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。
This commit is contained in:
@ -1,2 +1,19 @@
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"""Memory related to skill evolution."""
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from .models import (
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SkillDraftEvalReport,
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SkillDraftSafetyReport,
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SkillLearningAuditEvent,
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SkillLearningCandidate,
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SkillPerformanceSnapshot,
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)
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from .store import SkillLearningStore
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__all__ = [
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"SkillDraftEvalReport",
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"SkillDraftSafetyReport",
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"SkillLearningAuditEvent",
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"SkillLearningCandidate",
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"SkillLearningStore",
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"SkillPerformanceSnapshot",
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]
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289
app-instance/backend/beaver/memory/skills/models.py
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289
app-instance/backend/beaver/memory/skills/models.py
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@ -0,0 +1,289 @@
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"""Aggregated skill learning models."""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from datetime import datetime, timezone
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from typing import Any
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LEARNING_CANDIDATE_STATUSES = {
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"open",
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"queued",
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"synthesizing",
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"draft_ready",
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"safety_failed",
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"eval_failed",
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"review_pending",
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"approved",
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"rejected",
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"published",
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"failed",
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"superseded",
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}
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RISK_LEVELS = {"low", "medium", "high", "critical"}
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@dataclass(slots=True)
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class SkillPerformanceSnapshot:
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skill_name: str
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skill_version: str
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activation_count: int
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success_count: int
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failure_count: int
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latest_used_at: str
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last_feedback_score: float | None = None
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def to_dict(self) -> dict[str, Any]:
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return {
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"skill_name": self.skill_name,
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"skill_version": self.skill_version,
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"activation_count": self.activation_count,
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"success_count": self.success_count,
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"failure_count": self.failure_count,
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"latest_used_at": self.latest_used_at,
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"last_feedback_score": self.last_feedback_score,
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}
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@classmethod
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def from_dict(cls, payload: dict[str, Any]) -> "SkillPerformanceSnapshot":
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value = payload.get("last_feedback_score")
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return cls(
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skill_name=str(payload["skill_name"]),
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skill_version=str(payload["skill_version"]),
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activation_count=int(payload.get("activation_count", 0) or 0),
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success_count=int(payload.get("success_count", 0) or 0),
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failure_count=int(payload.get("failure_count", 0) or 0),
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latest_used_at=str(payload.get("latest_used_at") or ""),
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last_feedback_score=None if value in (None, "") else float(value),
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)
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@dataclass(slots=True)
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class SkillLearningCandidate:
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candidate_id: str
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kind: str
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source_run_ids: list[str]
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source_session_ids: list[str]
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related_skill_names: list[str]
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reason: str
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evidence: dict[str, Any] = field(default_factory=dict)
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status: str = "open"
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priority: int = 0
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confidence: float = 0.0
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risk_level: str = "medium"
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owner: str | None = None
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retry_count: int = 0
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last_error: str | None = None
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trigger_reason: str = ""
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evidence_summary: str = ""
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draft_skill_name: str | None = None
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draft_id: str | None = None
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safety_report_id: str | None = None
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eval_report_id: str | None = None
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created_at: str = ""
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updated_at: str = ""
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def to_dict(self) -> dict[str, Any]:
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return {
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"candidate_id": self.candidate_id,
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"kind": self.kind,
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"source_run_ids": list(self.source_run_ids),
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"source_session_ids": list(self.source_session_ids),
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"related_skill_names": list(self.related_skill_names),
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"reason": self.reason,
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"evidence": dict(self.evidence),
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"status": self.status,
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"priority": self.priority,
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"confidence": self.confidence,
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"risk_level": self.risk_level,
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"owner": self.owner,
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"retry_count": self.retry_count,
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"last_error": self.last_error,
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"trigger_reason": self.trigger_reason,
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"evidence_summary": self.evidence_summary,
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"draft_skill_name": self.draft_skill_name,
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"draft_id": self.draft_id,
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"safety_report_id": self.safety_report_id,
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"eval_report_id": self.eval_report_id,
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"created_at": self.created_at,
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"updated_at": self.updated_at,
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}
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@classmethod
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def from_dict(cls, payload: dict[str, Any]) -> "SkillLearningCandidate":
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now = _utc_now()
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status = str(payload.get("status") or "open")
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risk_level = str(payload.get("risk_level") or "medium")
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return cls(
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candidate_id=str(payload["candidate_id"]),
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kind=str(payload.get("kind") or "revise_skill"),
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source_run_ids=[str(item) for item in payload.get("source_run_ids") or []],
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source_session_ids=[str(item) for item in payload.get("source_session_ids") or []],
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related_skill_names=[str(item) for item in payload.get("related_skill_names") or []],
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reason=str(payload.get("reason") or ""),
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evidence=dict(payload.get("evidence") or {}),
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status=status if status in LEARNING_CANDIDATE_STATUSES else "open",
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priority=int(payload.get("priority", 0) or 0),
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confidence=float(payload.get("confidence", 0.0) or 0.0),
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risk_level=risk_level if risk_level in RISK_LEVELS else "medium",
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owner=_optional_str(payload.get("owner")),
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retry_count=int(payload.get("retry_count", 0) or 0),
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last_error=_optional_str(payload.get("last_error")),
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trigger_reason=str(payload.get("trigger_reason") or payload.get("reason") or ""),
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evidence_summary=str(payload.get("evidence_summary") or _summarize_evidence(payload)),
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draft_skill_name=_optional_str(payload.get("draft_skill_name")),
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draft_id=_optional_str(payload.get("draft_id")),
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safety_report_id=_optional_str(payload.get("safety_report_id")),
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eval_report_id=_optional_str(payload.get("eval_report_id")),
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created_at=str(payload.get("created_at") or now),
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updated_at=str(payload.get("updated_at") or payload.get("created_at") or now),
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)
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@dataclass(slots=True)
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class SkillLearningAuditEvent:
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event_id: str
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candidate_id: str
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event_type: str
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created_at: str
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payload: dict[str, Any] = field(default_factory=dict)
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def to_dict(self) -> dict[str, Any]:
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return {
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"event_id": self.event_id,
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"candidate_id": self.candidate_id,
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"event_type": self.event_type,
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"created_at": self.created_at,
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"payload": dict(self.payload),
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}
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@classmethod
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def from_dict(cls, payload: dict[str, Any]) -> "SkillLearningAuditEvent":
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return cls(
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event_id=str(payload["event_id"]),
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candidate_id=str(payload["candidate_id"]),
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event_type=str(payload.get("event_type") or ""),
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created_at=str(payload.get("created_at") or ""),
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payload=dict(payload.get("payload") or {}),
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)
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@dataclass(slots=True)
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class SkillDraftSafetyReport:
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report_id: str
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skill_name: str
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draft_id: str
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passed: bool
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risk_level: str
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issues: list[str] = field(default_factory=list)
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blocked_reasons: list[str] = field(default_factory=list)
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suggested_fix: str = ""
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created_at: str = ""
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def to_dict(self) -> dict[str, Any]:
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return {
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"report_id": self.report_id,
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"skill_name": self.skill_name,
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"draft_id": self.draft_id,
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"passed": self.passed,
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"risk_level": self.risk_level,
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"issues": list(self.issues),
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"blocked_reasons": list(self.blocked_reasons),
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"suggested_fix": self.suggested_fix,
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"created_at": self.created_at,
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}
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@classmethod
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def from_dict(cls, payload: dict[str, Any]) -> "SkillDraftSafetyReport":
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risk_level = str(payload.get("risk_level") or "medium")
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return cls(
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report_id=str(payload["report_id"]),
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skill_name=str(payload["skill_name"]),
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draft_id=str(payload["draft_id"]),
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passed=bool(payload.get("passed")),
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risk_level=risk_level if risk_level in RISK_LEVELS else "medium",
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issues=[str(item) for item in payload.get("issues") or []],
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blocked_reasons=[str(item) for item in payload.get("blocked_reasons") or []],
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suggested_fix=str(payload.get("suggested_fix") or ""),
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created_at=str(payload.get("created_at") or ""),
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)
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@dataclass(slots=True)
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class SkillDraftEvalReport:
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report_id: str
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skill_name: str
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draft_id: str
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candidate_id: str
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passed: bool
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baseline_score_avg: float
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candidate_score_avg: float
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score_delta: float
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regression_count: int
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improved_count: int
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unchanged_count: int
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cases: list[dict[str, Any]] = field(default_factory=list)
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status: str = "completed"
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created_at: str = ""
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def to_dict(self) -> dict[str, Any]:
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return {
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"report_id": self.report_id,
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"skill_name": self.skill_name,
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"draft_id": self.draft_id,
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"candidate_id": self.candidate_id,
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"passed": self.passed,
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"baseline_score_avg": self.baseline_score_avg,
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"candidate_score_avg": self.candidate_score_avg,
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"score_delta": self.score_delta,
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"regression_count": self.regression_count,
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"improved_count": self.improved_count,
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"unchanged_count": self.unchanged_count,
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"cases": [dict(item) for item in self.cases],
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"status": self.status,
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"created_at": self.created_at,
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}
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@classmethod
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def from_dict(cls, payload: dict[str, Any]) -> "SkillDraftEvalReport":
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return cls(
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report_id=str(payload["report_id"]),
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skill_name=str(payload["skill_name"]),
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draft_id=str(payload["draft_id"]),
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candidate_id=str(payload.get("candidate_id") or ""),
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passed=bool(payload.get("passed")),
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baseline_score_avg=float(payload.get("baseline_score_avg", 0.0) or 0.0),
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candidate_score_avg=float(payload.get("candidate_score_avg", 0.0) or 0.0),
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score_delta=float(payload.get("score_delta", 0.0) or 0.0),
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regression_count=int(payload.get("regression_count", 0) or 0),
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improved_count=int(payload.get("improved_count", 0) or 0),
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unchanged_count=int(payload.get("unchanged_count", 0) or 0),
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cases=[dict(item) for item in payload.get("cases") or [] if isinstance(item, dict)],
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status=str(payload.get("status") or "completed"),
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created_at=str(payload.get("created_at") or ""),
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)
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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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return str(value)
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def _summarize_evidence(payload: dict[str, Any]) -> str:
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evidence = payload.get("evidence")
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if isinstance(evidence, dict):
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theme = evidence.get("theme")
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if theme:
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return f"Theme: {theme}"
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skill_version = evidence.get("skill_version")
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if skill_version:
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return f"Skill version: {skill_version}"
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source_run_ids = payload.get("source_run_ids") or []
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return f"{len(source_run_ids)} source run(s)"
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def _utc_now() -> str:
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return datetime.now(timezone.utc).isoformat()
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216
app-instance/backend/beaver/memory/skills/store.py
Normal file
216
app-instance/backend/beaver/memory/skills/store.py
Normal file
@ -0,0 +1,216 @@
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"""File-backed skill learning store."""
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from __future__ import annotations
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import json
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from pathlib import Path
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from uuid import uuid4
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from .models import (
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SkillDraftEvalReport,
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SkillDraftSafetyReport,
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SkillLearningAuditEvent,
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SkillLearningCandidate,
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SkillPerformanceSnapshot,
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)
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class SkillLearningStore:
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def __init__(self, root: str | Path) -> None:
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self.root = Path(root)
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self.root.mkdir(parents=True, exist_ok=True)
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self.performance_path = self.root / "performance.jsonl"
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self.candidates_path = self.root / "learning-candidates.jsonl"
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self.audit_path = self.root / "learning-audit.jsonl"
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self.safety_reports_dir = self.root / "safety-reports"
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self.eval_reports_dir = self.root / "eval-reports"
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def record_learning_candidate(self, candidate: SkillLearningCandidate) -> None:
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normalized = SkillLearningCandidate.from_dict(candidate.to_dict())
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self._append_jsonl(self.candidates_path, normalized.to_dict())
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self.append_audit_event(
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normalized.candidate_id,
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"candidate_created",
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{
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"kind": normalized.kind,
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"status": normalized.status,
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"reason": normalized.reason,
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},
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)
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def update_learning_candidate(self, candidate_id: str, **updates: object) -> SkillLearningCandidate | None:
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candidates = self.list_learning_candidates()
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updated: SkillLearningCandidate | None = None
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for index, candidate in enumerate(candidates):
|
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if candidate.candidate_id != candidate_id:
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continue
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payload = candidate.to_dict()
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payload.update(updates)
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if "updated_at" not in updates:
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payload["updated_at"] = _utc_now()
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updated = SkillLearningCandidate.from_dict(payload)
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candidates[index] = updated
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break
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if updated is None:
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return None
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self.candidates_path.parent.mkdir(parents=True, exist_ok=True)
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self.candidates_path.write_text(
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"".join(
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json.dumps(candidate.to_dict(), ensure_ascii=False, sort_keys=True) + "\n"
|
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for candidate in candidates
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),
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encoding="utf-8",
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||||
)
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return updated
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|
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def transition_learning_candidate(
|
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self,
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candidate_id: str,
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status: str,
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*,
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event_type: str | None = None,
|
||||
payload: dict | None = None,
|
||||
**updates: object,
|
||||
) -> SkillLearningCandidate | None:
|
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updated = self.update_learning_candidate(candidate_id, status=status, **updates)
|
||||
if updated is not None:
|
||||
self.append_audit_event(
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candidate_id,
|
||||
event_type or f"candidate_{status}",
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||||
{"status": status, **dict(payload or {})},
|
||||
)
|
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return updated
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||||
|
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def list_learning_candidates(self, status: str | None = None) -> list[SkillLearningCandidate]:
|
||||
results: list[SkillLearningCandidate] = []
|
||||
for payload in self._read_jsonl(self.candidates_path):
|
||||
candidate = SkillLearningCandidate.from_dict(payload)
|
||||
if status is not None and candidate.status != status:
|
||||
continue
|
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results.append(candidate)
|
||||
return results
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||||
|
||||
def update_performance_snapshot(self, snapshot: SkillPerformanceSnapshot) -> None:
|
||||
snapshots = self.list_performance_snapshots()
|
||||
filtered = [
|
||||
item
|
||||
for item in snapshots
|
||||
if not (item.skill_name == snapshot.skill_name and item.skill_version == snapshot.skill_version)
|
||||
]
|
||||
filtered.append(snapshot)
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||||
self.performance_path.write_text(
|
||||
"".join(json.dumps(item.to_dict(), ensure_ascii=False, sort_keys=True) + "\n" for item in filtered),
|
||||
encoding="utf-8",
|
||||
)
|
||||
|
||||
def list_performance_snapshots(self) -> list[SkillPerformanceSnapshot]:
|
||||
return [SkillPerformanceSnapshot.from_dict(item) for item in self._read_jsonl(self.performance_path)]
|
||||
|
||||
def list_low_performing_versions(self, *, minimum_activations: int = 2, success_ratio_threshold: float = 0.5) -> list[SkillPerformanceSnapshot]:
|
||||
results: list[SkillPerformanceSnapshot] = []
|
||||
for snapshot in self.list_performance_snapshots():
|
||||
if snapshot.activation_count < minimum_activations:
|
||||
continue
|
||||
if snapshot.activation_count == 0:
|
||||
continue
|
||||
ratio = snapshot.success_count / snapshot.activation_count
|
||||
if ratio <= success_ratio_threshold:
|
||||
results.append(snapshot)
|
||||
return results
|
||||
|
||||
def list_merge_candidates(self) -> list[SkillLearningCandidate]:
|
||||
return [item for item in self.list_learning_candidates(status="open") if item.kind == "merge_skills"]
|
||||
|
||||
def append_audit_event(self, candidate_id: str, event_type: str, payload: dict | None = None) -> SkillLearningAuditEvent:
|
||||
event = SkillLearningAuditEvent(
|
||||
event_id=uuid4().hex,
|
||||
candidate_id=candidate_id,
|
||||
event_type=event_type,
|
||||
created_at=_utc_now(),
|
||||
payload=dict(payload or {}),
|
||||
)
|
||||
self._append_jsonl(self.audit_path, event.to_dict())
|
||||
return event
|
||||
|
||||
def list_audit_events(self, candidate_id: str | None = None) -> list[SkillLearningAuditEvent]:
|
||||
events = [SkillLearningAuditEvent.from_dict(item) for item in self._read_jsonl(self.audit_path)]
|
||||
if candidate_id is None:
|
||||
return events
|
||||
return [event for event in events if event.candidate_id == candidate_id]
|
||||
|
||||
def write_safety_report(self, report: SkillDraftSafetyReport) -> None:
|
||||
path = self._report_path(self.safety_reports_dir, report.skill_name, report.draft_id, report.report_id)
|
||||
path.parent.mkdir(parents=True, exist_ok=True)
|
||||
path.write_text(json.dumps(report.to_dict(), ensure_ascii=False, sort_keys=True) + "\n", encoding="utf-8")
|
||||
|
||||
def get_safety_report(self, skill_name: str, draft_id: str, report_id: str | None = None) -> SkillDraftSafetyReport | None:
|
||||
reports = self.list_safety_reports(skill_name, draft_id)
|
||||
if report_id is not None:
|
||||
return next((item for item in reports if item.report_id == report_id), None)
|
||||
return reports[-1] if reports else None
|
||||
|
||||
def list_safety_reports(self, skill_name: str, draft_id: str) -> list[SkillDraftSafetyReport]:
|
||||
root = self.safety_reports_dir / skill_name / draft_id
|
||||
if not root.exists():
|
||||
return []
|
||||
return [
|
||||
SkillDraftSafetyReport.from_dict(self._read_json(path))
|
||||
for path in sorted(root.glob("report-*.json"))
|
||||
]
|
||||
|
||||
def write_eval_report(self, report: SkillDraftEvalReport) -> None:
|
||||
path = self._report_path(self.eval_reports_dir, report.skill_name, report.draft_id, report.report_id)
|
||||
path.parent.mkdir(parents=True, exist_ok=True)
|
||||
path.write_text(json.dumps(report.to_dict(), ensure_ascii=False, sort_keys=True) + "\n", encoding="utf-8")
|
||||
|
||||
def get_eval_report(self, skill_name: str, draft_id: str, report_id: str | None = None) -> SkillDraftEvalReport | None:
|
||||
reports = self.list_eval_reports(skill_name, draft_id)
|
||||
if report_id is not None:
|
||||
return next((item for item in reports if item.report_id == report_id), None)
|
||||
return reports[-1] if reports else None
|
||||
|
||||
def list_eval_reports(self, skill_name: str, draft_id: str) -> list[SkillDraftEvalReport]:
|
||||
root = self.eval_reports_dir / skill_name / draft_id
|
||||
if not root.exists():
|
||||
return []
|
||||
return [
|
||||
SkillDraftEvalReport.from_dict(self._read_json(path))
|
||||
for path in sorted(root.glob("report-*.json"))
|
||||
]
|
||||
|
||||
@staticmethod
|
||||
def _report_path(root: Path, skill_name: str, draft_id: str, report_id: str) -> Path:
|
||||
return root / skill_name / draft_id / f"report-{report_id}.json"
|
||||
|
||||
@staticmethod
|
||||
def _append_jsonl(path: Path, payload: dict) -> None:
|
||||
path.parent.mkdir(parents=True, exist_ok=True)
|
||||
with path.open("a", encoding="utf-8") as handle:
|
||||
handle.write(json.dumps(payload, ensure_ascii=False, sort_keys=True) + "\n")
|
||||
|
||||
@staticmethod
|
||||
def _read_jsonl(path: Path) -> list[dict]:
|
||||
if not path.exists():
|
||||
return []
|
||||
results: list[dict] = []
|
||||
for line in path.read_text(encoding="utf-8").splitlines():
|
||||
cleaned = line.strip()
|
||||
if not cleaned:
|
||||
continue
|
||||
payload = json.loads(cleaned)
|
||||
if isinstance(payload, dict):
|
||||
results.append(payload)
|
||||
return results
|
||||
|
||||
@staticmethod
|
||||
def _read_json(path: Path) -> dict:
|
||||
payload = json.loads(path.read_text(encoding="utf-8"))
|
||||
if not isinstance(payload, dict):
|
||||
raise ValueError(f"Expected JSON object in {path}")
|
||||
return payload
|
||||
|
||||
|
||||
def _utc_now() -> str:
|
||||
from datetime import datetime, timezone
|
||||
|
||||
return datetime.now(timezone.utc).isoformat()
|
||||
Reference in New Issue
Block a user