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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175
app-instance/backend/beaver/skills/learning/worker.py
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175
app-instance/backend/beaver/skills/learning/worker.py
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"""Background worker for assisted skill learning."""
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from __future__ import annotations
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import asyncio
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import os
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from dataclasses import dataclass, field
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from typing import Callable
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from beaver.engine.providers import ProviderBundle
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from beaver.memory.skills import SkillLearningCandidate
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from beaver.skills.learning.pipeline import SkillLearningPipelineService
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@dataclass(slots=True)
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class SkillLearningWorkerConfig:
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enabled: bool = True
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max_drafts_per_run: int = 5
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max_retries: int = 3
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interval_seconds: float = 300.0
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@classmethod
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def from_env(cls) -> "SkillLearningWorkerConfig":
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return cls(
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enabled=_env_bool("BEAVER_SKILL_LEARNING_WORKER_ENABLED", True),
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max_drafts_per_run=_env_int("BEAVER_SKILL_LEARNING_MAX_DRAFTS_PER_RUN", 5),
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max_retries=_env_int("BEAVER_SKILL_LEARNING_MAX_RETRIES", 3),
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interval_seconds=float(os.getenv("BEAVER_SKILL_LEARNING_INTERVAL_SECONDS", "300") or "300"),
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)
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@dataclass(slots=True)
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class SkillLearningWorkerResult:
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processed: int = 0
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succeeded: int = 0
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failed: int = 0
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skipped: int = 0
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failures: list[dict[str, str]] = field(default_factory=list)
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def to_dict(self) -> dict:
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return {
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"processed": self.processed,
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"succeeded": self.succeeded,
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"failed": self.failed,
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"skipped": self.skipped,
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"failures": [dict(item) for item in self.failures],
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}
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class SkillLearningWorker:
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"""Synthesizes drafts for open candidates; never approves or publishes."""
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_ACTIVE_DRAFT_STATUSES = {"queued", "synthesizing", "draft_ready", "review_pending", "approved"}
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def __init__(
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self,
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*,
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pipeline: SkillLearningPipelineService,
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provider_bundle_factory: Callable[[], ProviderBundle],
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config: SkillLearningWorkerConfig | None = None,
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) -> None:
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self.pipeline = pipeline
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self.provider_bundle_factory = provider_bundle_factory
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self.config = config or SkillLearningWorkerConfig.from_env()
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self._running = False
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self._lock = asyncio.Lock()
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async def run_forever(self) -> None:
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if not self.config.enabled:
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return
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self._running = True
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try:
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while self._running:
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await self.run_once()
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await asyncio.sleep(self.config.interval_seconds)
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finally:
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self._running = False
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def stop(self) -> None:
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self._running = False
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async def run_once(self) -> SkillLearningWorkerResult:
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if not self.config.enabled:
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return SkillLearningWorkerResult()
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async with self._lock:
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result = SkillLearningWorkerResult()
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candidates = self._select_candidates()
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for candidate in candidates[: self.config.max_drafts_per_run]:
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result.processed += 1
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try:
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handled = await self._process_candidate(candidate)
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if handled:
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result.succeeded += 1
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else:
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result.skipped += 1
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except Exception as exc:
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result.failed += 1
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result.failures.append({"candidate_id": candidate.candidate_id, "error": str(exc)})
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self._mark_failure(candidate, str(exc))
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return result
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def _select_candidates(self) -> list[SkillLearningCandidate]:
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candidates = [
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item
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for item in self.pipeline.list_candidates()
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if item.status == "open" and item.retry_count < self.config.max_retries
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]
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return sorted(candidates, key=lambda item: (item.priority, item.confidence, item.created_at), reverse=True)
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async def _process_candidate(self, candidate: SkillLearningCandidate) -> bool:
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if self._has_active_draft(candidate):
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self.pipeline.mark_candidate_superseded(candidate.candidate_id, "active draft already exists for this skill")
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return False
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self.pipeline.mark_candidate_queued(candidate.candidate_id)
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self.pipeline.mark_candidate_synthesizing(candidate.candidate_id)
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draft = await self.pipeline.synthesize_draft(
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candidate.candidate_id,
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provider_bundle=self.provider_bundle_factory(),
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)
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self.pipeline.mark_draft_synthesized(candidate.candidate_id, draft)
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safety = self.pipeline.check_safety(draft.skill_name, draft.draft_id)
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if not safety.passed or safety.risk_level == "critical":
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return True
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await self.pipeline.evaluate_draft(
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candidate.candidate_id,
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draft.skill_name,
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draft.draft_id,
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provider_bundle=self.provider_bundle_factory(),
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)
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return True
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def _has_active_draft(self, candidate: SkillLearningCandidate) -> bool:
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target_names = set(candidate.related_skill_names)
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if candidate.draft_skill_name:
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target_names.add(candidate.draft_skill_name)
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if not target_names:
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return False
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for item in self.pipeline.list_candidates():
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if item.candidate_id == candidate.candidate_id:
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continue
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if item.status not in self._ACTIVE_DRAFT_STATUSES:
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continue
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item_names = set(item.related_skill_names)
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if item.draft_skill_name:
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item_names.add(item.draft_skill_name)
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if target_names.intersection(item_names):
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return True
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return False
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def _mark_failure(self, candidate: SkillLearningCandidate, error: str) -> None:
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retry_count = candidate.retry_count + 1
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status = "failed" if retry_count >= self.config.max_retries else "open"
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self.pipeline.mark_candidate_failed(
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candidate.candidate_id,
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error,
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retry_count=retry_count,
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terminal=(status == "failed"),
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)
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def _env_bool(name: str, default: bool) -> bool:
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raw = os.getenv(name)
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if raw is None:
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return default
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return raw.strip().lower() not in {"0", "false", "no", "off"}
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def _env_int(name: str, default: int) -> int:
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raw = os.getenv(name)
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if raw in (None, ""):
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return default
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try:
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return int(raw)
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except ValueError:
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return default
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