steven_li
a27560102b
添加了 `revise_task` 路由动作类型，允许用户修改、纠正或重新执行最新活动任务结果。 实现了工具失败指导原则，防止相同类别工具重复失败。 为任务规划器添加了超时处理机制，避免长时间等待。 BREAKING CHANGE: 任务路由逻辑已更新，新增 `revise_task` 动作类型。 fix(api): 修复任务详情API返回完整流程投影 修复了任务详情API端点，现在会包含过滤后的流程运行、事件和工件信息， 并确保时间戳字段正确序列化。 refactor(engine): 优化任务技能解析器摘要节点处理 改进了任务技能解析器对摘要节点的处理逻辑，对于仅依赖文本生成功能的摘要节 点不再分配具体技能，直接使用依赖项输出进行汇总。 test: 增加任务修订和超时处理测试用例 添加了测试用例验证任务修订输入记录反馈、超时回退到单模式以及 摘要节点技能解析等新功能。
1265 lines
50 KiB
Python
1265 lines
50 KiB
Python
"""Application service for agent entry.
|
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|
||
这层的职责是把“接口层如何调用 AgentLoop”统一收口。
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接口层以后不应该各自做这些事情：
|
||
1. 自己 new `AgentLoop`
|
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2. 自己决定何时 `boot()`
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3. 自己处理 direct run 的同步/异步包装
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统一放在 `AgentService` 后，CLI / Web / Gateway 才能共享同一条运行主链。
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"""
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from __future__ import annotations
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import asyncio
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from pathlib import Path
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from typing import Any
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from uuid import uuid4
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|
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from beaver.coordinator.models import ExecutionNode, TeamRunResult
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from beaver.engine import AgentLoop, AgentProfile, AgentRunResult, EngineLoader
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from beaver.engine.providers import make_provider_bundle
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from beaver.foundation.events import InboundMessage, OutboundMessage
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from beaver.foundation.models import CronJob, CronRunRecord
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from beaver.tasks import MainAgentRouter, TaskExecutionPlan, TaskRecord, ValidationResult
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NOTIFICATION_SESSION_ID = "notify:default:scheduled"
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class AgentService:
|
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"""面向 interfaces 的统一 agent 运行入口。
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这里明确区分两种调用模式：
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1. direct mode
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- 不启动后台运行循环
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- 直接调用 `process_direct()` / `run_direct()`
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2. running mode
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- 先 `await start()`
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- 之后所有外部任务都必须走 `submit_direct()`
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- 不允许再直接调用 `process_direct()`
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"""
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def __init__(
|
||
self,
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*,
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||
workspace: str | Path | None = None,
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||
config_path: str | Path | None = None,
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||
profile: AgentProfile | None = None,
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||
loader: EngineLoader | None = None,
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||
) -> None:
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||
self.profile = profile or AgentProfile()
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||
self.loader = loader or EngineLoader(workspace=workspace, config_path=config_path)
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self._loop: AgentLoop | None = None
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self._run_task: asyncio.Task[None] | None = None
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self._main_agent_router = MainAgentRouter()
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||
self._runtime_services: dict[str, Any] = {}
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def create_loop(self) -> AgentLoop:
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"""创建并缓存当前 service 使用的 AgentLoop。"""
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||
|
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if self._loop is None:
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self._loop = AgentLoop(profile=self.profile, loader=self.loader)
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self._loop.runtime_services.update(self._runtime_services)
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self._loop.boot()
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return self._loop
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||
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def register_runtime_service(self, name: str, service: Any) -> None:
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"""Expose process-level services to tools during agent runs."""
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self._runtime_services[name] = service
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if self._loop is not None:
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self._loop.runtime_services[name] = service
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@property
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def has_loop(self) -> bool:
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"""当前 service 是否已经创建过 loop。"""
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return self._loop is not None
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@property
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def is_running(self) -> bool:
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"""当前 service 是否处于 running mode。"""
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||
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||
return self._run_task is not None and not self._run_task.done()
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def close(self) -> None:
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"""关闭当前 service 持有的 runtime。"""
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if self._run_task is not None and not self._run_task.done():
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raise RuntimeError("AgentService.close() requires stop() before closing a running loop")
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self._run_task = None
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if self._loop is None:
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return
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try:
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self._loop.close()
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||
finally:
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self._loop = None
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||
|
||
async def start(self) -> None:
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||
"""启动后台运行循环，进入 running mode。
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||
|
||
进入 running mode 后：
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||
- 外部任务必须通过 `submit_direct()` 提交
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- `process_direct()` 不再允许直接调用
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"""
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||
|
||
if self._run_task is not None and not self._run_task.done():
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return
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loop = self.create_loop()
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self._run_task = asyncio.create_task(loop.run())
|
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while not loop.is_running:
|
||
if self._run_task.done():
|
||
await self._run_task
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break
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await asyncio.sleep(0)
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|
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async def _stop_impl(
|
||
self,
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*,
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||
timeout_seconds: float | None = None,
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||
force: bool = False,
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||
) -> None:
|
||
"""内部停止实现，支持 graceful timeout 和可选 force cancel。"""
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||
|
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if self._run_task is None:
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return
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run_task = self._run_task
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loop = self.create_loop()
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try:
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await loop.stop()
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if timeout_seconds is None:
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await run_task
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else:
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try:
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await asyncio.wait_for(asyncio.shield(run_task), timeout=timeout_seconds)
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except asyncio.TimeoutError as exc:
|
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if force:
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run_task.cancel()
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try:
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await run_task
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except asyncio.CancelledError:
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pass
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else:
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raise TimeoutError(
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f"AgentService.stop() timed out after {timeout_seconds} seconds while draining queued tasks"
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) from exc
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finally:
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if run_task.done():
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self._run_task = None
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async def stop(
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self,
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*,
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timeout_seconds: float | None = None,
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||
force: bool = False,
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) -> None:
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"""停止后台运行循环并等待退出。
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参数：
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- `timeout_seconds`: graceful drain 的最长等待时间；`None` 表示一直等
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- `force`: 超时后是否 cancel 掉运行循环 task
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"""
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await self._stop_impl(timeout_seconds=timeout_seconds, force=force)
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async def shutdown(
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self,
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*,
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timeout_seconds: float | None = None,
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||
force: bool = False,
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) -> None:
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"""先停运行循环，再释放 runtime。"""
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await self._stop_impl(timeout_seconds=timeout_seconds, force=force)
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self.close()
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async def process_direct(
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self,
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message: str,
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**kwargs: Any,
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) -> AgentRunResult:
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"""异步 direct run 入口。
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仅在 direct mode 下可用。
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如果 service 已经 `start()` 进入 running mode，
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调用方必须改用 `submit_direct()`，不能绕过运行队列直接执行。
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"""
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if self._run_task is not None and not self._run_task.done():
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raise RuntimeError(
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"AgentService.process_direct() is unavailable while the service is running; "
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"use 'await AgentService.submit_direct(...)' after start()."
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)
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loop = self.create_loop()
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return await self._process_with_main_agent(message, runner=loop.process_direct, kwargs=kwargs)
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async def submit_direct(
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self,
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message: str,
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**kwargs: Any,
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) -> AgentRunResult:
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"""向 running mode 下的 loop 提交 direct task。
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这是 `start()` 之后唯一合法的外部任务入口。
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"""
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loop = self.create_loop()
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return await self._process_with_main_agent(message, runner=loop.submit_direct, kwargs=kwargs)
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async def run_scheduled_task(
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self,
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message: str,
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*,
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||
session_id: str,
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cron_job_id: str,
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cron_job_name: str,
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scheduled_run_id: str | None = None,
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requires_followup: bool = False,
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||
) -> AgentRunResult:
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"""Run a cron trigger as a normal internal Task.
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Scheduled jobs are product-level Tasks, not hidden one-off agent turns.
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This entry bypasses the main-agent classifier and forces Task mode so
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every trigger produces a TaskRecord, validation, feedback state, and a
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run_id that the scheduled-task history can link to.
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||
"""
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||
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loaded = self.create_loop().boot()
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task_service = self._require_loaded(loaded, "task_service")
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loop = self.create_loop()
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task = task_service.create_task(
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session_id=session_id,
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description=message,
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creator="cron",
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metadata={
|
||
"source": "scheduled_cron",
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||
"cron_job_id": cron_job_id,
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||
"cron_job_name": cron_job_name,
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||
"scheduled_run_id": scheduled_run_id,
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||
"user_engaged": False,
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||
"requires_followup": requires_followup,
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||
},
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||
)
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||
execution_context = (
|
||
"This turn was triggered automatically by a scheduled task.\n\n"
|
||
f"Cron Job ID: {cron_job_id}\n"
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||
f"Cron Job Name: {cron_job_name}\n"
|
||
f"Scheduled Run ID: {scheduled_run_id or 'unknown'}\n"
|
||
"Run it as a normal Beaver Task. Do not ask the user for confirmation; "
|
||
"execute the task and report the concrete outcome."
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||
)
|
||
runner = loop.submit_direct if self.is_running else loop.process_direct
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||
result = await self._run_task_mode(
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message,
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runner=runner,
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||
task=task,
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||
kwargs={
|
||
"session_id": session_id,
|
||
"source": "cron",
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"user_id": "cron",
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"title": cron_job_name,
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"execution_context": execution_context,
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},
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||
)
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||
loaded = self.create_loop().boot()
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||
session_manager = self._require_loaded(loaded, "session_manager")
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||
session_manager.update_latest_assistant_event_payload(
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||
result.session_id,
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||
result.run_id,
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||
{
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||
"message_type": "scheduled_reply",
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||
"scheduled_job_id": job.id,
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||
"scheduled_run_id": run.scheduled_run_id,
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||
"cron_job_name": job.name,
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"mode": "notification",
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},
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||
)
|
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return result
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||
|
||
async def run_scheduled_notification(
|
||
self,
|
||
message: str,
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||
*,
|
||
session_id: str = NOTIFICATION_SESSION_ID,
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||
cron_job_id: str,
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||
cron_job_name: str,
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||
scheduled_run_id: str,
|
||
) -> AgentRunResult:
|
||
"""Run a cron trigger as a notification result, not as an active Task."""
|
||
|
||
loop = self.create_loop()
|
||
loaded = loop.boot()
|
||
session_manager = self._require_loaded(loaded, "session_manager")
|
||
runner = loop.submit_direct if self.is_running else loop.process_direct
|
||
execution_context = (
|
||
"This turn was triggered automatically by a scheduled notification.\n\n"
|
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f"Cron Job ID: {cron_job_id}\n"
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||
f"Cron Job Name: {cron_job_name}\n"
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||
f"Scheduled Run ID: {scheduled_run_id}\n"
|
||
"Generate the notification content directly for the user. Do not ask for confirmation."
|
||
)
|
||
result = await runner(
|
||
message,
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session_id=session_id,
|
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source="notification",
|
||
user_id="cron",
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title=cron_job_name,
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||
execution_context=execution_context,
|
||
)
|
||
session_manager.update_latest_assistant_event_payload(
|
||
result.session_id,
|
||
result.run_id,
|
||
{
|
||
"message_type": "scheduled_result",
|
||
"scheduled_job_id": cron_job_id,
|
||
"scheduled_run_id": scheduled_run_id,
|
||
"cron_job_name": cron_job_name,
|
||
"mode": "notification",
|
||
},
|
||
)
|
||
return result
|
||
|
||
def engage_scheduled_run(
|
||
self,
|
||
*,
|
||
job: CronJob,
|
||
run: CronRunRecord,
|
||
intent: str = "revise_once",
|
||
thinking_enabled: bool | None = None,
|
||
) -> TaskRecord:
|
||
"""Create or mark the Task that lets the user work on a scheduled result."""
|
||
|
||
loaded = self.create_loop().boot()
|
||
task_service = self._require_loaded(loaded, "task_service")
|
||
if run.task_id:
|
||
existing = task_service.get_task(run.task_id)
|
||
if existing is not None:
|
||
existing.metadata["user_engaged"] = True
|
||
existing.metadata["engage_intent"] = intent
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||
task_service.store.upsert_task(existing)
|
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return existing
|
||
|
||
task = task_service.create_task(
|
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session_id=run.notification_session_id or NOTIFICATION_SESSION_ID,
|
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description=f"修改定时通知：{job.name}",
|
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creator="cron",
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metadata={
|
||
"source": "scheduled_run",
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"cron_job_id": job.id,
|
||
"cron_job_name": job.name,
|
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"scheduled_run_id": run.scheduled_run_id,
|
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"scheduled_output": run.output,
|
||
"user_engaged": True,
|
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"engage_intent": intent,
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},
|
||
)
|
||
return task
|
||
|
||
async def submit_scheduled_reply(
|
||
self,
|
||
message: str,
|
||
*,
|
||
job: CronJob,
|
||
run: CronRunRecord,
|
||
intent: str = "revise_once",
|
||
) -> AgentRunResult:
|
||
task = self.engage_scheduled_run(job=job, run=run, intent=intent)
|
||
loop = self.create_loop()
|
||
runner = loop.submit_direct if self.is_running else loop.process_direct
|
||
execution_context = (
|
||
"The user is replying to a scheduled notification result.\n\n"
|
||
f"Cron Job ID: {job.id}\n"
|
||
f"Cron Job Name: {job.name}\n"
|
||
f"Scheduled Run ID: {run.scheduled_run_id}\n"
|
||
f"Engagement intent: {intent}\n"
|
||
f"Original scheduled instruction: {job.payload.message}\n"
|
||
f"Original notification output:\n{run.output or ''}\n\n"
|
||
"Handle this as a Task continuation. If the intent is update_future, explain the durable change "
|
||
"that should apply to future notifications."
|
||
)
|
||
return await self._run_task_mode(
|
||
message,
|
||
runner=runner,
|
||
task=task,
|
||
kwargs={
|
||
"session_id": task.session_id,
|
||
"source": "notification",
|
||
"user_id": "web",
|
||
"title": job.name,
|
||
"execution_context": execution_context,
|
||
"thinking_enabled": thinking_enabled,
|
||
},
|
||
)
|
||
|
||
async def submit_feedback(
|
||
self,
|
||
*,
|
||
session_id: str,
|
||
run_id: str,
|
||
feedback_type: str,
|
||
comment: str | None = None,
|
||
) -> dict[str, Any]:
|
||
"""Record chat feedback for the internal task linked to a run."""
|
||
|
||
loaded = self.create_loop().boot()
|
||
task_service = self._require_loaded(loaded, "task_service")
|
||
task = task_service.get_task_by_run_id(run_id)
|
||
if task is None or task.session_id != session_id:
|
||
raise ValueError(f"No internal task found for run_id={run_id!r}")
|
||
|
||
normalized = feedback_type.strip().lower()
|
||
if normalized not in {"satisfied", "revise", "abandon"}:
|
||
raise ValueError("feedback_type must be one of: satisfied, revise, abandon")
|
||
|
||
already_recorded = any(
|
||
item.get("run_id") == run_id and item.get("feedback_type") == normalized
|
||
for item in task.feedback
|
||
)
|
||
conflicting_feedback = next(
|
||
(
|
||
item
|
||
for item in task.feedback
|
||
if item.get("run_id") == run_id and item.get("feedback_type") != normalized
|
||
),
|
||
None,
|
||
)
|
||
if conflicting_feedback is not None:
|
||
raise ValueError(
|
||
f"Feedback for run_id={run_id!r} was already recorded as "
|
||
f"{conflicting_feedback.get('feedback_type')!r}"
|
||
)
|
||
if task.status in {"closed", "abandoned"} and not already_recorded:
|
||
raise ValueError(f"Task {task.task_id} is already finalized as {task.status!r}")
|
||
updated = task if already_recorded else task_service.add_feedback(
|
||
task.task_id,
|
||
feedback_type=normalized,
|
||
comment=comment,
|
||
run_id=run_id,
|
||
)
|
||
session_manager = self._require_loaded(loaded, "session_manager")
|
||
session_manager.update_latest_assistant_event_payload(
|
||
session_id,
|
||
run_id,
|
||
{
|
||
"task_id": updated.task_id,
|
||
"task_status": updated.status,
|
||
"feedback_state": normalized,
|
||
},
|
||
)
|
||
if not already_recorded:
|
||
session_manager.append_message(
|
||
session_id,
|
||
run_id=run_id,
|
||
role="system",
|
||
event_type="task_feedback_recorded",
|
||
event_payload={
|
||
"task_id": task.task_id,
|
||
"feedback_type": normalized,
|
||
"comment": comment,
|
||
"task_status": updated.status,
|
||
},
|
||
content=comment,
|
||
context_visible=False,
|
||
)
|
||
|
||
generated_candidates = []
|
||
validation = ValidationResult.from_dict(updated.validation_result)
|
||
if not already_recorded:
|
||
run_memory_store = self._require_loaded(loaded, "run_memory_store")
|
||
feedback_payload = {
|
||
"feedback_type": normalized,
|
||
"comment": comment or "",
|
||
"task_status": updated.status,
|
||
}
|
||
run_memory_store.update_run_record(
|
||
run_id,
|
||
success=normalized == "satisfied",
|
||
feedback=feedback_payload,
|
||
)
|
||
run_memory_store.update_skill_effects_for_run(
|
||
run_id,
|
||
success=normalized == "satisfied",
|
||
feedback_score=self._feedback_score_for_learning(normalized, validation),
|
||
notes=(comment or normalized).strip(),
|
||
)
|
||
skill_learning_service = self._require_loaded(loaded, "skill_learning_service")
|
||
skill_learning_service.rescore_skill_versions()
|
||
if already_recorded:
|
||
generated_candidates = []
|
||
elif normalized == "satisfied" and validation is not None and validation.accepted:
|
||
generated_candidates = [
|
||
item.to_dict()
|
||
for item in skill_learning_service.build_learning_candidates_for_task(
|
||
updated.task_id,
|
||
trigger_run_id=run_id,
|
||
)
|
||
]
|
||
elif normalized == "abandon":
|
||
session_manager.append_message(
|
||
session_id,
|
||
run_id=run_id,
|
||
role="system",
|
||
event_type="task_failure_evidence_recorded",
|
||
event_payload={
|
||
"task_id": updated.task_id,
|
||
"feedback_type": normalized,
|
||
"comment": comment or "",
|
||
"task_status": updated.status,
|
||
"durable_memory_written": False,
|
||
},
|
||
content=(comment or "Task abandoned; retained as run/session failure evidence."),
|
||
context_visible=False,
|
||
)
|
||
|
||
return {
|
||
"session_id": session_id,
|
||
"run_id": run_id,
|
||
"task_id": updated.task_id,
|
||
"task_status": updated.status,
|
||
"feedback_type": normalized,
|
||
"learning_candidates": generated_candidates,
|
||
}
|
||
|
||
async def _process_with_main_agent(
|
||
self,
|
||
message: str,
|
||
*,
|
||
runner: Any,
|
||
kwargs: dict[str, Any],
|
||
) -> AgentRunResult:
|
||
loaded = self.create_loop().boot()
|
||
task_service = self._require_loaded(loaded, "task_service")
|
||
session_manager = self._require_loaded(loaded, "session_manager")
|
||
session_id = kwargs.get("session_id") or uuid4().hex
|
||
kwargs = dict(kwargs)
|
||
kwargs["session_id"] = session_id
|
||
|
||
provider_bundle = kwargs.get("provider_bundle") or self._make_provider_bundle_for_task(loaded, kwargs)
|
||
kwargs["provider_bundle"] = provider_bundle
|
||
router_provider = provider_bundle.auxiliary_provider or provider_bundle.main_provider
|
||
router_runtime = provider_bundle.auxiliary_runtime or provider_bundle.main_runtime
|
||
active_task = task_service.get_latest_open_task(session_id)
|
||
decision = await self._main_agent_router.classify(
|
||
message,
|
||
active_task=active_task,
|
||
provider=router_provider,
|
||
model=getattr(router_runtime, "model", None),
|
||
recent_messages=session_manager.get_messages_as_conversation(session_id),
|
||
intent_skill=self._load_intent_agent_skill(loaded),
|
||
thinking_enabled=kwargs.get("thinking_enabled"),
|
||
)
|
||
kwargs["intent_agent_decision"] = self._intent_decision_payload(
|
||
decision,
|
||
active_task=active_task,
|
||
)
|
||
if active_task is not None and decision.short_title and not active_task.metadata.get("short_title"):
|
||
active_task.metadata["short_title"] = decision.short_title
|
||
task_service.store.upsert_task(active_task)
|
||
if active_task is not None and decision.closes_task:
|
||
task_service.close_task(active_task.task_id, reason=decision.reason)
|
||
return await runner(message, **kwargs)
|
||
if active_task is not None and decision.abandons_task:
|
||
task_service.abandon_task(active_task.task_id, reason=decision.reason)
|
||
return await runner(message, **kwargs)
|
||
if not decision.is_task:
|
||
kwargs["include_skill_assembly"] = False
|
||
kwargs["include_tools"] = False
|
||
return await runner(message, **kwargs)
|
||
|
||
task = (
|
||
task_service.create_task(
|
||
session_id=session_id,
|
||
description=message,
|
||
metadata={
|
||
"router_reason": decision.reason,
|
||
**({"short_title": decision.short_title} if decision.short_title else {}),
|
||
},
|
||
)
|
||
if active_task is None or decision.starts_new_task
|
||
else active_task
|
||
)
|
||
if active_task is not None and decision.action == "revise_task" and task.task_id == active_task.task_id:
|
||
task = self._record_revision_feedback_for_task(
|
||
loaded,
|
||
task=task,
|
||
session_id=session_id,
|
||
comment=message,
|
||
)
|
||
return await self._run_task_mode(message, runner=runner, kwargs=kwargs, task=task)
|
||
|
||
def _record_revision_feedback_for_task(
|
||
self,
|
||
loaded: Any,
|
||
*,
|
||
task: TaskRecord,
|
||
session_id: str,
|
||
comment: str,
|
||
) -> TaskRecord:
|
||
"""Mark the latest feedback-eligible run as revised before continuing a task."""
|
||
|
||
if task.status not in {"awaiting_feedback", "needs_revision"}:
|
||
return task
|
||
run_id = next((item for item in reversed(task.run_ids) if item), None)
|
||
if not run_id:
|
||
return task
|
||
|
||
existing = next((item for item in task.feedback if item.get("run_id") == run_id), None)
|
||
if existing is not None:
|
||
if existing.get("feedback_type") != "revise":
|
||
return task
|
||
updated = task
|
||
already_recorded = True
|
||
else:
|
||
task_service = self._require_loaded(loaded, "task_service")
|
||
updated = task_service.add_feedback(
|
||
task.task_id,
|
||
feedback_type="revise",
|
||
comment=comment,
|
||
run_id=run_id,
|
||
)
|
||
already_recorded = False
|
||
|
||
session_manager = self._require_loaded(loaded, "session_manager")
|
||
session_manager.update_latest_assistant_event_payload(
|
||
session_id,
|
||
run_id,
|
||
{
|
||
"task_id": updated.task_id,
|
||
"task_status": updated.status,
|
||
"feedback_state": "revise",
|
||
},
|
||
)
|
||
if already_recorded:
|
||
return updated
|
||
|
||
session_manager.append_message(
|
||
session_id,
|
||
run_id=run_id,
|
||
role="system",
|
||
event_type="task_feedback_recorded",
|
||
event_payload={
|
||
"task_id": updated.task_id,
|
||
"feedback_type": "revise",
|
||
"comment": comment,
|
||
"task_status": updated.status,
|
||
"auto_recorded": True,
|
||
},
|
||
content=comment,
|
||
context_visible=False,
|
||
)
|
||
validation = ValidationResult.from_dict(updated.validation_result)
|
||
run_memory_store = self._require_loaded(loaded, "run_memory_store")
|
||
run_memory_store.update_run_record(
|
||
run_id,
|
||
success=False,
|
||
feedback={
|
||
"feedback_type": "revise",
|
||
"comment": comment,
|
||
"task_status": updated.status,
|
||
},
|
||
)
|
||
run_memory_store.update_skill_effects_for_run(
|
||
run_id,
|
||
success=False,
|
||
feedback_score=self._feedback_score_for_learning("revise", validation),
|
||
notes=comment.strip() or "revise",
|
||
)
|
||
skill_learning_service = self._require_loaded(loaded, "skill_learning_service")
|
||
skill_learning_service.rescore_skill_versions()
|
||
return updated
|
||
|
||
async def _run_task_mode(
|
||
self,
|
||
message: str,
|
||
*,
|
||
runner: Any,
|
||
kwargs: dict[str, Any],
|
||
task: TaskRecord,
|
||
) -> AgentRunResult:
|
||
loaded = self.create_loop().boot()
|
||
task_service = self._require_loaded(loaded, "task_service")
|
||
validation_service = self._require_loaded(loaded, "validation_service")
|
||
task_execution_planner = self._require_loaded(loaded, "task_execution_planner")
|
||
session_manager = self._require_loaded(loaded, "session_manager")
|
||
run_memory_store = self._require_loaded(loaded, "run_memory_store")
|
||
|
||
last_result: AgentRunResult | None = None
|
||
latest_validation: ValidationResult | None = None
|
||
base_execution_context = kwargs.get("execution_context")
|
||
provider_bundle = kwargs.get("provider_bundle") or self._make_provider_bundle_for_task(loaded, kwargs)
|
||
kwargs = dict(kwargs)
|
||
team_provider_bundle_factory = kwargs.pop("team_provider_bundle_factory", None)
|
||
kwargs["provider_bundle"] = provider_bundle
|
||
|
||
for attempt_index in (1, 2):
|
||
task_service.start_run(task.task_id, user_message=message, attempt_index=attempt_index)
|
||
plan = await task_execution_planner.plan(
|
||
task=task,
|
||
user_message=message,
|
||
attempt_index=attempt_index,
|
||
latest_validation=latest_validation,
|
||
provider_bundle=provider_bundle,
|
||
)
|
||
self._append_task_observation(
|
||
session_manager,
|
||
task.session_id,
|
||
event_type="task_execution_planned",
|
||
payload={
|
||
"task_id": task.task_id,
|
||
"attempt_index": attempt_index,
|
||
**plan.to_event_payload(),
|
||
},
|
||
)
|
||
team_summaries: list[str] = []
|
||
team_execution_context = ""
|
||
if plan.is_team:
|
||
team_result, team_error = await self._run_team_for_task(
|
||
plan,
|
||
task=task,
|
||
parent_session_id=kwargs["session_id"],
|
||
provider_bundle_factory=team_provider_bundle_factory
|
||
or self._build_team_provider_bundle_factory(loaded, kwargs),
|
||
)
|
||
if team_result is not None:
|
||
team_summaries = [self._team_summary_for_validation(team_result)]
|
||
team_execution_context = self._team_execution_context(plan, team_result)
|
||
self._append_task_observation(
|
||
session_manager,
|
||
task.session_id,
|
||
event_type="task_team_run_completed" if team_result.success else "task_team_run_failed",
|
||
payload={
|
||
"task_id": task.task_id,
|
||
"attempt_index": attempt_index,
|
||
"plan_mode": plan.mode,
|
||
"strategy": plan.graph.strategy if plan.graph else None,
|
||
"node_ids": [node.node_id for node in plan.graph.nodes] if plan.graph else [],
|
||
"team_run_ids": team_result.run_ids,
|
||
"team_success": team_result.success,
|
||
"node_results": self._team_node_results_for_event(plan, team_result),
|
||
"reason": plan.reason,
|
||
"error": None if team_result.success else "one or more team nodes failed",
|
||
},
|
||
)
|
||
else:
|
||
team_summaries = [f"Team execution failed: {team_error}"]
|
||
team_execution_context = self._failed_team_execution_context(plan, team_error or "unknown error")
|
||
self._append_task_observation(
|
||
session_manager,
|
||
task.session_id,
|
||
event_type="task_team_run_failed",
|
||
payload={
|
||
"task_id": task.task_id,
|
||
"attempt_index": attempt_index,
|
||
"plan_mode": plan.mode,
|
||
"strategy": plan.graph.strategy if plan.graph else None,
|
||
"node_ids": [node.node_id for node in plan.graph.nodes] if plan.graph else [],
|
||
"team_run_ids": [],
|
||
"team_success": False,
|
||
"reason": plan.reason,
|
||
"error": team_error,
|
||
},
|
||
)
|
||
|
||
attempt_kwargs = dict(kwargs)
|
||
attempt_kwargs.update(
|
||
{
|
||
"task_id": task.task_id,
|
||
"task_mode": True,
|
||
"attempt_index": attempt_index,
|
||
"allow_candidate_generation": False,
|
||
}
|
||
)
|
||
if attempt_index == 2 and latest_validation is not None:
|
||
revision_context = latest_validation.recommended_revision_prompt.strip()
|
||
if revision_context:
|
||
attempt_kwargs["execution_context"] = self._join_context(
|
||
base_execution_context,
|
||
f"Task validation revision request:\n{revision_context}",
|
||
team_execution_context,
|
||
)
|
||
elif team_execution_context:
|
||
attempt_kwargs["execution_context"] = self._join_context(base_execution_context, team_execution_context)
|
||
attempt_kwargs["skill_selection_context"] = self._build_skill_selection_context(
|
||
task=task,
|
||
user_message=message,
|
||
attempt_index=attempt_index,
|
||
latest_validation=latest_validation,
|
||
plan=plan,
|
||
team_summaries=team_summaries,
|
||
)
|
||
|
||
result = await runner(message, **attempt_kwargs)
|
||
last_result = result
|
||
self._append_task_observation(
|
||
session_manager,
|
||
task.session_id,
|
||
event_type="task_synthesis_completed",
|
||
payload={
|
||
"task_id": task.task_id,
|
||
"attempt_index": attempt_index,
|
||
"main_run_id": result.run_id,
|
||
"plan_mode": plan.mode,
|
||
"strategy": plan.graph.strategy if plan.graph else None,
|
||
},
|
||
)
|
||
task = task_service.append_run(
|
||
task.task_id,
|
||
result.run_id,
|
||
skill_names=self._skill_names_for_run(loaded, result.run_id),
|
||
)
|
||
validation = await validation_service.validate_task_result(
|
||
task=task,
|
||
user_message=message,
|
||
final_output=result.output_text,
|
||
transcript_excerpt=self._run_excerpt(session_manager, result.session_id, result.run_id),
|
||
tool_summaries=self._tool_summaries(session_manager, result.session_id, result.run_id),
|
||
team_summaries=team_summaries,
|
||
provider_bundle=provider_bundle,
|
||
)
|
||
latest_validation = validation
|
||
task = task_service.record_validation(task.task_id, result.run_id, validation)
|
||
run_memory_store.update_run_record(result.run_id, validation_result=validation.to_dict())
|
||
session_manager.update_latest_assistant_event_payload(
|
||
result.session_id,
|
||
result.run_id,
|
||
{
|
||
"task_id": task.task_id,
|
||
"task_status": task.status,
|
||
"validation_status": "passed" if validation.accepted else "failed",
|
||
},
|
||
)
|
||
session_manager.append_message(
|
||
result.session_id,
|
||
run_id=result.run_id,
|
||
role="system",
|
||
event_type="task_validation_snapshotted",
|
||
event_payload={
|
||
"task_id": task.task_id,
|
||
"attempt_index": attempt_index,
|
||
"validation_result": validation.to_dict(),
|
||
"retry_scheduled": not validation.accepted and attempt_index == 1,
|
||
},
|
||
content=validation.recommended_revision_prompt or None,
|
||
context_visible=False,
|
||
)
|
||
if not validation.accepted and attempt_index == 1:
|
||
session_manager.set_run_context_visible(result.session_id, result.run_id, False)
|
||
result.task_id = task.task_id
|
||
result.task_status = task.status
|
||
result.validation_result = validation.to_dict()
|
||
if validation.accepted or attempt_index == 2:
|
||
return result
|
||
|
||
if last_result is None: # pragma: no cover - defensive
|
||
raise RuntimeError("Task mode did not produce a run result")
|
||
return last_result
|
||
|
||
async def _run_team_for_task(
|
||
self,
|
||
plan: TaskExecutionPlan,
|
||
*,
|
||
task: TaskRecord,
|
||
parent_session_id: str,
|
||
provider_bundle_factory: Any,
|
||
) -> tuple[TeamRunResult | None, str | None]:
|
||
if plan.graph is None:
|
||
return None, "team plan did not include an execution graph"
|
||
try:
|
||
from beaver.services.team_service import TeamService
|
||
|
||
result = await TeamService(self.create_loop()).run_team(
|
||
plan.graph,
|
||
parent_task_id=task.task_id,
|
||
parent_session_id=parent_session_id,
|
||
parent_run_id=None,
|
||
provider_bundle_factory=provider_bundle_factory,
|
||
allow_candidate_generation=False,
|
||
)
|
||
return result, None
|
||
except Exception as exc:
|
||
return None, str(exc)
|
||
|
||
@staticmethod
|
||
def _require_loaded(loaded: Any, field_name: str) -> Any:
|
||
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 _load_intent_agent_skill(loaded: Any) -> str | None:
|
||
skills_loader = getattr(loaded, "skills_loader", None)
|
||
if skills_loader is None:
|
||
return None
|
||
return skills_loader.load_skill("intent-agent-router")
|
||
|
||
@staticmethod
|
||
def _intent_decision_payload(decision: Any, *, active_task: TaskRecord | None) -> dict[str, Any]:
|
||
action = decision.action or ("create_task" if decision.is_task and active_task is None else decision.mode)
|
||
return {
|
||
"agent": "intent_agent",
|
||
"choice": action,
|
||
"mode": "task" if decision.is_task else "simple",
|
||
"reason": decision.reason,
|
||
"active_task_id": active_task.task_id if active_task is not None else None,
|
||
"starts_new_task": bool(decision.starts_new_task or (decision.is_task and active_task is None)),
|
||
"closes_task": bool(decision.closes_task),
|
||
"abandons_task": bool(decision.abandons_task),
|
||
"short_title": decision.short_title,
|
||
}
|
||
|
||
@staticmethod
|
||
def _skill_names_for_run(loaded: Any, run_id: str) -> list[str]:
|
||
store = getattr(loaded, "run_memory_store", None)
|
||
if store is None:
|
||
return []
|
||
for record in store.list_runs():
|
||
if record.run_id == run_id:
|
||
return [receipt.skill_name for receipt in record.activated_skills]
|
||
return []
|
||
|
||
@staticmethod
|
||
def _feedback_score_for_learning(feedback_type: str, validation: ValidationResult | None) -> float:
|
||
if feedback_type == "satisfied":
|
||
if validation is not None:
|
||
return max(0.0, min(1.0, float(validation.score)))
|
||
return 1.0
|
||
if feedback_type == "revise":
|
||
return 0.5
|
||
return 0.0
|
||
|
||
@staticmethod
|
||
def _build_skill_selection_context(
|
||
*,
|
||
task: TaskRecord,
|
||
user_message: str,
|
||
attempt_index: int,
|
||
latest_validation: ValidationResult | None = None,
|
||
plan: TaskExecutionPlan | None = None,
|
||
team_summaries: list[str] | None = None,
|
||
) -> str:
|
||
phase = f"attempt_{attempt_index}"
|
||
if latest_validation is not None:
|
||
phase = f"revision_attempt_{attempt_index}"
|
||
elif plan is not None and plan.is_team:
|
||
phase = f"team_synthesis_attempt_{attempt_index}"
|
||
|
||
sections = [
|
||
f"Task goal:\n{task.goal or task.description}",
|
||
f"Task description:\n{task.description}",
|
||
f"Current user request:\n{user_message}",
|
||
f"Execution phase:\n{phase}",
|
||
f"Task status:\n{task.status}",
|
||
]
|
||
if task.constraints:
|
||
sections.append("Known constraints:\n" + "\n".join(f"- {item}" for item in task.constraints))
|
||
if task.skill_names:
|
||
sections.append(
|
||
"Previously activated skills (reuse bias, not pinned):\n"
|
||
+ "\n".join(f"- {item}" for item in task.skill_names)
|
||
)
|
||
else:
|
||
sections.append("Previously activated skills:\nNone")
|
||
if latest_validation is not None:
|
||
validation_lines = [
|
||
f"accepted: {latest_validation.accepted}",
|
||
f"score: {latest_validation.score}",
|
||
]
|
||
if latest_validation.issues:
|
||
validation_lines.append("issues:\n" + "\n".join(f"- {item}" for item in latest_validation.issues))
|
||
if latest_validation.missing_requirements:
|
||
validation_lines.append(
|
||
"missing requirements:\n"
|
||
+ "\n".join(f"- {item}" for item in latest_validation.missing_requirements)
|
||
)
|
||
if latest_validation.recommended_revision_prompt:
|
||
validation_lines.append(
|
||
"recommended revision:\n"
|
||
+ latest_validation.recommended_revision_prompt
|
||
)
|
||
sections.append("Validation feedback:\n" + "\n".join(validation_lines))
|
||
if plan is not None:
|
||
plan_lines = [
|
||
f"mode: {plan.mode}",
|
||
f"reason: {plan.reason}",
|
||
]
|
||
if plan.final_synthesis_instruction:
|
||
plan_lines.append(f"final synthesis instruction: {plan.final_synthesis_instruction}")
|
||
if plan.graph is not None:
|
||
plan_lines.append(f"strategy: {plan.graph.strategy}")
|
||
plan_lines.append(
|
||
"nodes:\n"
|
||
+ "\n".join(
|
||
f"- {node.node_id}: {node.task}"
|
||
for node in plan.graph.nodes
|
||
)
|
||
)
|
||
sections.append("Execution plan:\n" + "\n".join(plan_lines))
|
||
if team_summaries:
|
||
sections.append("Team execution summaries:\n" + "\n\n".join(team_summaries)[:2400])
|
||
sections.append(
|
||
"Skill selection instruction:\n"
|
||
"Prefer reusing previously activated skills when they still match the Task. "
|
||
"Select new skills only if the current request, revision, or execution plan needs a different capability. "
|
||
"If no published skill matches, return [] and let the run continue without skills."
|
||
)
|
||
return "\n\n".join(section for section in sections if section.strip())
|
||
|
||
@staticmethod
|
||
def _run_excerpt(session_manager: Any, session_id: str, run_id: str) -> str:
|
||
lines = []
|
||
for event in session_manager.get_run_event_records(session_id, run_id):
|
||
if event.context_visible and event.content:
|
||
lines.append(f"{event.role}: {event.content.strip()}")
|
||
return "\n".join(lines[:12])[:2400]
|
||
|
||
@staticmethod
|
||
def _tool_summaries(session_manager: Any, session_id: str, run_id: str) -> list[str]:
|
||
summaries = []
|
||
for event in session_manager.get_run_event_records(session_id, run_id):
|
||
if event.event_type != "tool_result_recorded":
|
||
continue
|
||
text = (event.content or "").strip()
|
||
if text:
|
||
summaries.append(f"{event.tool_name or 'tool'}: {text[:500]}")
|
||
return summaries[:12]
|
||
|
||
@staticmethod
|
||
def _append_task_observation(
|
||
session_manager: Any,
|
||
session_id: str,
|
||
*,
|
||
event_type: str,
|
||
payload: dict[str, Any],
|
||
) -> None:
|
||
session_manager.append_message(
|
||
session_id,
|
||
role="system",
|
||
event_type=event_type,
|
||
event_payload=payload,
|
||
content=payload.get("reason") or payload.get("error"),
|
||
context_visible=False,
|
||
)
|
||
|
||
@staticmethod
|
||
def _join_context(*parts: str | None) -> str:
|
||
return "\n\n".join(part.strip() for part in parts if part and part.strip())
|
||
|
||
@staticmethod
|
||
def _team_summary_for_validation(result: TeamRunResult) -> str:
|
||
lines = [
|
||
f"success={result.success}",
|
||
f"task_id={result.task_id or ''}",
|
||
"summary:",
|
||
result.summary,
|
||
"nodes:",
|
||
]
|
||
for node in result.node_results:
|
||
lines.append(
|
||
f"- {node.node_id}: success={node.success} finish_reason={node.finish_reason} "
|
||
f"error={node.error or ''} output={node.output_text[:500]}"
|
||
)
|
||
return "\n".join(lines)
|
||
|
||
@staticmethod
|
||
def _team_node_results_for_event(plan: TaskExecutionPlan, result: TeamRunResult) -> list[dict[str, Any]]:
|
||
nodes = {node.node_id: node for node in plan.graph.nodes} if plan.graph else {}
|
||
payloads: list[dict[str, Any]] = []
|
||
for item in result.node_results:
|
||
payload = item.to_dict()
|
||
node = nodes.get(item.node_id)
|
||
if node is not None:
|
||
payload["selected_skill_names"] = list(node.inherited_pinned_skills)
|
||
payload["ephemeral_skill_names"] = [
|
||
skill.name for skill in node.inherited_pinned_skill_contexts
|
||
]
|
||
payload["skill_query"] = node.agent.metadata.get("skill_query")
|
||
payload["ephemeral_guidance_id"] = node.agent.metadata.get("ephemeral_guidance_id")
|
||
payload["ephemeral_guidance_name"] = node.agent.metadata.get("ephemeral_guidance_name")
|
||
payload["ephemeral_used"] = bool(node.inherited_pinned_skill_contexts)
|
||
payloads.append(payload)
|
||
return payloads
|
||
|
||
@staticmethod
|
||
def _team_execution_context(plan: TaskExecutionPlan, result: TeamRunResult) -> str:
|
||
node_lines = [
|
||
(
|
||
f"- {node.node_id}: success={node.success}, finish_reason={node.finish_reason}, "
|
||
f"run_id={node.run_id or ''}, error={node.error or ''}\n{node.output_text}"
|
||
)
|
||
for node in result.node_results
|
||
]
|
||
return "\n\n".join(
|
||
item
|
||
for item in [
|
||
"Task team execution result:",
|
||
f"Planner reason: {plan.reason}",
|
||
f"Strategy: {plan.graph.strategy if plan.graph else ''}",
|
||
f"Team success: {result.success}",
|
||
f"Team summary:\n{result.summary}",
|
||
"Node results:\n" + "\n\n".join(node_lines),
|
||
(
|
||
"Final synthesis instruction:\n" + plan.final_synthesis_instruction
|
||
if plan.final_synthesis_instruction
|
||
else None
|
||
),
|
||
(
|
||
"Use successful team outputs as internal evidence. If one or more nodes failed, "
|
||
"do not blindly repeat failed tool calls. Produce a user-visible fallback answer "
|
||
"with available evidence and clearly state any missing or uncertain data."
|
||
),
|
||
]
|
||
if item
|
||
)
|
||
|
||
@staticmethod
|
||
def _failed_team_execution_context(plan: TaskExecutionPlan, error: str) -> str:
|
||
return "\n\n".join(
|
||
[
|
||
"Task team execution failed before final synthesis.",
|
||
f"Planner reason: {plan.reason}",
|
||
f"Strategy: {plan.graph.strategy if plan.graph else ''}",
|
||
f"Error: {error}",
|
||
(
|
||
"Proceed as the main agent. Do not blindly repeat failed tool calls; "
|
||
"produce a user-visible fallback answer with available evidence and clearly "
|
||
"state any missing or uncertain data."
|
||
),
|
||
]
|
||
)
|
||
|
||
def _build_team_provider_bundle_factory(self, loaded: Any, kwargs: dict[str, Any]) -> Any:
|
||
def factory(node: ExecutionNode) -> Any:
|
||
node_kwargs = dict(kwargs)
|
||
node_kwargs.pop("provider_bundle", None)
|
||
if node.agent.model:
|
||
node_kwargs["model"] = node.agent.model
|
||
if node.agent.provider_name:
|
||
node_kwargs["provider_name"] = node.agent.provider_name
|
||
return self._make_provider_bundle_for_task(loaded, node_kwargs)
|
||
|
||
return factory
|
||
|
||
def _make_provider_bundle_for_task(self, loaded: Any, kwargs: dict[str, Any]) -> Any:
|
||
config = loaded.config
|
||
configured_provider = config.resolve_provider_target(
|
||
model=kwargs.get("model"),
|
||
provider_name=kwargs.get("provider_name"),
|
||
)
|
||
resolved_model = configured_provider.get("model") or self.profile.default_model
|
||
resolved_provider_name = configured_provider.get("provider_name") or kwargs.get("provider_name")
|
||
return make_provider_bundle(
|
||
model=resolved_model,
|
||
provider_name=resolved_provider_name,
|
||
api_key=kwargs.get("api_key") or configured_provider.get("api_key"),
|
||
api_base=kwargs.get("api_base") or configured_provider.get("api_base"),
|
||
request_timeout_seconds=configured_provider.get("request_timeout_seconds"),
|
||
extra_headers=kwargs.get("extra_headers") or configured_provider.get("extra_headers"),
|
||
routing=kwargs.get("routing"),
|
||
fallback_target=kwargs.get("fallback_target"),
|
||
auxiliary_target=kwargs.get("auxiliary_target"),
|
||
embedding_target=kwargs.get("embedding_target") or config.resolve_embedding_target(),
|
||
embedding_model=kwargs.get("embedding_model") or config.default_embedding_model,
|
||
)
|
||
|
||
async def handle_inbound_message(self, inbound: InboundMessage) -> OutboundMessage:
|
||
"""把 bus inbound 映射成标准 runtime 调用，并返回结构化 outbound。"""
|
||
|
||
try:
|
||
result = await self.submit_direct(
|
||
inbound.content,
|
||
session_id=inbound.session_id,
|
||
source=f"gateway:{inbound.channel}",
|
||
user_id=inbound.user_id,
|
||
title=inbound.title,
|
||
execution_context=inbound.execution_context,
|
||
model=inbound.model,
|
||
provider_name=inbound.provider_name,
|
||
embedding_model=inbound.embedding_model,
|
||
)
|
||
except Exception as exc:
|
||
return self.build_outbound_error(
|
||
inbound,
|
||
detail=str(exc),
|
||
finish_reason=self._classify_inbound_failure(exc),
|
||
)
|
||
return self.build_outbound_message(inbound, result)
|
||
|
||
@staticmethod
|
||
def _classify_inbound_failure(exc: Exception) -> str:
|
||
"""把 runtime 异常收口为更稳定的 bus finish reason。"""
|
||
|
||
if isinstance(exc, RuntimeError):
|
||
detail = str(exc)
|
||
if (
|
||
"requires an active run() loop" in detail
|
||
or "not accepting new tasks after stop()" in detail
|
||
):
|
||
return "stopped"
|
||
return "error"
|
||
|
||
@staticmethod
|
||
def build_outbound_message(inbound: InboundMessage, result: AgentRunResult) -> OutboundMessage:
|
||
"""把一次 runtime 正常结果转成 bus outbound。"""
|
||
|
||
return OutboundMessage(
|
||
message_id=inbound.message_id,
|
||
channel=inbound.channel,
|
||
session_id=result.session_id,
|
||
run_id=result.run_id,
|
||
content=result.output_text,
|
||
finish_reason=result.finish_reason,
|
||
provider_name=result.provider_name,
|
||
model=result.model,
|
||
usage=dict(result.usage),
|
||
metadata={
|
||
"inbound_metadata": dict(inbound.metadata),
|
||
"task_id": getattr(result, "task_id", None),
|
||
"task_status": getattr(result, "task_status", None),
|
||
"validation_result": getattr(result, "validation_result", None),
|
||
},
|
||
)
|
||
|
||
@staticmethod
|
||
def build_outbound_error(
|
||
inbound: InboundMessage,
|
||
*,
|
||
detail: str,
|
||
finish_reason: str = "error",
|
||
) -> OutboundMessage:
|
||
"""把 inbound 处理失败转换成结构化 outbound 错误消息。"""
|
||
|
||
return OutboundMessage(
|
||
message_id=inbound.message_id,
|
||
channel=inbound.channel,
|
||
session_id=inbound.session_id,
|
||
content=detail,
|
||
finish_reason=finish_reason,
|
||
metadata={"error": detail, "inbound_metadata": dict(inbound.metadata)},
|
||
)
|
||
|
||
def run_direct(
|
||
self,
|
||
message: str,
|
||
**kwargs: Any,
|
||
) -> AgentRunResult:
|
||
"""同步 direct run 包装。
|
||
|
||
主要给当前 CLI 或简单脚本使用。真正的长期方向仍然是让 interfaces
|
||
在 direct mode 下直接走 `await process_direct(...)`。
|
||
"""
|
||
|
||
try:
|
||
asyncio.get_running_loop()
|
||
except RuntimeError:
|
||
pass
|
||
else:
|
||
raise RuntimeError(
|
||
"AgentService.run_direct() cannot be used inside an active event loop; "
|
||
"use 'await AgentService.process_direct(...)' instead."
|
||
)
|
||
return asyncio.run(self.process_direct(message, **kwargs))
|