修改了nanobot，往Hermes agent的风格走，进度1/3
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267
app-instance/backend-old/nanobot/agent_team/target_resolver.py
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267
app-instance/backend-old/nanobot/agent_team/target_resolver.py
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"""Resolve and provision team targets before execution.
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该模块负责在真正启动 agent-team / swarms 执行前，把“任务需要哪些角色”
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转换成一组可执行的 agent id。它优先复用 registry 里已有的 agent；当没有合适
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agent 覆盖某个角色时，再通过 ProvisioningManager 在本地创建 A2A specialist。
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"""
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from __future__ import annotations
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from pathlib import Path
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from loguru import logger
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from nanobot.agent.agent_registry import AgentDescriptor, AgentRegistry
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from nanobot.agent_team.provisioning import ProvisioningManager
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from nanobot.agent_team.types import ResolvedTeamPlan
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from nanobot.providers.base import LLMProvider
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class TargetResolver:
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"""把任务级的 specialist 需求解析成最终可执行的 agent id 列表。
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解析策略分两层：
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1. 先读取当前 registry 里所有可见 agent，并过滤掉 router/planner 等
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不适合作为群聊工作成员的 agent。
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2. 如果调用方明确给出 required_specialists，则把 role 和候选 agent 交给
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LLM 直接选择最合适的已有 agent；LLM 选不出来时才 provision 本地
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specialist。没有明确角色时，则直接使用过滤后的已有 agent；若为空再
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兜底创建 general specialist。
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"""
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def __init__(
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self,
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*,
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workspace: Path,
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registry: AgentRegistry,
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provider: LLMProvider,
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model: str | None = None,
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max_parallel_agents: int = 16,
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gateway_port: int = 18790,
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provisioning: ProvisioningManager | None = None,
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) -> None:
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# max_parallel_agents 同时限制“最多尝试的角色数”和“最终返回的 agent 数”，
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# 避免一次 team run 生成过多并行成员。
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self.workspace = workspace
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self.registry = registry
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self.provider = provider
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self.model = model or provider.get_default_model()
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self.max_parallel_agents = max(1, max_parallel_agents)
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self.provisioning = provisioning or ProvisioningManager(workspace, gateway_port=gateway_port)
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async def resolve_team_targets(
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self,
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*,
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task: str,
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skills: list[str] | None = None,
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required_specialists: list[str] | None = None,
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) -> ResolvedTeamPlan:
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"""解析一次 team run 的目标 agent。
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Args:
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task: 用户原始任务，用于 LLM 选 agent 和 specialist provision prompt。
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skills: 本次任务要求携带的技能列表，会传给新 provision 的 specialist。
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required_specialists: 上游 planner 推导出的角色需求。例如来自
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AutoSwarmBuilder config 的 agent description，或 skills 的简单映射。
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Returns:
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ResolvedTeamPlan: 包含已复用 agent、已 provision agent、最终执行目标、
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选择理由和审计 metadata。
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"""
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# 清理空字符串/空白角色，避免后续创建出没有意义的 specialist。
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required = [item for item in (required_specialists or []) if str(item).strip()]
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# 直接读取 registry 当前所有可见 agent，再过滤掉 router、planner、
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# local-subagent 这类不适合作为 swarms/group worker 的 agent。
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suggestions = [
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agent
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for agent in self.registry.list_agents(include_local_fallback=False)
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if self._is_group_worker_candidate(agent)
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]
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# selected: 从 registry 复用的已有 agent id。
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# covered_roles: 哪些 required role 已经被已有 agent 覆盖，用于 metadata。
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# provisioned: 为缺失角色新建/确保存在的本地 specialist id。
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# created_provisioned: 本次 run 真正新建出来的 specialist id；后续自动清理只看它，
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# 避免把之前已经存在、只是被刷新/复用的 specialist 误删。
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# actions: provision 审计记录，方便上层解释“为什么创建了某个 agent”。
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selected: list[str] = []
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covered_roles: list[str] = []
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provisioned: list[str] = []
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created_provisioned: list[str] = []
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actions: list[dict[str, str]] = []
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if required:
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# 调用方给出了明确角色时，不再做本地词法规则匹配，而是直接把
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# role + task + 候选 agent 交给 LLM 判断最适合复用哪个已有 agent。
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# 这里切片是为了遵守 max_parallel_agents 上限。
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for role in required[: self.max_parallel_agents]:
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existing = await self._select_existing_for_role_with_llm(
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task=task,
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role=role,
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suggestions=suggestions,
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selected=selected,
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)
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if existing is not None:
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selected.append(existing.id)
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covered_roles.append(role)
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continue
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provision_result = await self.provisioning.ensure_local_specialist_with_result(
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role=role,
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task=task,
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skills=skills or [],
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)
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agent_id = provision_result.agent_id
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provisioned.append(agent_id)
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if provision_result.created:
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created_provisioned.append(agent_id)
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actions.append({
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"action": "ensure_local_specialist",
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"role": role,
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"agent_id": agent_id,
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"created": str(provision_result.created).lower(),
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})
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else:
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# 没有明确角色需求时，直接使用当前可见的已有 agent，最多取并行上限。
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selected = [agent.id for agent in suggestions[: self.max_parallel_agents]]
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if not selected:
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# 当前 registry 没有可用 worker 时，创建一个通用 specialist 作为最低可执行兜底。
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provision_result = await self.provisioning.ensure_local_specialist_with_result(
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role="general specialist",
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task=task,
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skills=skills or [],
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)
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agent_id = provision_result.agent_id
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provisioned.append(agent_id)
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if provision_result.created:
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created_provisioned.append(agent_id)
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actions.append({
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"action": "ensure_local_specialist",
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"role": "general specialist",
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"agent_id": agent_id,
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"created": str(provision_result.created).lower(),
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})
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# 合并已有 agent 和新 provision 的 agent：
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# - dict.fromkeys 保留顺序并去重，避免同一个 agent 被重复加入；
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# - 最后再次截断，防止 selected + provisioned 总数超过并行上限。
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final_targets = list(dict.fromkeys([*selected, *provisioned]))[: self.max_parallel_agents]
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# selection_reason 是给上层/日志展示的粗粒度解释，metadata 里会保留更细的明细。
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reason = (
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"已选择现有 registry agent。"
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if selected and not provisioned
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else "已选择现有 registry agent，并为缺失角色补充了 specialist。"
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if selected and provisioned
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else "没有匹配到合适的现有 agent，已补充本地 A2A specialist。"
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if provisioned
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else "没有匹配到合适的现有 agent，且未补充任何 specialist。"
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)
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logger.info(
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"Resolved agent-team targets selected={} provisioned={} final={}",
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selected,
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provisioned,
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final_targets,
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)
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# ResolvedTeamPlan 是后续 orchestrator/swarms planner 使用的稳定边界：
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# final_targets 用于实际执行，selected/provisioned/actions/metadata 用于解释和调试。
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return ResolvedTeamPlan(
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selected_existing_targets=selected,
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provisioned_targets=provisioned,
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created_provisioned_targets=created_provisioned,
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final_targets=final_targets,
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selection_reason=reason,
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provision_actions=actions,
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metadata={
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"required_specialists": required,
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"available_agent_count": len(suggestions),
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"covered_roles": covered_roles,
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"created_provisioned_targets": created_provisioned,
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"max_parallel_agents": self.max_parallel_agents,
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},
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)
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@staticmethod
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def _is_group_worker_candidate(agent: AgentDescriptor) -> bool:
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"""判断一个 registry agent 是否适合作为 team/group worker。
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router/planner 类 agent 通常负责调度，不应被当作普通成员加入 GroupChat 或
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swarms worker 列表；local-subagent 是通用本地代理入口，也避免在这里重复选中。
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"""
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probe = " ".join([
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agent.id,
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agent.name,
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agent.description,
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" ".join(agent.tags),
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" ".join(agent.aliases),
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]).lower()
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if agent.id == "local-subagent":
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return False
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return not any(marker in probe for marker in ("chat-router", "router", "planner"))
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async def _select_existing_for_role_with_llm(
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self,
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*,
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task: str,
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role: str,
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suggestions: list[AgentDescriptor],
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selected: list[str],
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) -> AgentDescriptor | None:
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"""让 LLM 从已有候选 agent 中为 role 选择最合适的一个。"""
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candidates = [agent for agent in suggestions if agent.id not in selected]
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if not candidates:
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return None
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if len(candidates) == 1:
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return candidates[0]
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lines = []
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for agent in candidates:
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tags = ", ".join(agent.tags) if agent.tags else "none"
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aliases = ", ".join(agent.aliases) if agent.aliases else "none"
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lines.append(
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f"- id: {agent.id}\n"
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f" name: {agent.name}\n"
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f" description: {agent.description}\n"
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f" tags: {tags}\n"
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f" aliases: {aliases}"
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)
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try:
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response = await self.provider.chat(
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messages=[
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{
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"role": "system",
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"content": (
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"You select one existing agent for a required team role.\n"
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"Return exactly one agent id from the candidate list, or NONE.\n"
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"Do not explain your reasoning."
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),
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},
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{
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"role": "user",
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"content": (
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f"Task:\n{task}\n\n"
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f"Required role:\n{role}\n\n"
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"Candidates:\n"
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f"{chr(10).join(lines)}\n\n"
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"Return exactly one candidate id, or NONE if none of them clearly fits."
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),
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},
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],
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model=self.model,
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temperature=0,
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max_tokens=32,
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)
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except Exception as exc:
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logger.warning("LLM role selection failed for role '{}': {}", role, exc)
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return None
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raw = str(response.content or "").strip()
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choice = raw.splitlines()[0].strip().strip("`'\"") if raw else ""
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candidate_map = {agent.id: agent for agent in candidates}
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if choice in candidate_map:
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return candidate_map[choice]
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if choice.upper() not in {"", "NONE"}:
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logger.info("LLM role selection returned unknown agent id '{}' for role '{}'", choice, role)
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return None
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