feat(core/agent): 实现 AgentLoop 推理循环引擎
保真 TRM4 算法 #11: json_repair 兜底、submit_answer 终止、 pluggy hook 生命周期、无效工具不计步。 Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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"""Agent Loop 引擎 — Thinking+JSON 推理循环，pluggy 驱动 hook。
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算法保真 #11: 完整保留 TRM4 core/loop.py 逻辑:
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- json_repair 兜底解析
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- submit_answer 终止
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- 无效工具（ValueError）不计步
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- pluggy hook 生命周期（before_step / after_tool / after_step / on_finish）
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TRM4 → TRM5 有意变更（非简化）:
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- 同步 → 全异步（async/await）
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- client: Any → llm: LLMProvider（Protocol 类型化）
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- tool_fn: Callable → ToolDispatcher.dispatch()（Protocol + context）
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- Step 新增 call_id（从 LLMResponse.call_id 透传）
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- thinking 从 getattr(msg, "reasoning_content") → response.thinking（adapters 已统一剥离）
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- token 用量从 response.usage.prompt_tokens → response.prompt_tokens（LLMResponse 扁平化）
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"""
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from __future__ import annotations
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import json
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from typing import TYPE_CHECKING, Any
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import pluggy
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from json_repair import repair_json
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from loguru import logger
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from core.agent.protocols import AgentLoopSpec, ToolDispatcher
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from core.agent.types import LoopResult, Step
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if TYPE_CHECKING:
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from core.protocols import LLMProvider
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from core.types import LLMResponse
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async def _call_hook(hook: Any, **kwargs: Any) -> list[Any]:
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"""调用 pluggy hook 并 await 异步返回值。
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pluggy 本身是同步调度，但 hookimpl 可以是 async def，
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此时 hook() 返回 coroutine 列表，需要逐个 await。
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参数:
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hook: pluggy hook caller（如 pm.hook.before_step）。
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**kwargs: 传递给 hook 的关键字参数。
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返回:
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已 resolve 的返回值列表。
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"""
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results = hook(**kwargs)
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if results is not None:
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resolved = []
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for r in results:
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if hasattr(r, "__await__"):
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resolved.append(await r)
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else:
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resolved.append(r)
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return resolved
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return []
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class AgentLoop:
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"""Thinking+JSON 推理循环引擎。
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类比 nn.Module: 接收 prompt + 工具调度器，返回 LoopResult。
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不感知视频树、QA、数据库等领域概念。
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参数:
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llm: LLMProvider 实例（Protocol 类型化，提供 async chat 方法）。
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max_steps: 最大有效步数（每次成功工具调用计一步）。
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max_retries: JSON 解析连续失败的最大容忍次数。
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"""
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def __init__(
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self,
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llm: LLMProvider,
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max_steps: int = 15,
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max_retries: int = 3,
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) -> None:
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self._llm = llm
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self._max_steps = max_steps
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self._max_retries = max_retries
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async def run(
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self,
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system_prompt: str,
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user_prompt: str,
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tool_dispatcher: ToolDispatcher,
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plugins: list[object] | None = None,
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*,
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session_id: str | None = None,
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) -> LoopResult:
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"""执行 Thinking+JSON 推理循环。
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参数:
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system_prompt: 系统提示词。
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user_prompt: 用户提示词。
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tool_dispatcher: 工具调度器，ToolDispatcher Protocol 实例。
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plugins: pluggy 插件列表。
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session_id: 会话 ID，透传给 LLMProvider。
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返回:
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LoopResult 实例，包含推理步骤、token 用量、终止原因。
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"""
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pm = self._create_plugin_manager(plugins)
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messages: list[dict[str, Any]] = [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_prompt},
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]
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steps: list[Step] = []
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token_usage: dict[str, int] = {"prompt_tokens": 0, "completion_tokens": 0}
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step_count = 0
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retry_count = 0
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iteration = 0
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while step_count < self._max_steps:
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await _call_hook(pm.hook.before_step, iteration=iteration, messages=messages)
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# Phase 1: LLM 调用
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try:
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response = await self._call_llm(messages, token_usage, session_id=session_id)
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except Exception as e:
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logger.error("LLM API 调用失败: {}", e)
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result = LoopResult(
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steps=steps,
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steps_used=step_count,
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token_usage=token_usage,
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stop_reason="error",
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)
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await _call_hook(pm.hook.on_finish, result=result)
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return result
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# Phase 2: 解析响应
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parsed = self._parse_response(response)
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if parsed is None:
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retry_count += 1
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logger.warning("响应解析失败 (retry {}/{})", retry_count, self._max_retries)
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messages.append({"role": "assistant", "content": response.content})
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messages.append(
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{
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"role": "user",
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"content": (
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"你的输出不是合法 JSON。请严格输出 JSON 格式："
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'{"reflect": {...}, "plan": {...}, '
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'"action": {"tool": "...", "args": {...}}}'
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),
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}
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)
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if retry_count >= self._max_retries:
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result = LoopResult(
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steps=steps,
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steps_used=step_count,
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token_usage=token_usage,
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stop_reason="parse_error",
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)
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await _call_hook(pm.hook.after_step, iteration=iteration, messages=messages)
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await _call_hook(pm.hook.on_finish, result=result)
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return result
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await _call_hook(pm.hook.after_step, iteration=iteration, messages=messages)
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iteration += 1
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continue
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thought, reflect, plan, raw_content, action, call_id = parsed
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retry_count = 0
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messages.append({"role": "assistant", "content": raw_content})
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# Phase 3: 执行工具
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tool_name: str = action["tool"]
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tool_args: dict[str, Any] = action["args"]
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context: dict[str, Any] = {
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"session_id": session_id,
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"iteration": iteration,
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}
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output, is_valid = await self._execute_tool(
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tool_dispatcher, tool_name, tool_args, context=context
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)
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if not is_valid:
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messages.append(
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{
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"role": "user",
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"content": f"[工具调用无效: {tool_name}] {output}",
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}
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)
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await _call_hook(pm.hook.after_step, iteration=iteration, messages=messages)
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iteration += 1
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continue
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step_count += 1
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step = Step(
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thought=thought,
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reflect=reflect,
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plan=plan,
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tool_call={"tool": tool_name, "args": tool_args},
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tool_output=output,
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raw_content=raw_content,
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call_id=call_id,
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)
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steps.append(step)
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# Phase 4: Hook + 反馈
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hints = await _call_hook(pm.hook.after_tool, iteration=iteration, step=step)
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feedback = self._build_feedback(tool_name, output, hints)
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messages.append(feedback)
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await _call_hook(pm.hook.after_step, iteration=iteration, messages=messages)
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# Phase 5: 终止检查
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if tool_name == "submit_answer":
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result = LoopResult(
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result=tool_args,
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steps=steps,
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steps_used=step_count,
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token_usage=token_usage,
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stop_reason="finished",
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)
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await _call_hook(pm.hook.on_finish, result=result)
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return result
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iteration += 1
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# 预算耗尽
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result = LoopResult(
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steps=steps,
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steps_used=step_count,
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token_usage=token_usage,
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stop_reason="budget_exceeded",
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)
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await _call_hook(pm.hook.on_finish, result=result)
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return result
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def _create_plugin_manager(self, plugins: list[object] | None) -> pluggy.PluginManager:
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"""创建并注册 plugins 的 PluginManager。
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参数:
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plugins: pluggy 插件列表，可为 None。
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返回:
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配置好的 PluginManager 实例。
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"""
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pm = pluggy.PluginManager("agent_loop")
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pm.add_hookspecs(AgentLoopSpec)
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for plugin in plugins or []:
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pm.register(plugin)
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return pm
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async def _call_llm(
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self,
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messages: list[dict[str, Any]],
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token_usage: dict[str, int],
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*,
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session_id: str | None = None,
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) -> LLMResponse:
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"""调用 LLMProvider 并累加 token 使用量。
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参数:
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messages: 消息历史。
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token_usage: 可变字典，就地累加。
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session_id: 会话 ID，透传给 LLMProvider。
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返回:
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LLMResponse 实例。
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"""
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response = await self._llm.chat(messages, session_id=session_id)
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token_usage["prompt_tokens"] += response.prompt_tokens
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token_usage["completion_tokens"] += response.completion_tokens
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return response
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def _parse_response(
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self, response: LLMResponse
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) -> tuple[str, dict, dict, str, dict, str] | None:
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"""从 LLMResponse 中提取结构化决策数据。
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解析流程: content → repair_json → json.loads → 校验 action/tool/args。
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参数:
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response: LLMResponse 实例。
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返回:
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解析成功返回 (thought, reflect, plan, raw_content, action, call_id)；
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解析失败返回 None。
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"""
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content = response.content
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thought = response.thinking
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if not content.strip():
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return None
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repaired = repair_json(content)
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try:
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data = json.loads(repaired)
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except (json.JSONDecodeError, ValueError):
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return None
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if not isinstance(data, dict) or "action" not in data:
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return None
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action = data["action"]
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if not isinstance(action, dict) or "tool" not in action or "args" not in action:
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return None
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reflect = data.get("reflect", {})
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plan = data.get("plan", {})
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return thought, reflect, plan, content, action, response.call_id
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async def _execute_tool(
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self,
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dispatcher: ToolDispatcher,
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name: str,
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args: dict[str, Any],
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*,
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context: dict[str, Any],
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) -> tuple[str, bool]:
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"""执行工具调用。
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参数:
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dispatcher: 工具调度器。
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name: 工具名称。
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args: 工具参数。
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context: 调用上下文（session_id、iteration 等）。
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返回:
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(output, is_valid) — ValueError 时 is_valid=False 且不计步数。
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"""
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try:
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output = await dispatcher.dispatch(name, args, context=context)
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return output, True
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except ValueError as e:
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return f"工具调用失败: {e}", False
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def _build_feedback(
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self,
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tool_name: str,
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tool_output: str,
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hints: list[str | None],
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) -> dict[str, Any]:
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"""组装工具结果反馈消息。
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参数:
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tool_name: 工具名称。
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tool_output: 工具原始输出。
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hints: hook 返回的 hint 列表（含 None）。
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返回:
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user role 消息字典。
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"""
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parts = [f"[工具执行结果: {tool_name}]", tool_output]
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for hint in hints:
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if hint is not None:
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parts.append(hint)
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return {"role": "user", "content": "\n".join(parts)}
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