Initial commit: workspace setup with skills, memory, config
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204
skills/qdrant-memory/scripts/consolidate_memories.py
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skills/qdrant-memory/scripts/consolidate_memories.py
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#!/usr/bin/env python3
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"""
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Memory consolidation - weekly and monthly maintenance
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Usage: consolidate_memories.py weekly|monthly
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"""
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import argparse
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import json
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import os
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import re
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import subprocess
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import sys
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from datetime import datetime, timedelta
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from pathlib import Path
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WORKSPACE = "/root/.openclaw/workspace"
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MEMORY_DIR = f"{WORKSPACE}/memory"
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MEMORY_FILE = f"{WORKSPACE}/MEMORY.md"
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def get_recent_daily_logs(days=7):
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"""Get daily log files from the last N days"""
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logs = []
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cutoff = datetime.now() - timedelta(days=days)
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for file in Path(MEMORY_DIR).glob("*.md"):
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# Extract date from filename (YYYY-MM-DD.md)
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match = re.match(r"(\d{4}-\d{2}-\d{2})\.md", file.name)
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if match:
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file_date = datetime.strptime(match.group(1), "%Y-%m-%d")
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if file_date >= cutoff:
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logs.append((file_date, file))
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return sorted(logs, reverse=True)
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def extract_key_memories(content):
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"""Extract key memories from daily log content"""
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key_memories = []
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# Look for lesson learned sections
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lessons_pattern = r"(?:##?\s*Lessons?\s*Learned|###?\s*Mistakes?|###?\s*Fixes?)(.*?)(?=##?|$)"
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lessons_match = re.search(lessons_pattern, content, re.DOTALL | re.IGNORECASE)
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if lessons_match:
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lessons_section = lessons_match.group(1)
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# Extract bullet points
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for line in lessons_section.split('\n'):
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if line.strip().startswith('-') or line.strip().startswith('*'):
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key_memories.append({
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"type": "lesson",
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"content": line.strip()[1:].strip(),
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"source": "daily_log"
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})
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# Look for preferences/decisions
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pref_pattern = r"(?:###?\s*Preferences?|###?\s*Decisions?|###?\s*Rules?)(.*?)(?=##?|$)"
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pref_match = re.search(pref_pattern, content, re.DOTALL | re.IGNORECASE)
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if pref_match:
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pref_section = pref_match.group(1)
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for line in pref_section.split('\n'):
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if line.strip().startswith('-') or line.strip().startswith('*'):
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key_memories.append({
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"type": "preference",
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"content": line.strip()[1:].strip(),
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"source": "daily_log"
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})
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return key_memories
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def update_memory_md(new_memories):
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"""Update MEMORY.md with new consolidated memories"""
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today = datetime.now().strftime("%Y-%m-%d")
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# Read current MEMORY.md
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if os.path.exists(MEMORY_FILE):
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with open(MEMORY_FILE, 'r') as f:
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content = f.read()
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else:
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content = "# MEMORY.md — Long-Term Memory\n\n*Curated memories. The distilled essence, not raw logs.*\n"
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# Check if we need to add a new section
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consolidation_header = f"\n\n## Consolidated Memories - {today}\n\n"
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if consolidation_header.strip() not in content:
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content += consolidation_header
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for memory in new_memories:
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emoji = "📚" if memory["type"] == "lesson" else "⚙️"
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content += f"- {emoji} [{memory['type'].title()}] {memory['content']}\n"
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# Write back
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with open(MEMORY_FILE, 'w') as f:
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f.write(content)
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return len(new_memories)
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return 0
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def archive_old_logs(keep_days=30):
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"""Archive daily logs older than N days"""
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archived = 0
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cutoff = datetime.now() - timedelta(days=keep_days)
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for file in Path(MEMORY_DIR).glob("*.md"):
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match = re.match(r"(\d{4}-\d{2}-\d{2})\.md", file.name)
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if match:
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file_date = datetime.strptime(match.group(1), "%Y-%m-%d")
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if file_date < cutoff:
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# Could move to archive folder
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# For now, just count
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archived += 1
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return archived
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def weekly_consolidation():
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"""Weekly: Extract key memories from last 7 days"""
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print("📅 Weekly Memory Consolidation")
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print("=" * 40)
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logs = get_recent_daily_logs(7)
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all_memories = []
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for file_date, log_file in logs:
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print(f"Processing {log_file.name}...")
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with open(log_file, 'r') as f:
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content = f.read()
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memories = extract_key_memories(content)
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all_memories.extend(memories)
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print(f" Found {len(memories)} key memories")
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if all_memories:
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count = update_memory_md(all_memories)
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print(f"\n✅ Consolidated {count} memories to MEMORY.md")
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else:
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print("\nℹ️ No new key memories to consolidate")
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return len(all_memories)
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def monthly_cleanup():
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"""Monthly: Archive old logs, update MEMORY.md index"""
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print("📆 Monthly Memory Cleanup")
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print("=" * 40)
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# Archive logs older than 30 days
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archived = archive_old_logs(30)
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print(f"Found {archived} old log files to archive")
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# Compact MEMORY.md if it's getting too long
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if os.path.exists(MEMORY_FILE):
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with open(MEMORY_FILE, 'r') as f:
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lines = f.readlines()
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if len(lines) > 500: # If more than 500 lines
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print("⚠️ MEMORY.md is getting long - consider manual review")
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print("\n✅ Monthly cleanup complete")
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return archived
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def search_qdrant_for_context():
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"""Search Qdrant for high-value memories to add to MEMORY.md"""
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cmd = [
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"python3", f"{WORKSPACE}/skills/qdrant-memory/scripts/search_memories.py",
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"important preferences rules",
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"--limit", "10",
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"--json"
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]
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result = subprocess.run(cmd, capture_output=True, text=True)
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if result.returncode == 0:
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try:
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memories = json.loads(result.stdout)
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# Filter for high importance
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high_importance = [m for m in memories if m.get("importance") == "high"]
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return high_importance
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except:
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return []
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return []
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Memory consolidation")
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parser.add_argument("action", choices=["weekly", "monthly", "status"])
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args = parser.parse_args()
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if args.action == "weekly":
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count = weekly_consolidation()
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sys.exit(0 if count >= 0 else 1)
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elif args.action == "monthly":
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archived = monthly_cleanup()
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# Also do weekly tasks
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weekly_consolidation()
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sys.exit(0)
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elif args.action == "status":
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logs = get_recent_daily_logs(30)
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print(f"📊 Memory Status")
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print(f" Daily logs (last 30 days): {len(logs)}")
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if os.path.exists(MEMORY_FILE):
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with open(MEMORY_FILE, 'r') as f:
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lines = len(f.readlines())
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print(f" MEMORY.md lines: {lines}")
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print(f" Memory directory: {MEMORY_DIR}")
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