feat(beaver): 完成Task Team功能v1实现，重构后端架构支持统一内核
新增内部Task系统，包括验证、反馈门控机制，实现自动质量验证 (通过率>=0.75)和用户反馈闭环(satisfied/revise/abandon)。 实现Agent Team v1协调器，支持sequence/parallel/dag执行策略， sub-agent复用主AgentLoop，每个run使用独立memory snapshot。 建立Skill学习pipeline，包含draft/审核/发布/回滚完整生命周期， 通过Task验证通过且用户满意才生成学习候选。 重构目录结构，移除third_party依赖，建立统一engine内核， 所有agent共享运行时基础组件。 更新ContextBuilder清理provider消息字段，增强SkillContext版本管理， 集成TaskExecutionPlanner和TaskSkillResolver实现技能解析机制。
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288
app-instance/backend/beaver/tasks/planner.py
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288
app-instance/backend/beaver/tasks/planner.py
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"""Internal Task execution planner for single-agent vs team execution."""
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from __future__ import annotations
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import json
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from dataclasses import dataclass, field
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from typing import Any, Literal
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from beaver.coordinator.models import AgentDescriptor, ExecutionGraph, ExecutionNode
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from beaver.engine.providers import ProviderBundle
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from .models import TaskRecord, ValidationResult
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from .skill_resolver import SkillResolutionReport, TaskSkillResolver
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TaskExecutionMode = Literal["single", "team"]
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@dataclass(slots=True)
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class TaskExecutionPlan:
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mode: TaskExecutionMode
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reason: str = ""
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graph: ExecutionGraph | None = None
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final_synthesis_instruction: str = ""
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fallback_error: str | None = None
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skill_resolution_report: list[SkillResolutionReport] = field(default_factory=list)
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@property
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def is_team(self) -> bool:
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return self.mode == "team" and self.graph is not None
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@classmethod
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def single(cls, reason: str, *, fallback_error: str | None = None) -> "TaskExecutionPlan":
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return cls(mode="single", reason=reason, fallback_error=fallback_error)
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def to_event_payload(self) -> dict[str, Any]:
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strategy = self.graph.strategy if self.graph is not None else None
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nodes = self.graph.nodes if self.graph is not None else []
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return {
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"plan_mode": self.mode,
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"reason": self.reason,
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"strategy": strategy,
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"node_ids": [node.node_id for node in nodes],
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"skill_queries": [
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str(node.agent.metadata.get("skill_query") or "")
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for node in nodes
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],
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"selected_skill_names": [
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name
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for node in nodes
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for name in node.inherited_pinned_skills
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],
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"generated_skill_draft_ids": [
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item.generated_skill_draft_id
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for item in self.skill_resolution_report
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if item.generated_skill_draft_id
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],
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"skill_resolution_report": [item.to_dict() for item in self.skill_resolution_report],
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"fallback_error": self.fallback_error,
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}
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class TaskExecutionPlanner:
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"""Plan whether a Task attempt should run through a team first."""
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_MAX_NODES = 6
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_SUPPORTED_STRATEGIES = {"sequence", "parallel", "dag"}
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def __init__(self, *, task_skill_resolver: TaskSkillResolver | None = None) -> None:
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self.task_skill_resolver = task_skill_resolver
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async def plan(
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self,
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*,
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task: TaskRecord,
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user_message: str,
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attempt_index: int,
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latest_validation: ValidationResult | None = None,
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provider_bundle: ProviderBundle | None = None,
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) -> TaskExecutionPlan:
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provider = None
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model = None
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if provider_bundle is not None:
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provider = provider_bundle.auxiliary_provider or provider_bundle.main_provider
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runtime = provider_bundle.auxiliary_runtime or provider_bundle.main_runtime
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model = getattr(runtime, "model", None)
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if provider is None:
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return TaskExecutionPlan.single("planner_provider_unavailable")
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try:
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response = await provider.chat(
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messages=[
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{
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"role": "system",
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"content": (
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"You choose whether an internal Beaver Task attempt should run as a single "
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"main-agent pass or use a small sub-agent team first. Return only compact JSON."
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),
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},
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{
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"role": "user",
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"content": self._prompt(
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task=task,
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user_message=user_message,
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attempt_index=attempt_index,
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latest_validation=latest_validation,
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),
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},
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],
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tools=None,
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model=model,
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max_tokens=1200,
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temperature=0.0,
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)
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plan = self.from_json(response.content or "")
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return await self._resolve_plan(
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plan,
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task=task,
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user_message=user_message,
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attempt_index=attempt_index,
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provider_bundle=provider_bundle,
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)
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except Exception as exc:
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return TaskExecutionPlan.single("planner_failed", fallback_error=str(exc))
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async def _resolve_plan(
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self,
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plan: TaskExecutionPlan,
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*,
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task: TaskRecord,
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user_message: str,
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attempt_index: int,
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provider_bundle: ProviderBundle | None,
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) -> TaskExecutionPlan:
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if not plan.is_team or self.task_skill_resolver is None:
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return plan
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if provider_bundle is None:
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return TaskExecutionPlan.single("planner_fallback_single", fallback_error="task_skill_resolver_provider_unavailable")
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try:
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assert plan.graph is not None
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graph, reports = await self.task_skill_resolver.resolve_graph(
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plan.graph,
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task=task,
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user_message=user_message,
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attempt_index=attempt_index,
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provider_bundle=provider_bundle,
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)
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graph.validate()
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plan.graph = graph
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plan.skill_resolution_report = reports
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return plan
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except Exception as exc:
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return TaskExecutionPlan.single("planner_fallback_single", fallback_error=f"task_skill_resolver_failed: {exc}")
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def from_json(self, text: str) -> TaskExecutionPlan:
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try:
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payload = self._parse_json_object(text)
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mode = str(payload.get("mode") or "single").strip().lower()
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reason = str(payload.get("reason") or "")
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if mode != "team":
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return TaskExecutionPlan.single(reason or "planner_selected_single")
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graph = self._graph_from_payload(payload)
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graph.validate()
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return TaskExecutionPlan(
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mode="team",
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reason=reason or "planner_selected_team",
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graph=graph,
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final_synthesis_instruction=str(payload.get("final_synthesis_instruction") or ""),
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)
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except Exception as exc:
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return TaskExecutionPlan.single("planner_fallback_single", fallback_error=str(exc))
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def _graph_from_payload(self, payload: dict[str, Any]) -> ExecutionGraph:
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strategy = str(payload.get("strategy") or "sequence").strip().lower()
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if strategy not in self._SUPPORTED_STRATEGIES:
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raise ValueError(f"Unsupported team strategy: {strategy}")
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raw_nodes = payload.get("nodes")
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if not isinstance(raw_nodes, list) or not raw_nodes:
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raise ValueError("Team plan requires at least one node")
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if len(raw_nodes) > self._MAX_NODES:
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raise ValueError(f"Team plan exceeds max node count {self._MAX_NODES}")
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nodes: list[ExecutionNode] = []
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for index, item in enumerate(raw_nodes, start=1):
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if not isinstance(item, dict):
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raise ValueError("Each team node must be an object")
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agent_payload = item.get("agent") if isinstance(item.get("agent"), dict) else {}
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skill_query = str(item.get("skill_query") or agent_payload.get("skill_query") or item.get("task") or "").strip()
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requested_capabilities = _string_list(
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item.get("required_capabilities") or item.get("capabilities") or agent_payload.get("capabilities")
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)
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requested_tags = _string_list(item.get("tags") or agent_payload.get("tags"))
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node_id = str(item.get("node_id") or item.get("id") or agent_payload.get("name") or f"node_{index}").strip()
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task = str(item.get("task") or "").strip()
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if not node_id or not task:
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raise ValueError("Each team node requires node_id/id and task")
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nodes.append(
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ExecutionNode(
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node_id=node_id,
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task=task,
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agent=AgentDescriptor(
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name=node_id,
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role="",
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system_prompt="",
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metadata={
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"skill_query": skill_query,
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"required_capabilities": requested_capabilities,
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"requested_tags": requested_tags,
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"sub_agent_kind": "generic_skill_worker",
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},
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),
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depends_on=[str(dep) for dep in item.get("depends_on") or []],
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inherited_pinned_skills=[str(name) for name in item.get("pinned_skills") or []],
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constraints=[str(value) for value in item.get("constraints") or []],
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expected_output=str(item.get("expected_output") or "") or None,
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)
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)
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return ExecutionGraph(strategy=strategy, nodes=nodes) # type: ignore[arg-type]
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@staticmethod
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def _prompt(
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*,
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task: TaskRecord,
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user_message: str,
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attempt_index: int,
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latest_validation: ValidationResult | None,
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) -> str:
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validation_note = ""
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if latest_validation is not None:
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validation_note = (
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"\nPrevious validation issues:\n"
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+ json.dumps(latest_validation.to_dict(), ensure_ascii=False)
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)
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return (
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"Decide execution mode for this internal Task attempt.\n"
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"Use mode=team only when independent research, review, implementation slices, or staged checks "
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"would materially improve the result. Otherwise use mode=single.\n\n"
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"JSON schema:\n"
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"{\n"
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' "mode": "single" | "team",\n'
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' "reason": "short reason",\n'
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' "strategy": "sequence" | "parallel" | "dag",\n'
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' "nodes": [{"node_id": "api_review", "task": "...", "skill_query": "API contract review", '
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'"required_capabilities": ["schema compatibility"], "depends_on": []}],\n'
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' "final_synthesis_instruction": "how the main agent should synthesize team output"\n'
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"}\n\n"
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f"Task goal:\n{task.goal}\n\n"
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f"Current user request:\n{user_message}\n\n"
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f"Attempt index: {attempt_index}\n"
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f"{validation_note}"
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)
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@staticmethod
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def _parse_json_object(text: str) -> dict[str, Any]:
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cleaned = text.strip()
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if cleaned.startswith("```"):
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cleaned = cleaned.strip("`")
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if cleaned.lower().startswith("json"):
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cleaned = cleaned[4:].strip()
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start = cleaned.find("{")
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end = cleaned.rfind("}")
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if start >= 0 and end >= start:
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cleaned = cleaned[start : end + 1]
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payload = json.loads(cleaned)
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if not isinstance(payload, dict):
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raise ValueError("planner response must be a JSON object")
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return payload
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def _optional_str(value: Any) -> str | None:
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if value in (None, ""):
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return None
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text = str(value).strip()
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return text or None
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def _string_list(value: Any) -> list[str]:
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if not isinstance(value, list):
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if isinstance(value, str):
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value = [item.strip() for item in value.split(",")]
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else:
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return []
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result: list[str] = []
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for item in value:
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text = str(item).strip()
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if text and text not in result:
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result.append(text)
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return result
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