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. 记录执行图（Trace + Steps）
3. 检索和注入记忆（Experience + Skill）
4. 收集反馈，提取经验
"""

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

from agent.core.config import AgentConfig, CallResult
from agent.execution import Trace, Step, TraceStore
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
from agent.execution import dump_tree, dump_markdown

logger = logging.getLogger(__name__)


# 内置工具列表（始终自动加载）
BUILTIN_TOOLS = [
    "read_file",
    "edit_file",
    "write_file",
    "glob_files",
    "grep_content",
    "bash_command",
    "skill",
    "list_skills",
]


class AgentRunner:
    """
    Agent 执行引擎

    支持两种模式：
    1. call(): 单次 LLM 调用（简洁 API）
    2. run(): Agent 模式（循环 + 记忆 + 追踪）
    """

    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,
        config: Optional[AgentConfig] = None,
        skills_dir: Optional[str] = None,
        debug: bool = False,
    ):
        """
        初始化 AgentRunner

        Args:
            trace_store: Trace 存储（可选，不提供则不记录）
            memory_store: Memory 存储（可选，不提供则不使用记忆）
            state_store: State 存储（可选，用于任务状态）
            tool_registry: 工具注册表（可选，默认使用全局注册表）
            llm_call: LLM 调用函数（必须提供，用于实际调用 LLM）
            config: Agent 配置
            skills_dir: Skills 目录路径（可选，不提供则不加载 skills）
            debug: 是否启用 debug 模式（输出 step tree 到 .trace/tree.txt）
        """
        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.config = config or AgentConfig()
        self.skills_dir = skills_dir
        self.debug = debug

    def _generate_id(self) -> str:
        """生成唯一 ID"""
        import uuid
        return str(uuid.uuid4())

    async def _dump_debug(self, trace_id: str) -> None:
        """Debug 模式下输出 step tree（txt + markdown 两种格式）"""
        if not self.debug or not self.trace_store:
            return
        trace = await self.trace_store.get_trace(trace_id)
        steps = await self.trace_store.get_trace_steps(trace_id)

        # 输出 tree.txt（简洁格式，兼容旧版）
        dump_tree(trace, steps)

        # 输出 tree.md（完整可折叠格式）
        dump_markdown(trace, steps)

    # ===== 单次调用 =====

    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

        Args:
            messages: 消息列表
            model: 模型名称
            tools: 工具名称列表
            uid: 用户 ID
            trace: 是否记录 Trace
            **kwargs: 其他参数传递给 LLM

        Returns:
            CallResult
        """
        if not self.llm_call:
            raise ValueError("llm_call function not provided")

        trace_id = None
        step_id = None

        # 创建 Trace
        if trace and self.trace_store:
            trace_obj = Trace.create(
                mode="call",
                uid=uid,
                context={"model": model}
            )
            trace_id = await self.trace_store.create_trace(trace_obj)

        # 准备工具 Schema
        # 合并内置工具 + 用户指定工具
        tool_names = BUILTIN_TOOLS.copy()
        if tools:
            # 添加用户指定的工具（去重）
            for tool in tools:
                if tool not in tool_names:
                    tool_names.append(tool)

        tool_schemas = self.tools.get_schemas(tool_names)

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

        # 记录 Step
        if trace and self.trace_store and trace_id:
            step = Step.create(
                trace_id=trace_id,
                step_type="thought",
                sequence=0,
                status="completed",
                description=f"LLM 调用 ({model})",
                data={
                    "messages": messages,
                    "response": result.get("content", ""),
                    "model": model,
                    "tools": tool_schemas,  # 记录传给模型的 tools schema
                    "tool_calls": result.get("tool_calls"),
                },
                tokens=result.get("prompt_tokens", 0) + result.get("completion_tokens", 0),
                cost=result.get("cost", 0),
            )
            step_id = await self.trace_store.add_step(step)
            await self._dump_debug(trace_id)

            # 完成 Trace
            await self.trace_store.update_trace(
                trace_id,
                status="completed",
                completed_at=datetime.now(),
                total_tokens=result.get("prompt_tokens", 0) + result.get("completion_tokens", 0),
                total_cost=result.get("cost", 0)
            )

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

    # ===== Agent 模式 =====

    async def run(
        self,
        task: str,
        messages: Optional[List[Dict]] = None,
        system_prompt: Optional[str] = None,
        model: str = "gpt-4o",
        tools: Optional[List[str]] = None,
        agent_type: Optional[str] = None,
        uid: Optional[str] = None,
        max_iterations: Optional[int] = None,
        enable_memory: Optional[bool] = None,
        auto_execute_tools: Optional[bool] = None,
        **kwargs
    ) -> AsyncIterator[Union[Trace, Step]]:
        """
        Agent 模式执行

        Args:
            task: 任务描述
            messages: 初始消息（可选）
            system_prompt: 系统提示（可选）
            model: 模型名称
            tools: 工具名称列表
            agent_type: Agent 类型
            uid: 用户 ID
            max_iterations: 最大迭代次数
            enable_memory: 是否启用记忆
            auto_execute_tools: 是否自动执行工具
            **kwargs: 其他参数

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

        # 使用配置默认值
        agent_type = agent_type or self.config.agent_type
        max_iterations = max_iterations or self.config.max_iterations
        enable_memory = enable_memory if enable_memory is not None else self.config.enable_memory
        auto_execute_tools = auto_execute_tools if auto_execute_tools is not None else self.config.auto_execute_tools

        # 创建 Trace
        trace_id = self._generate_id()
        trace_obj = None
        if self.trace_store:
            trace_obj = Trace(
                trace_id=trace_id,
                mode="agent",
                task=task,
                agent_type=agent_type,
                uid=uid,
                context={"model": model, **kwargs}
            )
            await self.trace_store.create_trace(trace_obj)
            # 返回 Trace（表示开始）
            yield trace_obj

        try:
            # 加载记忆（Experience 和 Skill）
            experiences_text = ""
            skills_text = ""

            if enable_memory and self.memory_store:
                scope = f"agent:{agent_type}"
                experiences = await self.memory_store.search_experiences(scope, task)
                experiences_text = self._format_experiences(experiences)

                # 记录 memory_read Step
                if self.trace_store:
                    mem_step = Step.create(
                        trace_id=trace_id,
                        step_type="memory_read",
                        sequence=0,
                        status="completed",
                        description=f"加载 {len(experiences)} 条经验",
                        data={
                            "experiences_count": len(experiences),
                            "experiences": [e.to_dict() for e in experiences],
                        }
                    )
                    await self.trace_store.add_step(mem_step)
                    await self._dump_debug(trace_id)
                    # 返回 Step（表示记忆加载完成）
                    yield mem_step

            # 加载 Skills（内置 + 用户自定义）
            # load_skills_from_dir() 会自动加载 agent/skills/ 中的内置 skills
            # 如果提供了 skills_dir，会额外加载用户自定义的 skills
            skills = load_skills_from_dir(self.skills_dir)
            if skills:
                skills_text = self._format_skills(skills)
                if self.skills_dir:
                    logger.info(f"加载 {len(skills)} 个 skills (内置 + 自定义: {self.skills_dir})")
                else:
                    logger.info(f"加载 {len(skills)} 个内置 skills")

            # 构建初始消息
            if messages is None:
                messages = []

            if system_prompt:
                # 注入记忆和 skills 到 system prompt
                full_system = system_prompt
                if skills_text:
                    full_system += f"\n\n## Skills\n{skills_text}"
                if experiences_text:
                    full_system += f"\n\n## 相关经验\n{experiences_text}"

                messages = [{"role": "system", "content": full_system}] + messages

            # 添加任务描述
            messages.append({"role": "user", "content": task})

            # 准备工具 Schema
            # 合并内置工具 + 用户指定工具
            tool_names = BUILTIN_TOOLS.copy()
            if tools:
                # 添加用户指定的工具（去重）
                for tool in tools:
                    if tool not in tool_names:
                        tool_names.append(tool)

            tool_schemas = self.tools.get_schemas(tool_names)

            # 执行循环
            current_goal_id = None  # 当前焦点 goal
            sequence = 1
            total_tokens = 0
            total_cost = 0.0

            for iteration in range(max_iterations):
                # 调用 LLM
                result = await self.llm_call(
                    messages=messages,
                    model=model,
                    tools=tool_schemas,
                    **kwargs
                )

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

                total_tokens += step_tokens
                total_cost += step_cost

                # 记录 LLM 调用 Step
                llm_step_id = self._generate_id()
                llm_step = None
                if self.trace_store:
                    # 推断 step_type
                    step_type = "thought"
                    if tool_calls:
                        step_type = "thought"  # 有工具调用的思考
                    elif not tool_calls and iteration > 0:
                        step_type = "response"  # 无工具调用，可能是最终回复

                    llm_step = Step(
                        step_id=llm_step_id,
                        trace_id=trace_id,
                        step_type=step_type,
                        status="completed",
                        sequence=sequence,
                        parent_id=current_goal_id,
                        description=response_content[:100] + "..." if len(response_content) > 100 else response_content,
                        data={
                            "messages": messages,  # 记录完整的 messages（包含 system prompt）
                            "content": response_content,
                            "model": model,
                            "tools": tool_schemas,  # 记录传给模型的 tools schema
                            "tool_calls": tool_calls,
                        },
                        tokens=step_tokens,
                        cost=step_cost,
                    )
                    await self.trace_store.add_step(llm_step)
                    await self._dump_debug(trace_id)
                    # 返回 Step（LLM 思考完成）
                    yield llm_step

                sequence += 1

                # 处理工具调用
                if tool_calls and auto_execute_tools:
                    # 检查是否需要用户确认
                    if self.tools.check_confirmation_required(tool_calls):
                        # 创建等待确认的 Step
                        await_step = Step.create(
                            trace_id=trace_id,
                            step_type="action",
                            status="awaiting_approval",
                            sequence=sequence,
                            parent_id=llm_step_id,
                            description="等待用户确认工具调用",
                            data={
                                "tool_calls": tool_calls,
                                "confirmation_flags": self.tools.get_confirmation_flags(tool_calls),
                                "editable_params": self.tools.get_editable_params_map(tool_calls)
                            }
                        )
                        if self.trace_store:
                            await self.trace_store.add_step(await_step)
                            await self._dump_debug(trace_id)
                        yield await_step
                        # TODO: 等待用户确认
                        break

                    # 执行工具
                    messages.append({"role": "assistant", "content": response_content, "tool_calls": tool_calls})

                    for tc in tool_calls:
                        tool_name = tc["function"]["name"]
                        tool_args = tc["function"]["arguments"]
                        if isinstance(tool_args, str):
                            import json
                            tool_args = json.loads(tool_args)

                        # 执行工具
                        tool_result = await self.tools.execute(
                            tool_name,
                            tool_args,
                            uid=uid or ""
                        )

                        # 记录 action Step
                        action_step_id = self._generate_id()
                        action_step = None
                        if self.trace_store:
                            action_step = Step(
                                step_id=action_step_id,
                                trace_id=trace_id,
                                step_type="action",
                                status="completed",
                                sequence=sequence,
                                parent_id=llm_step_id,
                                description=f"{tool_name}({', '.join(f'{k}={v}' for k, v in list(tool_args.items())[:2])})",
                                data={
                                    "tool_name": tool_name,
                                    "arguments": tool_args,
                                }
                            )
                            await self.trace_store.add_step(action_step)
                            await self._dump_debug(trace_id)
                            # 返回 Step（工具调用）
                            yield action_step

                        sequence += 1

                        # 记录 result Step
                        result_step_id = self._generate_id()
                        result_step = None
                        if self.trace_store:
                            result_step = Step(
                                step_id=result_step_id,
                                trace_id=trace_id,
                                step_type="result",
                                status="completed",
                                sequence=sequence,
                                parent_id=action_step_id,
                                description=str(tool_result)[:100] if tool_result else "",
                                data={
                                    "tool_name": tool_name,
                                    "output": tool_result,
                                }
                            )
                            await self.trace_store.add_step(result_step)
                            await self._dump_debug(trace_id)
                            # 返回 Step（工具结果）
                            yield result_step

                        sequence += 1

                        # 添加到消息（Gemini 需要 name 字段！）
                        messages.append({
                            "role": "tool",
                            "tool_call_id": tc["id"],
                            "name": tool_name,
                            "content": tool_result
                        })

                    continue  # 继续循环

                # 无工具调用，任务完成
                # 记录 response Step
                response_step_id = self._generate_id()
                response_step = None
                if self.trace_store:
                    response_step = Step(
                        step_id=response_step_id,
                        trace_id=trace_id,
                        step_type="response",
                        status="completed",
                        sequence=sequence,
                        parent_id=current_goal_id,
                        description=response_content[:100] + "..." if len(response_content) > 100 else response_content,
                        data={
                            "content": response_content,
                            "is_final": True
                        }
                    )
                    await self.trace_store.add_step(response_step)
                    await self._dump_debug(trace_id)
                    # 返回 Step（最终回复）
                    yield response_step

                break

            # 完成 Trace
            if self.trace_store:
                await self.trace_store.update_trace(
                    trace_id,
                    status="completed",
                    completed_at=datetime.now(),
                    total_tokens=total_tokens,
                    total_cost=total_cost
                )
                # 重新获取更新后的 Trace 并返回
                trace_obj = await self.trace_store.get_trace(trace_id)
                if trace_obj:
                    yield trace_obj

        except Exception as e:
            logger.error(f"Agent run failed: {e}")

            if self.trace_store:
                await self.trace_store.update_trace(
                    trace_id,
                    status="failed",
                    completed_at=datetime.now()
                )
                # 重新获取更新后的 Trace 并返回
                trace_obj = await self.trace_store.get_trace(trace_id)
                if trace_obj:
                    yield trace_obj
            raise

    # ===== 反馈 =====

    async def add_feedback(
        self,
        trace_id: str,
        target_step_id: str,
        feedback_type: Literal["positive", "negative", "correction"],
        content: str,
        extract_experience: bool = True
    ) -> Optional[str]:
        """
        添加人工反馈

        Args:
            trace_id: Trace ID
            target_step_id: 反馈针对的 Step ID
            feedback_type: 反馈类型
            content: 反馈内容
            extract_experience: 是否自动提取经验

        Returns:
            experience_id: 如果提取了经验
        """
        if not self.trace_store:
            return None

        # 获取 Trace
        trace = await self.trace_store.get_trace(trace_id)
        if not trace:
            logger.warning(f"Trace not found: {trace_id}")
            return None

        # 创建 feedback Step
        steps = await self.trace_store.get_trace_steps(trace_id)
        max_seq = max(s.sequence for s in steps) if steps else 0

        feedback_step = Step.create(
            trace_id=trace_id,
            step_type="feedback",
            sequence=max_seq + 1,
            status="completed",
            description=f"{feedback_type}: {content[:50]}...",
            parent_id=target_step_id,
            data={
                "target_step_id": target_step_id,
                "feedback_type": feedback_type,
                "content": content
            }
        )
        await self.trace_store.add_step(feedback_step)
        await self._dump_debug(trace_id)

        # 提取经验
        exp_id = None
        if extract_experience and self.memory_store and feedback_type in ("positive", "correction"):
            exp = Experience.create(
                scope=f"agent:{trace.agent_type}" if trace.agent_type else "agent:default",
                condition=f"执行类似 '{trace.task}' 任务时" if trace.task else "通用场景",
                rule=content,
                evidence=[target_step_id, feedback_step.step_id],
                source="feedback",
                confidence=0.8 if feedback_type == "positive" else 0.6
            )
            exp_id = await self.memory_store.add_experience(exp)

            # 记录 memory_write Step
            mem_step = Step.create(
                trace_id=trace_id,
                step_type="memory_write",
                sequence=max_seq + 2,
                status="completed",
                description=f"保存经验: {exp.condition[:30]}...",
                parent_id=feedback_step.step_id,
                data={
                    "experience_id": exp_id,
                    "condition": exp.condition,
                    "rule": exp.rule
                }
            )
            await self.trace_store.add_step(mem_step)
            await self._dump_debug(trace_id)

        return exp_id

    # ===== 辅助方法 =====

    def _format_skills(self, skills: List[Skill]) -> str:
        """格式化技能为 Prompt 文本"""
        if not skills:
            return ""
        return "\n\n".join(s.to_prompt_text() for s in skills)

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

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