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
185 lines
7.3 KiB
Python
185 lines
7.3 KiB
Python
"""Planner that prepares a minimal swarms run spec for agent-team tasks."""
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from __future__ import annotations
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import asyncio
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import json
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from typing import Any
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from loguru import logger
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from nanobot.agent.agent_registry import AgentRegistry
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from nanobot.agent_team.memory import ProcedureMemory
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from nanobot.agent_team.swarms_adapter import load_swarms_runtime, safe_swarms_name
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from nanobot.agent_team.swarms_policy import SwarmsPolicy
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from nanobot.agent_team.target_resolver import TargetResolver
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from nanobot.agent_team.types import SwarmsRunSpec
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class SwarmsRunPlanner:
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"""Generate `SwarmsRunSpec` without rebuilding swarms' own planner/runtime."""
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def __init__(
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self,
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*,
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model: str | None,
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registry: AgentRegistry,
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target_resolver: TargetResolver,
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procedure_memory: ProcedureMemory,
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policy: SwarmsPolicy,
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) -> None:
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self.model = model
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self.registry = registry
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self.target_resolver = target_resolver
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self.procedure_memory = procedure_memory
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self.policy = policy
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async def plan(self, *, task: str, label: str, skills: list[str]) -> SwarmsRunSpec:
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memory_hint = self.procedure_memory.match_procedure(task)
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if self._should_auto_build(task, skills, memory_hint):
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raw_config = await self._run_auto_swarm_builder(task, skills, memory_hint)
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return await self._spec_from_auto_config(task, label, skills, raw_config)
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target_plan = await self.target_resolver.resolve_team_targets(
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task=task,
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skills=skills,
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required_specialists=self._simple_required_roles(task, skills),
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)
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return SwarmsRunSpec(
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task=task,
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label=label,
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skills=list(skills),
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swarm_type="GroupChat",
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agent_ids=list(target_plan.final_targets),
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auto_generated=False,
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max_loops=2,
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rules=self._default_rules(),
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metadata={
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"memory_hint": memory_hint.id if memory_hint else None,
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"target_plan": target_plan.to_dict(),
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},
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)
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def _should_auto_build(self, task: str, skills: list[str], memory_hint: Any) -> bool:
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source = task or ""
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text = source.lower()
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markers = ("架构", "调研", "复杂", "多阶段", "strategy", "architecture", "research")
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return len(source) > 80 or memory_hint is not None or any(
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marker in source or marker in text for marker in markers
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)
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async def _run_auto_swarm_builder(self, task: str, skills: list[str], memory_hint: Any) -> dict[str, Any]:
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try:
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runtime = load_swarms_runtime()
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builder = runtime["AutoSwarmBuilder"](
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name="nanobot-auto-swarm-builder",
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description="Generate a safe swarms router config for nanobot",
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max_loops=1,
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model_name=self._auto_builder_model_name(),
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generate_router_config=True,
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execution_type="return-swarm-router-config",
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interactive=False,
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verbose=False,
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)
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raw = await asyncio.to_thread(
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builder.run,
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self._auto_builder_prompt(task, skills, memory_hint),
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)
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if isinstance(raw, dict):
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return raw
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if isinstance(raw, str):
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return json.loads(raw)
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model_dump = getattr(raw, "model_dump", None)
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if callable(model_dump):
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payload = model_dump()
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return payload if isinstance(payload, dict) else {}
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except Exception as exc:
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logger.warning("AutoSwarmBuilder failed; falling back to deterministic run spec: {}", exc)
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return {}
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def _auto_builder_model_name(self) -> str:
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model_name = str(self.model or "").strip()
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if not model_name:
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return "gpt-4.1"
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if "/" in model_name:
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return model_name
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return f"openai/{model_name}"
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def _auto_builder_prompt(self, task: str, skills: list[str], memory_hint: Any) -> str:
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return (
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"Build a multi-agent swarm router config for nanobot.\n\n"
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f"User task:\n{task}\n\n"
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f"Required nanobot skills:\n{skills}\n\n"
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f"Procedure memory hint:\n{memory_hint}\n\n"
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"Return a valid JSON object that matches the swarm router config schema.\n\n"
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"Hard constraints:\n"
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"- Every generated role must follow the listed skills.\n"
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"- Do not replace, ignore, or reinterpret the listed skills.\n"
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"- Do not add external tools, credentials, MCP URLs, or hidden side effects.\n"
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"- Prefer existing nanobot registry agents; only describe missing roles."
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)
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async def _spec_from_auto_config(
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self,
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task: str,
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label: str,
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skills: list[str],
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raw_config: dict[str, Any],
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) -> SwarmsRunSpec:
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safe_config = self.policy.validate_auto_config(raw_config)
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target_plan = await self.target_resolver.resolve_team_targets(
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task=task,
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skills=skills,
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required_specialists=self._roles_from_auto_config(safe_config),
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)
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return SwarmsRunSpec(
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task=task,
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label=label,
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skills=list(skills),
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swarm_type=str(safe_config.get("swarm_type") or "GroupChat"),
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agent_ids=list(target_plan.final_targets),
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auto_generated=bool(raw_config),
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max_loops=min(int(safe_config.get("max_loops") or 2), self.policy.max_loops),
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rearrange_flow=self._rearrange_flow(safe_config, target_plan.final_targets),
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rules=str(safe_config.get("rules") or self._default_rules()),
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raw_auto_config=safe_config,
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metadata={
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"target_plan": target_plan.to_dict(),
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"auto_builder_returned_config": bool(raw_config),
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},
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)
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def _rearrange_flow(self, config: dict[str, Any], agent_ids: list[str]) -> str | None:
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if str(config.get("swarm_type") or "") == "AgentRearrange" and agent_ids:
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return " -> ".join(safe_swarms_name(agent_id) for agent_id in agent_ids)
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flow = config.get("rearrange_flow") or config.get("flow")
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if flow:
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return str(flow)
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return None
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def _roles_from_auto_config(self, config: dict[str, Any]) -> list[str]:
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roles: list[str] = []
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for item in config.get("agents", []) or []:
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if not isinstance(item, dict):
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continue
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role = str(
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item.get("description")
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or item.get("system_prompt")
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or item.get("agent_name")
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or ""
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).strip()
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if role:
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roles.append(role)
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return roles or ["general specialist", "synthesis analyst"]
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def _simple_required_roles(self, task: str, skills: list[str]) -> list[str]:
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if skills:
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return [f"{skill} specialist" for skill in skills]
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return ["general specialist", "synthesis analyst"]
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def _default_rules(self) -> str:
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return (
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"You are running inside a nanobot agent team. Follow the provided skills, "
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"stay within your assigned role, and produce a concise final synthesis."
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)
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