")
                lines.append(f"- {name} [{ctype}, {status_str}]: {summary}")
            parts.append("\n".join(lines))

        return "\n\n".join(parts)

    # ===== 辅助方法 =====

    def _get_tool_schemas(self, tools: Optional[List[str]]) -> List[Dict]:
        """获取工具 Schema"""
        tool_names = BUILTIN_TOOLS.copy()
        if tools:
            for tool in tools:
                if tool not in tool_names:
                    tool_names.append(tool)
        return self.tools.get_schemas(tool_names)

    async def _build_system_prompt(self, config: RunConfig) -> Optional[str]:
        """构建 system prompt（注入 skills 和 experiences）"""
        system_prompt = config.system_prompt

        # 加载 Skills
        skills_text = ""
        skills = load_skills_from_dir(self.skills_dir)
        if skills:
            skills_text = self._format_skills(skills)

        # 加载 Experiences
        experiences_text = ""
        if config.enable_memory and self.memory_store:
            scope = f"agent:{config.agent_type}"
            # 从 messages 提取文本作为查询
            experiences = await self.memory_store.search_experiences(scope, system_prompt or "")
            experiences_text = self._format_experiences(experiences)

        # 拼装
        if system_prompt:
            if skills_text:
                system_prompt += f"\n\n## Skills\n{skills_text}"
            if experiences_text:
                system_prompt += f"\n\n## 相关经验\n{experiences_text}"
        elif skills_text or experiences_text:
            parts = []
            if skills_text:
                parts.append(f"## Skills\n{skills_text}")
            if experiences_text:
                parts.append(f"## 相关经验\n{experiences_text}")
            system_prompt = "\n\n".join(parts)

        return system_prompt

    async def _generate_task_name(self, messages: List[Dict]) -> str:
        """生成任务名称：优先使用 utility_llm，fallback 到文本截取"""
        # 提取 messages 中的文本内容
        text_parts = []
        for msg in messages:
            content = msg.get("content", "")
            if isinstance(content, str):
                text_parts.append(content)
            elif isinstance(content, list):
                for part in content:
                    if isinstance(part, dict) and part.get("type") == "text":
                        text_parts.append(part.get("text", ""))
        raw_text = " ".join(text_parts).strip()

        if not raw_text:
            return "未命名任务"

        # 尝试使用 utility_llm 生成标题
        if self.utility_llm_call:
            try:
                result = await self.utility_llm_call(
                    messages=[
                        {"role": "system", "content": "用中文为以下任务生成一个简短标题（10-30字），只输出标题本身："},
                        {"role": "user", "content": raw_text[:2000]},
                    ],
                    model="gpt-4o-mini",  # 使用便宜模型
                )
                title = result.get("content", "").strip()
                if title and len(title) < 100:
                    return title
            except Exception:
                pass

        # Fallback: 截取前 50 字符
        return raw_text[:50] + ("..." if len(raw_text) > 50 else "")

    def _format_skills(self, skills: List[Skill]) -> str:
        if not skills:
            return ""
        return "\n\n".join(s.to_prompt_text() for s in skills)

    def _format_experiences(self, experiences: List[Experience]) -> str:
        if not experiences:
            return ""
        return "\n".join(f"- {e.to_prompt_text()}" for e in experiences)