mirror of
https://github.com/NousResearch/hermes-agent.git
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3cd384dc43
Add a comprehensive self-evolution system that enables Hermes Agent to continuously improve through automated analysis and optimization: Core components: - reflection_engine: Nightly session analysis (1:00 AM) - evolution_proposer: Generate improvement proposals from insights - quality_scorer: Multi-dimensional session quality evaluation - strategy_injector: Inject learned strategies into new sessions - strategy_compressor: Strategy optimization and deduplication - git_analyzer: Code change pattern analysis - rule_engine: Pattern-based rule generation - feishu_notifier: Feishu card notifications for evolution events Storage: - db.py: SQLite telemetry storage - strategy_store: Persistent strategy storage - models.py: Data models Plugin integration: - plugin.yaml, hooks.py, __init__.py for plugin system - cron_jobs.py for scheduled tasks Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
363 lines
13 KiB
Python
363 lines
13 KiB
Python
"""
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Self Evolution Plugin — Data Models
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=====================================
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"""
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from __future__ import annotations
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from dataclasses import dataclass, field, asdict
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from typing import Any, Dict, List, Optional
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import json
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import time
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def _now() -> float:
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return time.time()
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def _ts() -> str:
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return time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
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# ── Quality Scoring ──────────────────────────────────────────────────────
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@dataclass
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class QualityScore:
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session_id: str
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composite: float = 0.0
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completion_rate: float = 0.0
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efficiency_score: float = 0.0
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cost_efficiency: float = 0.0
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satisfaction_proxy: float = 0.0
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task_category: str = ""
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model: str = ""
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created_at: float = field(default_factory=_now)
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def to_db_row(self) -> dict:
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return {
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"session_id": self.session_id,
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"composite_score": self.composite,
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"completion_rate": self.completion_rate,
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"efficiency_score": self.efficiency_score,
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"cost_efficiency": self.cost_efficiency,
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"satisfaction_proxy": self.satisfaction_proxy,
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"task_category": self.task_category,
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"model": self.model,
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"created_at": self.created_at,
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}
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# ── Error Analysis ───────────────────────────────────────────────────────
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@dataclass
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class ToolFailure:
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tool_name: str
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error_type: str
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count: int
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sessions_affected: List[str] = field(default_factory=list)
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example_session: str = ""
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@dataclass
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class RetryPattern:
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session_id: str
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tool_name: str
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attempt_count: int
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final_outcome: str # "success" | "failure" | "abandoned"
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@dataclass
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class ErrorAnalysis:
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tool_failures: List[ToolFailure] = field(default_factory=list)
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retry_patterns: List[RetryPattern] = field(default_factory=list)
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incomplete_sessions: List[str] = field(default_factory=list)
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user_corrections: int = 0
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correction_examples: List[str] = field(default_factory=list)
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api_error_count: int = 0
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api_error_types: Dict[str, int] = field(default_factory=dict)
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def summary(self) -> str:
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lines = []
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if self.tool_failures:
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lines.append(f"工具失败: {len(self.tool_failures)} 种工具出错")
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for tf in self.tool_failures[:5]:
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lines.append(f" - {tf.tool_name}: {tf.count}次 ({tf.error_type})")
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if self.retry_patterns:
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retries = len(self.retry_patterns)
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lines.append(f"重复重试: {retries} 次")
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if self.incomplete_sessions:
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lines.append(f"未完成session: {len(self.incomplete_sessions)} 个")
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if self.user_corrections:
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lines.append(f"用户纠正: {self.user_corrections} 次")
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if self.api_error_count:
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lines.append(f"API错误: {self.api_error_count} 次")
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return "\n".join(lines)
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# ── Time Waste Analysis ──────────────────────────────────────────────────
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@dataclass
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class ToolDuration:
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tool_name: str
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total_duration_ms: int
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call_count: int
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avg_duration_ms: float
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@dataclass
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class RepeatedOperation:
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description: str
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count: int
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sessions: List[str] = field(default_factory=list)
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wasted_ms: int = 0
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@dataclass
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class WasteAnalysis:
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slowest_tools: List[ToolDuration] = field(default_factory=list)
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repeated_operations: List[RepeatedOperation] = field(default_factory=list)
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inefficient_sessions: List[str] = field(default_factory=list)
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shortcut_opportunities: List[str] = field(default_factory=list)
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def summary(self) -> str:
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lines = []
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if self.slowest_tools:
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lines.append("耗时最长的工具:")
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for td in self.slowest_tools[:5]:
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lines.append(f" - {td.tool_name}: 平均{td.avg_duration_ms:.0f}ms ({td.call_count}次)")
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if self.repeated_operations:
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lines.append(f"重复操作: {len(self.repeated_operations)} 种")
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for ro in self.repeated_operations[:5]:
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lines.append(f" - {ro.description}: {ro.count}次")
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if self.inefficient_sessions:
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lines.append(f"低效session: {len(self.inefficient_sessions)} 个")
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if self.shortcut_opportunities:
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lines.append(f"可优化路径: {len(self.shortcut_opportunities)} 个")
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return "\n".join(lines)
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# ── Code Change Analysis ──────────────────────────────────────────────────
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@dataclass
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class CommitInfo:
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hash_short: str
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subject: str
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body: str = ""
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author: str = ""
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timestamp: float = 0.0
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files_changed: int = 0
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insertions: int = 0
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deletions: int = 0
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file_list: List[str] = field(default_factory=list)
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@dataclass
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class CodeChangeAnalysis:
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commits: List[CommitInfo] = field(default_factory=list)
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total_commits: int = 0
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total_insertions: int = 0
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total_deletions: int = 0
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total_files_changed: int = 0
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authors: List[str] = field(default_factory=list)
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change_categories: Dict[str, int] = field(default_factory=dict)
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areas_touched: List[str] = field(default_factory=list)
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def summary(self) -> str:
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if not self.commits:
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return "代码更新: 无新提交"
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lines = [
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f"代码更新: {self.total_commits} commits, "
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f"+{self.total_insertions}/-{self.total_deletions} lines, "
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f"{self.total_files_changed} files changed",
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]
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if self.change_categories:
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cats = ", ".join(f"{k}: {v}" for k, v in self.change_categories.items())
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lines.append(f"提交类型分布: {cats}")
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if self.areas_touched:
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lines.append(f"涉及模块: {', '.join(self.areas_touched)}")
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lines.append("主要变更:")
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for c in self.commits[:8]:
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lines.append(f" - {c.subject} ({c.hash_short}, +{c.insertions}/-{c.deletions})")
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return "\n".join(lines)
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# ── Reflection Report ────────────────────────────────────────────────────
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@dataclass
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class ReflectionReport:
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period_start: float
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period_end: float
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sessions_analyzed: int = 0
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avg_score: float = 0.0
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error_summary: str = ""
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waste_summary: str = ""
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worst_patterns: List[str] = field(default_factory=list)
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best_patterns: List[str] = field(default_factory=list)
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tool_insights: Dict[str, Dict[str, Any]] = field(default_factory=dict)
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recommendations: List[str] = field(default_factory=list)
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code_change_summary: str = ""
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model_used: str = ""
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created_at: float = field(default_factory=_now)
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def to_db_row(self) -> dict:
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return {
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"period_start": self.period_start,
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"period_end": self.period_end,
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"sessions_analyzed": self.sessions_analyzed,
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"avg_score": self.avg_score,
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"error_summary": self.error_summary,
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"waste_summary": self.waste_summary,
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"worst_patterns": json.dumps(self.worst_patterns, ensure_ascii=False),
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"best_patterns": json.dumps(self.best_patterns, ensure_ascii=False),
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"tool_insights": json.dumps(self.tool_insights, ensure_ascii=False),
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"recommendations": json.dumps(self.recommendations, ensure_ascii=False),
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"code_change_summary": self.code_change_summary,
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"model_used": self.model_used,
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"created_at": self.created_at,
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}
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# ── Evolution Proposal ───────────────────────────────────────────────────
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@dataclass
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class Proposal:
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id: str
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proposal_type: str # skill | strategy | memory | tool_preference | code_improvement
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title: str
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description: str
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expected_impact: str = ""
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risk_assessment: str = "low"
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rollback_plan: str = ""
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status: str = "pending_approval"
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report_id: Optional[int] = None
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user_feedback: str = ""
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created_at: float = field(default_factory=_now)
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resolved_at: Optional[float] = None
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def to_db_row(self) -> dict:
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return {
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"id": self.id,
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"report_id": self.report_id,
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"proposal_type": self.proposal_type,
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"title": self.title,
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"description": self.description,
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"expected_impact": self.expected_impact,
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"risk_assessment": self.risk_assessment,
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"rollback_plan": self.rollback_plan,
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"status": self.status,
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"user_feedback": self.user_feedback,
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"created_at": self.created_at,
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"resolved_at": self.resolved_at,
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}
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# ── Improvement Unit (A/B Test Tracking) ─────────────────────────────────
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@dataclass
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class ImprovementUnit:
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id: str
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proposal_id: str
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change_type: str
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version: int = 0
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baseline_score: float = 0.0
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current_score: float = 0.0
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sessions_sampled: int = 0
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min_sessions: int = 10
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min_improvement: float = 0.05
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max_regression: float = 0.10
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status: str = "active" # active | promoted | reverted
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created_at: float = field(default_factory=_now)
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resolved_at: Optional[float] = None
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@property
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def should_revert(self) -> bool:
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return (
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self.sessions_sampled >= 3
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and (self.baseline_score - self.current_score) > self.max_regression
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)
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@property
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def should_promote(self) -> bool:
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return (
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self.sessions_sampled >= self.min_sessions
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and (self.current_score - self.baseline_score) >= self.min_improvement
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)
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def to_db_row(self) -> dict:
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return {
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"id": self.id,
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"proposal_id": self.proposal_id,
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"change_type": self.change_type,
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"version": self.version,
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"baseline_score": self.baseline_score,
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"current_score": self.current_score,
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"sessions_sampled": self.sessions_sampled,
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"min_sessions": self.min_sessions,
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"min_improvement": self.min_improvement,
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"max_regression": self.max_regression,
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"status": self.status,
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"created_at": self.created_at,
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"resolved_at": self.resolved_at,
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}
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# ── Strategy Rule ────────────────────────────────────────────────────────
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@dataclass
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class StrategyCondition:
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field: str
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operator: str # regex_match | contains | equals | not_contains
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pattern: str
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@dataclass
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class StrategyRule:
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id: str
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name: str
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strategy_type: str # hint | avoid | prefer
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description: str
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conditions: List[StrategyCondition] = field(default_factory=list)
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hint_text: str = ""
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severity: str = "medium" # high | medium | low
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enabled: bool = True
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version: int = 1
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source: str = "learned" # learned | manual | default
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created_at: float = field(default_factory=_now)
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def to_dict(self) -> dict:
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return {
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"id": self.id,
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"name": self.name,
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"strategy_type": self.strategy_type,
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"description": self.description,
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"conditions": [
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{"field": c.field, "operator": c.operator, "pattern": c.pattern}
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for c in self.conditions
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],
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"hint_text": self.hint_text,
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"severity": self.severity,
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"enabled": self.enabled,
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"version": self.version,
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"source": self.source,
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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, d: dict) -> StrategyRule:
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conditions = [
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StrategyCondition(field=c["field"], operator=c["operator"], pattern=c["pattern"])
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for c in d.get("conditions", [])
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]
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return cls(
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id=d["id"],
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name=d["name"],
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strategy_type=d.get("strategy_type", "hint"),
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description=d.get("description", ""),
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conditions=conditions,
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hint_text=d.get("hint_text", ""),
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severity=d.get("severity", "medium"),
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enabled=d.get("enabled", True),
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version=d.get("version", 1),
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source=d.get("source", "learned"),
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created_at=d.get("created_at", _now()),
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)
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