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feat: add self-evolution plugin — agent self-optimization system
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>
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self_evolution/evolution_executor.py
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self_evolution/evolution_executor.py
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"""
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Self Evolution Plugin — Evolution Executor
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============================================
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Executes approved evolution proposals with rollback support.
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Design reference: Claude Code plugins/ralph-wiggum/
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- Self-referential feedback loop: execute → verify → rollback if needed
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- Each change has a "completion promise" (verification criteria)
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- Iteration > Perfection
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"""
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from __future__ import annotations
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import json
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import logging
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import time
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import uuid
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from pathlib import Path
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from typing import Optional
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from self_evolution import db
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from self_evolution.models import Proposal, ImprovementUnit
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logger = logging.getLogger(__name__)
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from self_evolution.paths import DATA_DIR as STRATEGIES_DIR, STRATEGIES_FILE, ARCHIVE_DIR
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from self_evolution.paths import SKILLS_DIR, MEMORIES_DIR
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class EvolutionExecutor:
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"""Execute approved evolution proposals.
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Supported proposal types:
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- skill: create a new skill via skill_manager_tool
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- strategy: update strategy rules
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- memory: update PERFORMANCE.md via memory_tool
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- tool_preference: update tool preference config
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"""
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def execute(self, proposal: Proposal):
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"""Execute an approved proposal."""
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logger.info("Executing proposal: %s (%s)", proposal.id, proposal.proposal_type)
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try:
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match proposal.proposal_type:
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case "skill":
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self._create_skill(proposal)
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case "strategy":
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self._update_strategy(proposal)
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case "memory":
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self._update_memory(proposal)
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case "tool_preference":
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self._update_tool_preference(proposal)
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case "code_improvement":
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self._save_optimization_request(proposal)
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# Mark as executed
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db.update(
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"evolution_proposals",
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{"status": "executed", "resolved_at": time.time()},
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where="id = ?",
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where_params=(proposal.id,),
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)
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# Create improvement tracking unit
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self._create_tracking_unit(proposal)
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logger.info("Proposal %s executed successfully", proposal.id)
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except Exception as exc:
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logger.exception("Failed to execute proposal %s: %s", proposal.id, exc)
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db.update(
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"evolution_proposals",
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{"status": "execution_failed", "resolved_at": time.time()},
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where="id = ?",
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where_params=(proposal.id,),
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)
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def check_and_rollback(self):
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"""Check active improvement units and rollback if needed.
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Called during dream consolidation to verify previous changes.
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"""
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units = db.fetch_all("improvement_units", where="status = 'active'")
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for unit_data in units:
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unit = ImprovementUnit(
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id=unit_data["id"],
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proposal_id=unit_data["proposal_id"],
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change_type=unit_data["change_type"],
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version=unit_data.get("version", 0),
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baseline_score=unit_data.get("baseline_score", 0),
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current_score=unit_data.get("current_score", 0),
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sessions_sampled=unit_data.get("sessions_sampled", 0),
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min_sessions=unit_data.get("min_sessions", 10),
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min_improvement=unit_data.get("min_improvement", 0.05),
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max_regression=unit_data.get("max_regression", 0.10),
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)
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# Update current score from recent sessions
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self._update_unit_score(unit)
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if unit.should_revert:
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self._revert(unit)
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logger.warning("Rolled back improvement unit %s", unit.id)
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elif unit.should_promote:
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self._promote(unit)
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logger.info("Promoted improvement unit %s", unit.id)
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# ── Proposal Type Handlers ────────────────────────────────────────────
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def _create_skill(self, proposal: Proposal):
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"""Create a new skill via the skill_manager_tool."""
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from self_evolution.strategy_store import StrategyStore
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store = StrategyStore()
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skill_dir = SKILLS_DIR / proposal.id
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skill_dir.mkdir(parents=True, exist_ok=True)
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skill_content = (
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f"---\n"
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f"name: {proposal.id}\n"
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f"description: {proposal.title}\n"
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f"---\n\n"
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f"{proposal.description}\n"
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)
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(skill_dir / "SKILL.md").write_text(skill_content, encoding="utf-8")
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logger.info("Created learned skill: %s", skill_dir)
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def _update_strategy(self, proposal: Proposal):
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"""Update strategy rules file with version tracking."""
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from self_evolution.strategy_store import StrategyStore
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store = StrategyStore()
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current = store.load()
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# Check for duplicate strategies by title similarity
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rules = current.get("rules", [])
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existing_titles = {r.get("name", "").strip().lower() for r in rules}
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if proposal.title.strip().lower() in existing_titles:
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logger.warning("Skipping duplicate strategy: %s", proposal.title)
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return
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# Archive current version
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version = current.get("version", 0) + 1
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store.archive(version - 1)
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# Parse new strategy from proposal description
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new_strategy = {
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"id": proposal.id,
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"name": proposal.title,
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"type": "learned",
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"description": proposal.description,
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"hint_text": proposal.description,
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"conditions": [],
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"severity": "medium",
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"created_at": time.time(),
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}
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# Add to strategies
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rules.append(new_strategy)
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current["rules"] = rules
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current["version"] = version
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store.save(current)
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logger.info("Updated strategies to version %d", version)
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# Invalidate injector cache so new strategy takes effect immediately
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from self_evolution.strategy_injector import invalidate_cache
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invalidate_cache()
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def _update_memory(self, proposal: Proposal):
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"""Update PERFORMANCE.md via the memory system."""
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perf_path = MEMORIES_DIR / "PERFORMANCE.md"
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perf_path.parent.mkdir(parents=True, exist_ok=True)
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existing = ""
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if perf_path.exists():
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existing = perf_path.read_text(encoding="utf-8")
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# Append new entry
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timestamp = time.strftime("%Y-%m-%d %H:%M", time.localtime())
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entry = f"\n## [{timestamp}] 自动学习\n{proposal.description}\n"
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# Keep file under reasonable size (last 50 entries)
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entries = (existing + entry).split("\n## ")
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if len(entries) > 50:
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entries = entries[-50:]
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perf_path.write_text("\n## ".join(entries), encoding="utf-8")
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logger.info("Updated PERFORMANCE.md")
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def _update_tool_preference(self, proposal: Proposal):
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"""Update tool preference config."""
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prefs_path = STRATEGIES_DIR / "tool_preferences.json"
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prefs = {}
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if prefs_path.exists():
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prefs = json.loads(prefs_path.read_text(encoding="utf-8"))
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prefs[proposal.id] = {
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"description": proposal.description,
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"expected_impact": proposal.expected_impact,
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"created_at": time.time(),
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}
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prefs_path.write_text(
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json.dumps(prefs, ensure_ascii=False, indent=2),
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encoding="utf-8",
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)
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logger.info("Updated tool preferences: %s", proposal.id)
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# ── Tracking & Verification ───────────────────────────────────────────
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def _create_tracking_unit(self, proposal: Proposal):
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"""Create an improvement tracking unit after execution.
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Inspired by Ralph Wiggum's completion_promise pattern.
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"""
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# Get baseline score from recent sessions
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recent = db.fetch_all(
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"session_scores",
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order_by="created_at DESC",
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limit=10,
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)
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baseline = (
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sum(s.get("composite_score", 0) for s in recent) / len(recent)
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if recent else 0
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)
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unit = ImprovementUnit(
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id=f"unit-{uuid.uuid4().hex[:8]}",
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proposal_id=proposal.id,
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change_type=proposal.proposal_type,
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baseline_score=baseline,
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min_sessions=10,
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min_improvement=0.05,
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max_regression=0.10,
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)
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db.insert("improvement_units", unit.to_db_row())
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logger.info("Created tracking unit: %s (baseline=%.3f)", unit.id, baseline)
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def _update_unit_score(self, unit: ImprovementUnit):
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"""Update the current score for an improvement unit."""
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# Count sessions since this unit was created
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unit_data = db.fetch_one("improvement_units", where="id = ?", params=(unit.id,))
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if not unit_data:
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return
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created_at = unit_data.get("created_at", 0)
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recent = db.fetch_all(
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"session_scores",
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where="created_at >= ?",
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params=(created_at,),
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order_by="created_at DESC",
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)
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if recent:
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current_score = sum(s.get("composite_score", 0) for s in recent) / len(recent)
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sessions_sampled = len(recent)
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db.update(
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"improvement_units",
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{
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"current_score": current_score,
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"sessions_sampled": sessions_sampled,
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},
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where="id = ?",
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where_params=(unit.id,),
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)
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unit.current_score = current_score
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unit.sessions_sampled = sessions_sampled
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def _revert(self, unit: ImprovementUnit):
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"""Revert a change by restoring the previous version."""
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from self_evolution.strategy_store import StrategyStore
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store = StrategyStore()
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if unit.version > 0:
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old = store.load_archive(unit.version - 1)
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if old:
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store.save(old)
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db.update(
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"improvement_units",
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{"status": "reverted", "resolved_at": time.time()},
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where="id = ?",
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where_params=(unit.id,),
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)
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def _promote(self, unit: ImprovementUnit):
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"""Promote an improvement unit from active to permanent."""
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db.update(
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"improvement_units",
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{"status": "promoted", "resolved_at": time.time()},
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where="id = ?",
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where_params=(unit.id,),
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)
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# ── Code Improvement (save request document) ────────────────────────────
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def _save_optimization_request(self, proposal: Proposal):
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"""Save a code improvement request as a document.
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Does NOT auto-modify code. The user reviews the request and decides
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whether to implement changes manually or via Claude Code.
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"""
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req_dir = DATA_DIR / "optimization_requests"
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req_dir.mkdir(parents=True, exist_ok=True)
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doc_path = req_dir / f"{proposal.id}.md"
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doc_content = (
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f"# 程序优化需求\n\n"
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f"**标题**: {proposal.title}\n"
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f"**预期影响**: {proposal.expected_impact}\n"
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f"**风险评估**: {proposal.risk_assessment}\n"
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f"**回滚方案**: {proposal.rollback_plan}\n"
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f"**创建时间**: {time.strftime('%Y-%m-%d %H:%M', time.localtime())}\n\n"
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f"---\n\n"
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f"{proposal.description}\n"
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)
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doc_path.write_text(doc_content, encoding="utf-8")
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logger.info("Saved optimization request: %s", doc_path)
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