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
303 lines
13 KiB
Python
303 lines
13 KiB
Python
"""Bridge from nanobot agent-team tasks into the vendored swarms runtime."""
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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 pathlib import Path
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from typing import Any
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from nanobot.agent.agent_registry import AgentRegistry
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from nanobot.agent.process_events import emit_process_event
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from nanobot.agent.run_result import has_meaningful_summary
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from nanobot.agent_team.swarms_adapter import MemberRunner, NanobotAgentAdapter, load_swarms_runtime
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from nanobot.agent_team.types import (
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BridgeAttempt,
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BridgeResult,
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ExecutionMode,
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ProcedureRecord,
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SwarmsRunResult,
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SwarmsRunSpec,
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)
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class SwarmsBridge:
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"""Execute a `SwarmsRunSpec` with `SwarmRouter` and normalize the output."""
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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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member_runner: MemberRunner,
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) -> None:
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self.workspace = workspace
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self.registry = registry
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self.member_runner = member_runner
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async def run_spec(self, *, spec: SwarmsRunSpec, run_id: str) -> BridgeResult:
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# 先发一条过程事件，告诉上层“swarms 执行阶段已经开始”。
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# metadata 里带完整 spec，便于前端或日志侧排查本次实际执行参数。
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await self._emit_progress(
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run_id,
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f"Starting swarms run: {spec.swarm_type}.",
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stage_label="启动 swarms runtime",
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metadata={"spec": spec.to_dict()},
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)
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# 真正调用 swarms runtime，返回的是“桥接层内部使用”的 SwarmsRunResult。
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swarms_result = await self._run_swarms(spec=spec, run_id=run_id)
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# success 不只看 swarms_result.success，还要求 summary 有实际内容。
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# 这样可以避免 runtime technically 跑完了，但最终没有任何可消费结论时，
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# 上层误把它当成一次成功执行。
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success = swarms_result.success and has_meaningful_summary(swarms_result.summary)
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error = None if success else (swarms_result.error or swarms_result.summary)
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# BridgeAttempt 表示“这次 swarms 模式尝试”的完整快照；
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# 后续 BridgeResult.attempts 可以累计不同执行策略/回退路径的尝试记录。
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attempt = BridgeAttempt(
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mode=ExecutionMode.SWARMS,
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success=success,
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summary=swarms_result.summary,
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error=error,
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member_results=list(swarms_result.member_results),
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targets=list(spec.agent_ids),
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raw={
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"spec": spec.to_dict(),
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"swarms_result": swarms_result.to_dict(),
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},
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)
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# 只有成功时才生成 candidate procedure，避免把失败或空结果学习成可复用流程。
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candidate = self._candidate_procedure(spec, swarms_result, run_id) if success else None
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# 再发一条归一化完成事件，让编排层知道 bridge 已经把 swarms 原始输出
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# 压成了 nanobot 可消费的标准结果结构。
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await self._emit_progress(
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run_id,
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"Swarms run returned a normalized bridge result.",
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stage_label="swarms 输出已归一",
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metadata={
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"success": success,
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"swarm_type": spec.swarm_type,
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"candidate_procedure_id": candidate.id if candidate else None,
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},
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)
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# BridgeResult 是 swarms bridge 对外暴露的稳定边界：
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# - summary/member_results 给上层公告和持久化使用
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# - attempts/raw 保留足够多细节，便于后续解释和调试
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return BridgeResult(
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mode=ExecutionMode.SWARMS,
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success=success,
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summary=swarms_result.summary,
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error=error,
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member_results=list(swarms_result.member_results),
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candidate_procedure=candidate,
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attempts=[attempt],
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raw={
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"spec": spec.to_dict(),
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"swarms_result": swarms_result.to_dict(),
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},
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)
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async def _run_swarms(self, *, spec: SwarmsRunSpec, run_id: str) -> SwarmsRunResult:
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try:
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# 先把 spec.agent_ids 解析成当前 registry 中的 AgentDescriptor。
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# 这里显式校验 agent 必须存在，避免 swarms runtime 在更深处才报模糊错误。
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descriptors = []
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for agent_id in spec.agent_ids:
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descriptor = self.registry.get_agent(agent_id)
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if descriptor is None:
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raise ValueError(f"Agent not found for swarms run: {agent_id}")
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descriptors.append(descriptor)
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# swarms runtime 运行在线程池里，但每个 NanobotAgentAdapter 最终仍要把执行
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# 切回当前事件循环中的 member_runner，因此这里提前拿到 running loop。
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loop = asyncio.get_running_loop()
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# 把 nanobot 的 AgentDescriptor 包装成 swarms 可以直接调用的 adapter。
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# swarms 视角下它们只是“可调用 agent”；nanobot 视角下它们会回流到
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# member_runner，再由本地执行器或 A2A client 真正完成任务。
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adapters = [
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NanobotAgentAdapter(
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descriptor=descriptor,
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run_id=run_id,
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loop=loop,
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member_runner=self.member_runner,
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skills=list(spec.skills),
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)
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for descriptor in descriptors
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]
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# SwarmRouter 是 vendored swarms runtime 的核心入口。
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# 这里把 planner 产出的 swarm_type / loops / flow / rules 全部映射进去。
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runtime = load_swarms_runtime()
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router = runtime["SwarmRouter"](
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name=spec.label or "nanobot-agent-team",
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description="Nanobot agent-team swarms router",
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agents=adapters,
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swarm_type=spec.swarm_type,
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max_loops=max(1, spec.max_loops),
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rearrange_flow=spec.rearrange_flow,
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rules=self._rules_with_skills(spec),
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autosave=False,
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verbose=False,
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)
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# swarms 的 router.run 是同步阻塞调用，因此放到线程池中执行，
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# 避免阻塞当前 asyncio 事件循环。
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raw_output = await asyncio.to_thread(router.run, task=self._task_with_skills(spec))
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# swarms 原始输出结构并不稳定，统一在这里归一成 SwarmsRunResult。
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return self._normalize_swarms_output(raw_output, adapters)
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except Exception as exc:
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# 桥接层把异常收口成失败结果，而不是继续向上抛，
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# 这样 orchestrator 可以用统一的 BridgeResult 流程处理失败。
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return SwarmsRunResult(
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success=False,
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summary=f"Swarms execution failed: {exc}",
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raw_output=None,
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error=str(exc),
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)
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def _rules_with_skills(self, spec: SwarmsRunSpec) -> str:
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# 把上层规则和桥接层的硬约束拼到一起：
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# 1. 保留 planner 指定的 rules
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# 2. 明确禁止 swarms 擅自引入额外 agent、工具或凭证
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# 3. 把 skills 也写入规则，确保团队行为不偏离 nanobot 约束
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parts = [
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spec.rules or "Run the nanobot agent team through swarms and produce a concise synthesis.",
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"Do not add tools, credentials, network endpoints, or agents outside the provided nanobot adapters.",
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]
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if spec.skills:
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parts.append("Required nanobot skills: " + ", ".join(spec.skills))
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return "\n".join(parts)
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def _task_with_skills(self, spec: SwarmsRunSpec) -> str:
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# skills 既体现在 rules 中，也直接拼到任务文本里，
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# 这样无论 swarms runtime 更依赖哪部分上下文，都能看到技能约束。
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if not spec.skills:
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return spec.task
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return (
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f"{spec.task}\n\n"
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"Required skills for this swarms run:\n"
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f"{', '.join(spec.skills)}"
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).strip()
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def _normalize_swarms_output(
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self,
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raw_output: Any,
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adapters: list[NanobotAgentAdapter],
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) -> SwarmsRunResult:
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# 优先从 adapters 收集每个成员真实执行后的 AgentRunResult。
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# 这些结果比 swarms runtime 的自由格式输出更稳定、也更适合后续持久化。
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member_results = [
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result
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for adapter in adapters
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for result in adapter.results
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]
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# summary 优先从成员结果推导；如果成员结果拿不到，再从 swarms 原始输出中兜底提取。
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summary = self._summary_from_swarms_output(raw_output, member_results)
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return SwarmsRunResult(
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success=bool(summary.strip()),
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summary=summary.strip(),
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raw_output=self._jsonable(raw_output),
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member_results=member_results,
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)
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def _summary_from_swarms_output(self, raw_output: Any, member_results: list[Any]) -> str:
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# 如果已经拿到了结构化 member_results，就优先用它们生成总结，
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# 因为这比直接依赖 swarms 的原始输出更稳定、更贴近 nanobot 的结果模型。
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if member_results:
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return "\n\n".join(
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f"{result.agent_name} ({result.status}):\n{result.summary}"
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for result in member_results
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if str(result.summary or "").strip()
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)
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# swarms 有时直接返回字符串，那就把它当作最终 summary。
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if isinstance(raw_output, str):
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return raw_output.strip()
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# swarms 也可能返回 transcript/list 结构；这里尝试提取非 user/system 的发言，
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# 拼成一个可读摘要。
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if isinstance(raw_output, list):
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lines: list[str] = []
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for item in raw_output:
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if not isinstance(item, dict):
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continue
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role = str(item.get("role") or item.get("speaker") or "").strip()
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content = str(item.get("content") or item.get("message") or "").strip()
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if not content or role.lower() in {"user", "system"}:
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continue
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lines.append(f"{role}: {content}" if role else content)
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if lines:
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return "\n\n".join(lines)
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# 最后兜底把原始输出尽量序列化成 JSON 文本；再不行就直接 str(...)。
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try:
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return json.dumps(raw_output, ensure_ascii=False, indent=2)
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except TypeError:
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return str(raw_output)
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def _jsonable(self, value: Any) -> Any:
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# raw_output 最终要落到 BridgeResult / RunMemory 里，因此这里尽量保证它可序列化。
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# 若原值无法直接 JSON 化，则退回字符串表示，避免整个持久化流程失败。
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try:
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json.dumps(value, ensure_ascii=False)
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return value
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except TypeError:
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return str(value)
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def _candidate_procedure(
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self,
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spec: SwarmsRunSpec,
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result: SwarmsRunResult,
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run_id: str,
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) -> ProcedureRecord:
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# bridge 只负责产出一个“可候选复用”的 procedure 草稿：
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# - task_template/agent_ids/strategy 记录执行骨架
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# - summary 提供人类可读概览
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# - metadata 记录它来自 swarms bridge
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# 真正是否持久化、如何更新统计，由更上层的 procedure memory 决定。
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return ProcedureRecord(
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task_template=spec.task,
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summary=result.summary,
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agent_ids=list(spec.agent_ids),
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strategy=spec.swarm_type,
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confidence=0.6,
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source_run_id=run_id,
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metadata={
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"source": "swarms_bridge",
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"swarm_type": spec.swarm_type,
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"auto_generated": spec.auto_generated,
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"skills": list(spec.skills),
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},
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)
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async def _emit_progress(
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self,
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run_id: str,
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text: str,
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*,
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stage_label: str,
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metadata: dict[str, Any] | None = None,
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) -> None:
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# 统一发 process_run_progress，让前端/日志看到 swarms bridge 当前阶段。
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await emit_process_event(
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"process_run_progress",
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run_id=run_id,
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actor_type="system",
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actor_id="swarms-bridge",
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actor_name="Swarms Bridge",
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text=text,
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metadata={
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"source": "swarms_bridge",
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"stage_label": stage_label,
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**(metadata or {}),
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},
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)
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