steven_li
cdfc222c9f
- 引入AgentTeamOrchestrator支持多agent协同任务执行 - 增加第三方swarms库依赖并配置git协议替换以改善包管理 - 扩展DelegationManager支持团队任务调度和进度跟踪 - 实现中文bigram分词算法提升中文任务检索准确性 - 调整A2AClient和DelegationManager超时时间从30秒增至600秒 - 优化AgentRunResult状态判断逻辑增加有意义摘要检测 - 修改Dockerfile配置npm仓库镜像地址和git协议映射 - 更新CLI命令行接口支持网关端口配置传递 - 调整提供者超时配置机制增强请求稳定性 - 移除过时的support_group字段简化agent描述符结构 - 增强错误处理和进度事件报告机制改进用户体验
312 lines
13 KiB
Python
312 lines
13 KiB
Python
"""本地委派执行器。
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这个类不再负责“后台任务管理”和“结果回流”，只保留一件事：
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在统一委派层要求执行本地任务时，提供一个受限工具集的本地 agent 执行环境。
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"""
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from __future__ import annotations
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import json
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import re
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import time as _time
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from datetime import datetime
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from pathlib import Path
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from typing import TYPE_CHECKING, Any, Awaitable, Callable
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from loguru import logger
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from nanobot.agent.run_result import AgentRunResult, has_meaningful_summary
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from nanobot.agent.tools.filesystem import EditFileTool, ListDirTool, ReadFileTool, WriteFileTool
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from nanobot.agent.tools.registry import ToolRegistry
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from nanobot.agent.tools.shell import ExecTool
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from nanobot.agent.tools.spawn import NestedDelegateTool
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from nanobot.agent.tools.web import WebFetchTool, WebSearchTool
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from nanobot.providers.base import LLMProvider
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if TYPE_CHECKING:
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from nanobot.agent.delegation import DelegationManager
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from nanobot.config.schema import ExecToolConfig
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class SubagentManager:
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"""用受限工具集在本地执行委派任务。"""
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def __init__(
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self,
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provider: LLMProvider,
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workspace: Path,
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model: str | None = None,
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temperature: float = 0.7,
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max_tokens: int = 4096,
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brave_api_key: str | None = None,
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exec_config: ExecToolConfig | None = None,
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restrict_to_workspace: bool = False,
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):
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from nanobot.config.schema import ExecToolConfig
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# 这里保存的都是本地执行所需的静态配置，不再维护后台任务表。
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self.provider = provider
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self.workspace = workspace
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self.model = model or provider.get_default_model()
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self.temperature = temperature
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self.max_tokens = max_tokens
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self.brave_api_key = brave_api_key
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self.exec_config = exec_config or ExecToolConfig()
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self.restrict_to_workspace = restrict_to_workspace
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self._nested_delegate: DelegationManager | None = None
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def set_nested_delegate(self, manager: "DelegationManager | None") -> None:
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"""注入 delegated worker 可用的受控下游委派器。"""
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self._nested_delegate = manager
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async def run_local_task(
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self,
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task: str,
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label: str | None = None,
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agent_id: str = "local-subagent",
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agent_name: str = "Local Subagent",
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system_prompt: str | None = None,
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model: str | None = None,
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progress_callback: Callable[..., Awaitable[None]] | None = None,
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allow_nested_delegation: bool = True,
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skill_context: str = "",
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skill_names: list[str] | None = None,
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) -> AgentRunResult:
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"""执行一次本地委派任务，并返回结构化结果。"""
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# 每次任务都新建一套局部工具注册表，避免不同任务之间共享临时状态。
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tools = self._build_local_tools(
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allow_nested_delegation=allow_nested_delegation,
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skill_names=skill_names,
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)
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prompt = self._build_subagent_prompt(
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task,
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agent_name=agent_name,
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custom_system_prompt=system_prompt,
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allow_nested_delegation=allow_nested_delegation,
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skill_context=skill_context,
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)
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# 本地委派不共享主会话历史，只带“专用 system prompt + 当前任务”。
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messages: list[dict[str, Any]] = [
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{"role": "system", "content": prompt},
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{"role": "user", "content": task},
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]
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# 本地子 agent 也走“模型 -> 工具 -> 模型”的短循环，但轮数更保守。
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max_iterations = 15
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iteration = 0
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final_result: str | None = None
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while iteration < max_iterations:
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iteration += 1
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response = await self.provider.chat(
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messages=messages,
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tools=tools.get_definitions(),
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model=model or self.model,
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temperature=self.temperature,
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max_tokens=self.max_tokens,
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)
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if response.has_tool_calls:
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if progress_callback:
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# 进度回调只发对用户有价值的文本，不把 `<think>` 之类内部推理暴露出去。
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clean = self._strip_think(response.content)
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if clean:
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await progress_callback(clean, tool_hint=False)
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# 额外补一条短工具提示，让上层 UI 知道当前在做什么。
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await progress_callback(self._tool_hint(response.tool_calls), tool_hint=True)
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tool_call_dicts = [
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{
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"id": tc.id,
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"type": "function",
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"function": {
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"name": tc.name,
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"arguments": json.dumps(tc.arguments, ensure_ascii=False),
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},
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}
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for tc in response.tool_calls
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]
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messages.append({
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"role": "assistant",
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"content": response.content or "",
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"tool_calls": tool_call_dicts,
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})
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for tool_call in response.tool_calls:
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args_str = json.dumps(tool_call.arguments, ensure_ascii=False)
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logger.debug("Agent [{}] executing: {} with arguments: {}", agent_id, tool_call.name, args_str)
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# 真正执行工具后，把结果回填到 messages，让下一轮模型能看到执行结果。
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result = await tools.execute(tool_call.name, tool_call.arguments)
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messages.append({
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"role": "tool",
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"tool_call_id": tool_call.id,
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"name": tool_call.name,
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"content": result,
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})
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else:
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# 没有继续调用工具时，视为任务已收敛，直接采纳当前回复。
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final_result = response.content
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break
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status = "ok"
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raw: dict[str, Any] | None = None
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if not has_meaningful_summary(final_result):
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# 兜底避免出现“任务做完了但完全没文本”的空结果，并显式标记为失败，
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# 防止上层把这类占位结果学习成 procedure。
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final_result = "Task completed but no final response was generated."
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status = "error"
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raw = {
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"reason": "no_final_response_generated",
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"iterations": iteration,
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}
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return AgentRunResult(
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agent_id=agent_id,
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agent_name=agent_name,
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status=status,
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summary=final_result,
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raw=raw,
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)
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def _build_local_tools(
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self,
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*,
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allow_nested_delegation: bool,
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skill_names: list[str] | None = None,
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) -> ToolRegistry:
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"""构建本地委派可用的受限工具集。"""
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tools = ToolRegistry()
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allowed_dir = self.workspace if self.restrict_to_workspace else None
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protected_skill_paths = [self.workspace / "skills"]
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# 文件工具统一按相同的 workspace / allowed_dir 约束注册。
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tools.register(ReadFileTool(workspace=self.workspace, allowed_dir=allowed_dir))
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tools.register(ListDirTool(workspace=self.workspace, allowed_dir=allowed_dir))
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tools.register(
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WriteFileTool(
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workspace=self.workspace,
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allowed_dir=allowed_dir,
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protected_paths=protected_skill_paths,
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)
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)
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tools.register(
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EditFileTool(
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workspace=self.workspace,
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allowed_dir=allowed_dir,
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protected_paths=protected_skill_paths,
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)
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)
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# 本地命令执行沿用主配置里的超时和 workspace 限制。
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tools.register(ExecTool(
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working_dir=str(self.workspace),
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timeout=self.exec_config.timeout,
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restrict_to_workspace=self.restrict_to_workspace,
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protected_paths=protected_skill_paths,
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))
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# 网络能力保持只读：搜索和抓取，不提供消息发送/再次委派等工具。
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tools.register(WebSearchTool(api_key=self.brave_api_key))
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tools.register(WebFetchTool())
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if allow_nested_delegation and self._nested_delegate is not None:
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tools.register(NestedDelegateTool(manager=self._nested_delegate, default_skills=skill_names))
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return tools
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@staticmethod
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def _strip_think(text: str | None) -> str | None:
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"""Remove provider-specific think blocks from visible progress text."""
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if not text:
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return None
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return re.sub(r"<think>[\s\S]*?</think>", "", text).strip() or None
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@staticmethod
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def _tool_hint(tool_calls: list) -> str:
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"""把工具调用列表格式化成简短进度提示。"""
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def _fmt(tc):
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val = next(iter(tc.arguments.values()), None) if tc.arguments else None
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if not isinstance(val, str):
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return tc.name
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return f'{tc.name}("{val[:40]}...")' if len(val) > 40 else f'{tc.name}("{val}")'
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return ", ".join(_fmt(tc) for tc in tool_calls)
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def _build_subagent_prompt(
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self,
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task: str,
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agent_name: str = "Local Subagent",
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custom_system_prompt: str | None = None,
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allow_nested_delegation: bool = True,
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skill_context: str = "",
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) -> str:
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"""构建子代理专用 system prompt。"""
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now = datetime.now().strftime("%Y-%m-%d %H:%M (%A)")
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tz = _time.strftime("%Z") or "UTC"
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# plugin agent 的自定义系统提示拼到末尾，保留通用约束，再叠加个性化指令。
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extra = f"\n\n## Agent Instructions\n{custom_system_prompt.strip()}" if custom_system_prompt else ""
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can_do_lines = [
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"- Read and write files in the workspace",
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"- Execute shell commands",
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"- Search the web and fetch web pages",
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"- Complete the task thoroughly",
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]
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cannot_do_lines = [
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"- Send messages directly to users (no message tool available)",
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"- Access the main agent's conversation history",
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]
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delegation_section = (
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"\n## Downstream Delegation\n"
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"- Do not delegate further. Complete the task yourself with the tools you have."
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)
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if allow_nested_delegation and self._nested_delegate is not None:
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can_do_lines.append(
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"- Use `delegate_task` for controlled downstream delegation when specialized help is required"
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)
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cannot_do_lines.append("- Do not start agent teams or use background delegation tools")
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nested_summary = self._nested_delegate.build_nested_agents_summary()
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summary_block = f"\n\n{nested_summary}" if nested_summary else ""
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delegation_section = (
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"\n## Downstream Delegation\n"
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"- Use `delegate_task` only when a specialized downstream worker is actually needed.\n"
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"- `strategy=\"a2a\"` delegates directly to an available A2A agent.\n"
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"- `strategy=\"ephemeral_subagent\"` runs a temporary local worker for this task only.\n"
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"- Never create, register, or persist a new local sub-agent through `subagentctl.py`, `/api/subagents`, or registry edits."
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f"{summary_block}"
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)
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else:
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cannot_do_lines.append("- Spawn other subagents or downstream workers")
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skill_section = f"\n## Required Skills\n{skill_context.strip()}" if skill_context.strip() else ""
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return f"""# {agent_name}
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## Current Time
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{now} ({tz})
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You are a delegated agent spawned by the main agent to complete a specific task.
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## Rules
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1. Stay focused - complete only the assigned task, nothing else
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2. Your final response will be reported back to the main agent
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3. Do not initiate conversations or take on side tasks
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4. Be concise but informative in your findings
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5. Do not create or modify persistent local sub-agents unless the task explicitly requires that workflow
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## What You Can Do
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{chr(10).join(can_do_lines)}
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## What You Cannot Do
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{chr(10).join(cannot_do_lines)}
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{delegation_section}
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{skill_section}
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## Workspace
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Your workspace is at: {self.workspace}
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Skills are available at: {self.workspace}/skills/ (read SKILL.md files as needed)
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## Special Workflow
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- If the task is about creating, updating, repairing, or deleting a persistent local sub-agent, read `skills/subagent-manager/SKILL.md` before making changes.
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- For persistent local sub-agents, follow only the canonical workflow from that skill.
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- Do not manually create `workspace/agents/<id>/agent.json` as a substitute for a persistent sub-agent.
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- Do not manually edit `workspace/agents/registry.json` to register a persistent sub-agent.
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- A valid persistent sub-agent must be created through `subagentctl.py` or `/api/subagents` and must end up at `workspace/agents/<id>_agent/AGENTS.json`.
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When you have completed the task, provide a clear summary of your findings or actions.{extra}"""
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