steven_li
5ba5c7e4c1
集成新的Beaver后端服务到应用实例中，替换原有的nanobot实现。 主要变更包括： - 在Dockerfile和环境配置中添加Beaver相关路径和配置变量 - 更新工作目录结构从.nanobot到.beaver - 实现Beaver引擎加载器，支持配置文件加载和工具组装 - 添加内置工具如ListDirectoryTool、ReadFileTool、SearchFilesTool - 更新消息处理流程，支持通道适配器和网关模式 - 重构技能系统，支持显式工具提示和嵌入式检索 - 改进错误处理和生命周期管理 此变更使应用实例能够使用统一的Beaver后端进行AI代理运行时管理。
728 lines
27 KiB
Python
728 lines
27 KiB
Python
"""Unified agent loop used by all Beaver agents."""
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from __future__ import annotations
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import asyncio
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from dataclasses import dataclass, field
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from typing import Any
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from uuid import uuid4
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from beaver.engine.context import ContextBuildInput, SessionContext
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from beaver.engine.providers import ProviderBundle, make_provider_bundle
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from beaver.tools import ToolContext
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from .loader import EngineLoader, EngineLoadResult
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@dataclass(slots=True)
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class AgentProfile:
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"""Runtime profile for a Beaver agent instance."""
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name: str = "default"
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system_prompt: str = ""
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default_model: str = "gpt-4.1-mini"
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max_tokens: int = 4096
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temperature: float = 0.2
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max_tool_iterations: int = 8
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@dataclass(slots=True)
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class AgentRunResult:
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"""一次 direct run 的最小结果结构。"""
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session_id: str
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run_id: str
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output_text: str
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finish_reason: str
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tool_iterations: int
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provider_name: str | None = None
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model: str | None = None
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usage: dict[str, Any] = field(default_factory=dict)
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@dataclass(slots=True)
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class _DirectRunRequest:
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"""运行循环中的单个 direct task。"""
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task: str
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kwargs: dict[str, Any]
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future: asyncio.Future[AgentRunResult]
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class AgentLoop:
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"""Single execution kernel shared by root agents and delegated agents."""
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def __init__(self, *, profile: AgentProfile | None = None, loader: EngineLoader | None = None) -> None:
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self.profile = profile or AgentProfile()
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self.loader = loader or EngineLoader()
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self.loaded: EngineLoadResult | None = None
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self._run_queue: asyncio.Queue[_DirectRunRequest | None] | None = None
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self._running = False
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self._stop_requested = False
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def boot(self) -> EngineLoadResult:
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"""Load shared runtime capabilities once for this agent instance."""
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if self.loaded is None:
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self.loaded = self.loader.load()
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return self.loaded
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@property
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def is_running(self) -> bool:
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return self._running
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async def run(self) -> None:
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"""启动最小运行循环，顺序消费提交进来的 direct tasks。
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第一版故意保持克制：
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1. 只做单消费者串行消费
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2. 真正执行仍复用 `process_direct()`
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3. 不引入 bus / worker / priority / retry
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"""
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if self._running:
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raise RuntimeError("AgentLoop.run() is already active")
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self.boot()
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self._run_queue = asyncio.Queue()
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self._running = True
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self._stop_requested = False
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try:
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while True:
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item = await self._run_queue.get()
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if item is None:
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if self._stop_requested:
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break
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continue
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if item.future.cancelled():
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continue
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try:
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result = await self._process_direct_impl(item.task, **item.kwargs)
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except asyncio.CancelledError:
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if not item.future.done():
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item.future.cancel()
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raise
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except Exception as exc: # pragma: no cover - defensive queue path
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if not item.future.done():
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item.future.set_exception(exc)
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else:
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if not item.future.done():
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item.future.set_result(result)
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finally:
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if self._run_queue is not None:
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while True:
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try:
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pending = self._run_queue.get_nowait()
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except asyncio.QueueEmpty:
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break
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if isinstance(pending, _DirectRunRequest) and not pending.future.done():
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pending.future.set_exception(
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RuntimeError("AgentLoop.run() stopped before processing the queued task")
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)
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self._running = False
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self._stop_requested = False
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self._run_queue = None
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async def stop(self) -> None:
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"""停止运行循环。
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第一版语义：
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- 不再接收新任务
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- 当前已经取出的任务允许收尾
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- 不自动 close runtime
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"""
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if not self._running or self._run_queue is None:
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return
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self._stop_requested = True
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await self._run_queue.put(None)
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async def submit_direct(
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self,
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task: str,
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**kwargs: Any,
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) -> AgentRunResult:
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"""向运行中的 loop 提交一个 direct task，并等待结果。"""
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if not self._running or self._run_queue is None:
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raise RuntimeError("AgentLoop.submit_direct() requires an active run() loop")
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if self._stop_requested:
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raise RuntimeError("AgentLoop.submit_direct() is not accepting new tasks after stop()")
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future: asyncio.Future[AgentRunResult] = asyncio.get_running_loop().create_future()
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await self._run_queue.put(_DirectRunRequest(task=task, kwargs=dict(kwargs), future=future))
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return await future
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def close(self) -> None:
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"""关闭当前 loop 持有的 runtime。
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第 6 阶段先把生命周期最小骨架立住：
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- `boot()` 负责建立 runtime
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- `close()` 负责释放由 runtime 持有的资源
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- 之后再在此基础上扩 `run()/stop()/shutdown hooks`
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"""
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if self._running:
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raise RuntimeError("AgentLoop.close() requires the run loop to be stopped first")
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if self.loaded is None:
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return
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try:
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self.loaded.close()
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finally:
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self.loaded = None
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async def process_direct(
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self,
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task: str,
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*,
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session_id: str | None = None,
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source: str = "direct",
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user_id: str | None = None,
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title: str | None = None,
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execution_context: str | None = None,
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model: str | None = None,
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provider_name: str | None = None,
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api_key: str | None = None,
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api_base: str | None = None,
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extra_headers: dict[str, str] | None = None,
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routing: Any = None,
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fallback_target: dict[str, Any] | None = None,
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auxiliary_target: dict[str, Any] | None = None,
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embedding_target: dict[str, Any] | None = None,
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embedding_model: str | None = None,
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max_tokens: int | None = None,
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temperature: float | None = None,
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max_tool_iterations: int | None = None,
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provider_bundle: ProviderBundle | None = None,
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) -> AgentRunResult:
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"""跑通最小 direct run 主链。
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当前主链刻意保持克制，只解决这些事情：
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1. 确保 session 存在
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2. 用 frozen memory + history 组 prompt
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3. 调 provider
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4. 若有 tool calls，则进入最小 tool loop
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5. 把 user/assistant/tool 消息和 usage 写回 session
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"""
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if self._running:
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raise RuntimeError(
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"AgentLoop.process_direct() is disabled while run() is active; "
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"submit tasks via submit_direct() instead."
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)
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return await self._process_direct_impl(
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task,
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session_id=session_id,
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source=source,
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user_id=user_id,
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title=title,
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execution_context=execution_context,
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model=model,
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provider_name=provider_name,
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api_key=api_key,
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api_base=api_base,
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extra_headers=extra_headers,
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routing=routing,
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fallback_target=fallback_target,
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auxiliary_target=auxiliary_target,
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embedding_target=embedding_target,
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embedding_model=embedding_model,
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max_tokens=max_tokens,
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temperature=temperature,
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max_tool_iterations=max_tool_iterations,
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provider_bundle=provider_bundle,
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)
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async def _process_direct_impl(
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self,
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task: str,
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*,
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session_id: str | None = None,
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source: str = "direct",
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user_id: str | None = None,
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title: str | None = None,
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execution_context: str | None = None,
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model: str | None = None,
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provider_name: str | None = None,
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api_key: str | None = None,
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api_base: str | None = None,
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extra_headers: dict[str, str] | None = None,
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routing: Any = None,
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fallback_target: dict[str, Any] | None = None,
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auxiliary_target: dict[str, Any] | None = None,
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embedding_target: dict[str, Any] | None = None,
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embedding_model: str | None = None,
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max_tokens: int | None = None,
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temperature: float | None = None,
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max_tool_iterations: int | None = None,
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provider_bundle: ProviderBundle | None = None,
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) -> AgentRunResult:
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"""真正执行一轮 direct run 的内部实现。
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规则：
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- 外部直接调用时走 `process_direct()`
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- 运行循环内部消费时走 `_process_direct_impl()`
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- 这样才能保证 run 模式下外部不能绕过队列直接执行
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"""
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loaded = self.boot()
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session_manager = self._require_loaded("session_manager")
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memory_service = self._require_loaded("memory_service")
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context_builder = self._require_loaded("context_builder")
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tool_registry = self._require_loaded("tool_registry")
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tool_assembler = self._require_loaded("tool_assembler")
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tool_executor = self._require_loaded("tool_executor")
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skills_loader = self._require_loaded("skills_loader")
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skill_assembler = self._require_loaded("skill_assembler")
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config = loaded.config
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configured_provider = config.resolve_provider_target(model=model, provider_name=provider_name)
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resolved_session_id = session_id or uuid4().hex
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resolved_run_id = uuid4().hex
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resolved_model = configured_provider.get("model") or self.profile.default_model
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resolved_provider_name = configured_provider.get("provider_name") or provider_name
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resolved_api_key = api_key or configured_provider.get("api_key")
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resolved_api_base = api_base or configured_provider.get("api_base")
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resolved_extra_headers = extra_headers or configured_provider.get("extra_headers")
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resolved_request_timeout_seconds = configured_provider.get("request_timeout_seconds")
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resolved_embedding_model = embedding_model or config.default_embedding_model
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resolved_embedding_target = embedding_target or config.resolve_embedding_target()
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resolved_max_tokens = max_tokens or self.profile.max_tokens
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resolved_temperature = self.profile.temperature if temperature is None else temperature
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resolved_max_tool_iterations = (
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self.profile.max_tool_iterations if max_tool_iterations is None else max_tool_iterations
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)
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# 每次新运行开始前都通过 MemoryService 刷新 live state。
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# 这样 memory policy 会收口在 service，而不是散在 loop 里。
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memory_service.reload_for_new_run()
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session_manager.ensure_session(
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resolved_session_id,
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source=source,
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model=resolved_model,
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title=title,
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user_id=user_id,
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)
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session_manager.append_message(
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resolved_session_id,
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run_id=resolved_run_id,
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role="system",
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event_type="run_started",
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event_payload={
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"source": source,
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"model": resolved_model,
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"agent_name": self.profile.name,
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},
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content=task,
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context_visible=False,
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source=source,
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title=title,
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model=resolved_model,
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user_id=user_id,
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)
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user_message_recorded = False
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iterations = 0
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final_usage: dict[str, Any] = {}
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final_provider_name: str | None = resolved_provider_name
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final_model: str | None = resolved_model
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try:
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bundle = provider_bundle or make_provider_bundle(
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model=resolved_model,
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provider_name=resolved_provider_name,
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api_key=resolved_api_key,
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api_base=resolved_api_base,
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request_timeout_seconds=resolved_request_timeout_seconds,
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extra_headers=resolved_extra_headers,
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routing=routing,
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fallback_target=fallback_target,
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auxiliary_target=auxiliary_target,
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embedding_target=resolved_embedding_target,
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embedding_model=resolved_embedding_model,
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)
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skill_selector_provider = bundle.auxiliary_provider or bundle.main_provider
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skill_selector_model = (
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bundle.auxiliary_runtime.model
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if bundle.auxiliary_runtime is not None
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else bundle.main_runtime.model
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)
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assembled_skills = await skill_assembler.assemble(
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task_description=task,
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provider=skill_selector_provider,
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model=skill_selector_model,
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embedding_runtime=bundle.embedding_runtime,
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)
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skill_activation_messages = context_builder.build_skill_activation_messages(
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assembled_skills.activated_skills
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)
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if skill_activation_messages:
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session_manager.append_message(
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resolved_session_id,
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run_id=resolved_run_id,
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role="system",
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event_type="skill_activation_snapshotted",
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event_payload={
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"activation_messages": skill_activation_messages,
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},
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content="\n\n".join(message["content"] for message in skill_activation_messages) or None,
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context_visible=False,
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source=source,
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title=title,
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model=resolved_model,
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user_id=user_id,
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)
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selected_tool_specs = await tool_assembler.assemble(
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task_description=task,
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registry=tool_registry,
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skills_loader=skills_loader,
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activated_skills=assembled_skills.activated_skills,
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embedding_runtime=bundle.embedding_runtime,
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top_k=10,
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)
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tool_schemas = tool_registry.export_selected_provider_schemas(selected_tool_specs)
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session_manager.append_message(
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resolved_session_id,
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run_id=resolved_run_id,
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role="system",
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event_type="tool_selection_snapshotted",
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event_payload={
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"tools": [spec.to_mcp_descriptor() for spec in selected_tool_specs],
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"tool_names": [spec.name for spec in selected_tool_specs],
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},
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content=", ".join(spec.name for spec in selected_tool_specs) or None,
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context_visible=False,
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source=source,
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title=title,
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model=resolved_model,
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user_id=user_id,
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)
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build_input = ContextBuildInput(
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base_system_prompt=self.profile.system_prompt,
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history=session_manager.get_history(resolved_session_id),
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current_user_input=task,
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memory_snapshot=memory_service.get_snapshot(),
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activated_skills=assembled_skills.activated_skills,
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session_context=SessionContext(
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session_id=resolved_session_id,
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source=source,
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model=resolved_model,
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user_id=user_id,
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),
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execution_context=execution_context,
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)
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context_result = context_builder.build_messages(build_input)
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session_manager.update_system_prompt(resolved_session_id, context_result.system_prompt)
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session_manager.append_message(
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resolved_session_id,
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run_id=resolved_run_id,
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role="system",
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event_type="system_prompt_snapshotted",
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event_payload={
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"source": source,
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"model": resolved_model,
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"system_prompt_length": len(context_result.system_prompt),
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},
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content=context_result.system_prompt,
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context_visible=False,
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source=source,
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title=title,
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model=resolved_model,
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user_id=user_id,
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)
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session_manager.append_message(
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resolved_session_id,
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run_id=resolved_run_id,
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role="user",
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event_type="user_message_added",
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content=task,
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source=source,
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title=title,
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model=resolved_model,
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user_id=user_id,
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)
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user_message_recorded = True
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provider = bundle.main_provider
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messages = list(context_result.messages)
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tool_context = ToolContext(
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workspace=str(loaded.workspace),
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session_id=resolved_session_id,
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user_id=user_id,
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services={
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"session_manager": session_manager,
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"memory_service": memory_service,
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"memory_store": memory_service.get_store(),
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"tool_registry": tool_registry,
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},
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metadata={
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"source": source,
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"agent_name": self.profile.name,
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},
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)
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final_text = ""
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final_finish_reason = "stop"
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final_provider_name = bundle.main_runtime.provider_name
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final_model = bundle.main_runtime.model
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while True:
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response = await provider.chat(
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messages=messages,
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tools=tool_schemas,
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model=final_model,
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max_tokens=resolved_max_tokens,
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temperature=resolved_temperature,
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)
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final_provider_name = response.provider_name or final_provider_name
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final_model = response.model or final_model
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final_usage = self._merge_usage(final_usage, response.usage or {})
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self._record_usage(session_manager, resolved_session_id, response.usage or {})
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assistant_tool_calls = self._serialize_tool_calls(response.tool_calls)
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session_manager.append_message(
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resolved_session_id,
|
||
run_id=resolved_run_id,
|
||
role="assistant",
|
||
event_type="assistant_message_added",
|
||
content=response.content,
|
||
tool_calls=assistant_tool_calls or None,
|
||
finish_reason=response.finish_reason,
|
||
reasoning=response.reasoning_content,
|
||
source=source,
|
||
title=title,
|
||
model=final_model,
|
||
user_id=user_id,
|
||
)
|
||
context_builder.add_assistant_message(
|
||
messages,
|
||
content=response.content,
|
||
tool_calls=assistant_tool_calls or None,
|
||
reasoning_content=response.reasoning_content,
|
||
)
|
||
|
||
if not response.has_tool_calls:
|
||
final_text = response.content or ""
|
||
final_finish_reason = response.finish_reason or "stop"
|
||
break
|
||
|
||
if iterations >= resolved_max_tool_iterations:
|
||
final_text = response.content or "Tool loop stopped after reaching the configured iteration limit."
|
||
final_finish_reason = "max_tool_iterations"
|
||
session_manager.append_message(
|
||
resolved_session_id,
|
||
run_id=resolved_run_id,
|
||
role="assistant",
|
||
event_type="assistant_message_added",
|
||
content=final_text,
|
||
finish_reason=final_finish_reason,
|
||
source=source,
|
||
title=title,
|
||
model=final_model,
|
||
user_id=user_id,
|
||
)
|
||
context_builder.add_assistant_message(
|
||
messages,
|
||
content=final_text,
|
||
)
|
||
break
|
||
|
||
iterations += 1
|
||
for tool_call in response.tool_calls:
|
||
result = await tool_executor.execute_tool_call(tool_call, context=tool_context)
|
||
session_manager.append_message(
|
||
resolved_session_id,
|
||
run_id=resolved_run_id,
|
||
role="tool",
|
||
event_type="tool_result_recorded",
|
||
event_payload={
|
||
"success": result.success,
|
||
"error": result.error,
|
||
},
|
||
content=result.content,
|
||
tool_name=result.tool_name,
|
||
tool_call_id=tool_call.id,
|
||
source=source,
|
||
title=title,
|
||
model=final_model,
|
||
user_id=user_id,
|
||
)
|
||
context_builder.add_tool_result(
|
||
messages,
|
||
tool_call_id=tool_call.id,
|
||
tool_name=result.tool_name,
|
||
result=result.content,
|
||
)
|
||
|
||
session_manager.append_message(
|
||
resolved_session_id,
|
||
run_id=resolved_run_id,
|
||
role="system",
|
||
event_type="run_completed",
|
||
event_payload={
|
||
"finish_reason": final_finish_reason,
|
||
"tool_iterations": iterations,
|
||
},
|
||
content=final_text,
|
||
finish_reason=final_finish_reason,
|
||
context_visible=False,
|
||
source=source,
|
||
title=title,
|
||
model=final_model,
|
||
user_id=user_id,
|
||
)
|
||
return AgentRunResult(
|
||
session_id=resolved_session_id,
|
||
run_id=resolved_run_id,
|
||
output_text=final_text,
|
||
finish_reason=final_finish_reason,
|
||
tool_iterations=iterations,
|
||
provider_name=final_provider_name,
|
||
model=final_model,
|
||
usage=final_usage,
|
||
)
|
||
except Exception as exc:
|
||
if not user_message_recorded:
|
||
session_manager.append_message(
|
||
resolved_session_id,
|
||
run_id=resolved_run_id,
|
||
role="user",
|
||
event_type="user_message_added",
|
||
content=task,
|
||
source=source,
|
||
title=title,
|
||
model=resolved_model,
|
||
user_id=user_id,
|
||
)
|
||
return self._build_error_result(
|
||
session_manager=session_manager,
|
||
session_id=resolved_session_id,
|
||
run_id=resolved_run_id,
|
||
source=source,
|
||
title=title,
|
||
user_id=user_id,
|
||
model=final_model or resolved_model,
|
||
message=f"Run failed before completion: {exc}",
|
||
tool_iterations=iterations,
|
||
provider_name=final_provider_name,
|
||
usage=final_usage,
|
||
)
|
||
|
||
def _require_loaded(self, field_name: str) -> Any:
|
||
loaded = self.boot()
|
||
value = getattr(loaded, field_name)
|
||
if value is None:
|
||
raise RuntimeError(f"Engine loader did not provide required dependency {field_name!r}")
|
||
return value
|
||
|
||
@staticmethod
|
||
def _serialize_tool_calls(tool_calls: list[Any]) -> list[dict[str, Any]]:
|
||
payload: list[dict[str, Any]] = []
|
||
for tool_call in tool_calls:
|
||
payload.append(
|
||
{
|
||
"id": tool_call.id,
|
||
"type": "function",
|
||
"function": {
|
||
"name": tool_call.name,
|
||
"arguments": tool_call.arguments,
|
||
},
|
||
}
|
||
)
|
||
return payload
|
||
|
||
@staticmethod
|
||
def _record_usage(session_manager: Any, session_id: str, usage: dict[str, Any]) -> None:
|
||
"""把 provider usage 映射到 session usage 字段。
|
||
|
||
这里先做最常见字段的最小映射：
|
||
- prompt_tokens -> input_tokens
|
||
- completion_tokens -> output_tokens
|
||
|
||
后面如果 provider 层补了更细的 cache/reasoning/cost，再往这里扩。
|
||
"""
|
||
|
||
if not usage:
|
||
return
|
||
session_manager.update_usage(
|
||
session_id,
|
||
input_tokens=int(usage.get("input_tokens", usage.get("prompt_tokens", 0)) or 0),
|
||
output_tokens=int(usage.get("output_tokens", usage.get("completion_tokens", 0)) or 0),
|
||
reasoning_tokens=int(usage.get("reasoning_tokens", 0) or 0),
|
||
)
|
||
|
||
@staticmethod
|
||
def _merge_usage(total: dict[str, Any], delta: dict[str, Any]) -> dict[str, Any]:
|
||
"""把多轮 provider usage 合并成一次 run 的累计 usage。"""
|
||
|
||
merged = dict(total)
|
||
for key, value in delta.items():
|
||
if isinstance(value, (int, float)) and isinstance(merged.get(key, 0), (int, float)):
|
||
merged[key] = merged.get(key, 0) + value
|
||
else:
|
||
merged[key] = value
|
||
return merged
|
||
|
||
@staticmethod
|
||
def _build_error_result(
|
||
*,
|
||
session_manager: Any,
|
||
session_id: str,
|
||
run_id: str,
|
||
source: str,
|
||
title: str | None,
|
||
user_id: str | None,
|
||
model: str | None,
|
||
message: str,
|
||
tool_iterations: int,
|
||
provider_name: str | None,
|
||
usage: dict[str, Any],
|
||
) -> AgentRunResult:
|
||
"""把主链中的未处理异常收口成可追踪的 assistant error turn。"""
|
||
|
||
session_manager.append_message(
|
||
session_id,
|
||
run_id=run_id,
|
||
role="assistant",
|
||
event_type="assistant_message_added",
|
||
content=message,
|
||
finish_reason="error",
|
||
source=source,
|
||
title=title,
|
||
model=model,
|
||
user_id=user_id,
|
||
)
|
||
session_manager.append_message(
|
||
session_id,
|
||
run_id=run_id,
|
||
role="system",
|
||
event_type="run_failed",
|
||
event_payload={
|
||
"tool_iterations": tool_iterations,
|
||
"provider_name": provider_name,
|
||
},
|
||
content=message,
|
||
finish_reason="error",
|
||
context_visible=False,
|
||
source=source,
|
||
title=title,
|
||
model=model,
|
||
user_id=user_id,
|
||
)
|
||
return AgentRunResult(
|
||
session_id=session_id,
|
||
run_id=run_id,
|
||
output_text=message,
|
||
finish_reason="error",
|
||
tool_iterations=tool_iterations,
|
||
provider_name=provider_name,
|
||
model=model,
|
||
usage=usage,
|
||
)
|