Memory Optimization

Memory Optimization Skill

Quickly implement a comprehensive memory management system for AI agents based on Moltbook community best practices.

When to Use This Skill

What This Skill Provides

1. TL;DR Summary System - 30-second context recovery 2. Three-File Pattern - Structured project tracking 3. Fixed Tags System - Quick grep search capability 4. Daily Cleanup Script - 3-minute automated maintenance 5. HEARTBEAT Integration - Mandatory memory checklist 6. Rolling Summary Template - Concise daily summaries 7. Testing Framework - 6 automated tests 8. Knowledge Graph - 18 entities, 15 relationships 9. Skill Usage Tracker - Track and analyze skill usage patterns

Quick Start

TL;DR Summary System

Add to each daily log (memory/YYYY-MM-DD.md):

## ⚡ TL;DR Summary
Core Achievements:
✅ Achievement 1
✅ Achievement 2
Today's Key Points:
Key point 1
Key point 2
Decisions: Important decision made today

Three-File Pattern

For complex projects, create:

Fixed Tags

Use consistent tags across files:

Daily Cleanup

Run automated cleanup:

./memory/daily-cleanup.sh

HEARTBEAT Integration

Add to HEARTBEAT.md:

### 🧠 Memory Management Checklist
Every Session Start:
[ ] Read SOUL.md (agent identity)
[ ] Read USER.md (user preferences)
[ ] Read memory/YYYY-MM-DD.md (today + yesterday)
[ ] Read MEMORY.md (long-term memory)

Scripts

See scripts/README.md for detailed usage:

Phase 6: Value-Aware Retrieval

KG entities can be retrieved by value score (weighted combination of reliability, strength, significance, preference match, recency):

# Retrieve entities sorted by value score
python3 scripts/memory_ontology.py retrieve --types Decision Finding --min-score 0.4
Query by text and filter by score
python3 scripts/memory_ontology.py retrieve --query "architecture" --show-scores
CLI help
python3 scripts/memory_ontology.py retrieve --help

References

See reference files for detailed guidance:

Key Metrics

| Metric | Before | After | Improvement | |--------|--------|-------|-------------| | Context Recovery | 5-10 min | 30 sec | -98% | | File Size | 2000+ tokens | 1.3KB | -99% | | Automation | Manual | 3-min script | +100% | | Tests | None | 6/6 pass | +100% |

Key Insights from Moltbook

> "Forget is a survival mechanism" - Compression forces distillation of experience into most resilient forms

> "Knowledge graph is an index for your brain" - Query efficiency 10x better than grep

> "Record immediately, not wait" - Details fade quickly

> "Focus on why, not what" - Rationale is more important than the fact

File Structure

memory/
├── YYYY-MM-DD.md          # Daily log with TL;DR
├── task_plan.md            # Task planning
├── findings.md             # Research findings
├── progress.md             # Progress tracking
├── rolling-summary-template.md
├── daily-cleanup.sh
├── test-memory-system.sh
└── ontology/
    ├── memory-schema.yaml
    ├── entity-templates.md
    ├── INTEGRATION.md
    └── graph.jsonl
scripts/
└── memory_ontology.py

Skill Usage Tracker

Track and analyze skill usage patterns via Knowledge Graph:

# Record a skill usage
python3 scripts/skill_tracker.py record --skill coding-agent --status success --duration 2.5
View usage statistics
python3 scripts/skill_tracker.py stats
Scan session history and save to KG
python3 scripts/skill_tracker.py scan --save

Features:

Usage Examples

Create New Daily Log with TL;DR

# Daily Memory - 2026-03-13
⚡ TL;DR Summary
Core Achievements:
✅ Completed task 1
✅ Completed task 2
Today's Key Points:
Working on project X
Found solution Y
Decisions: Chose approach Z

Use Knowledge Graph

# Create a decision entity
python3 scripts/memory_ontology.py create --type Decision --props '{"title":"...","rationale":"...","made_at":"...","confidence":0.9,"tags":["#decision"]}'
Query by tags
python3 scripts/memory_ontology.py query --tags "#memory" "#decision"
Get related entities
python3 scripts/memory_ontology.py related --id dec_xxx

Environment Variables

# GLM API configuration (used by kg_extractor.py)
export OPENAI_API_KEY="your-glm-token"
export OPENAI_BASE_URL="https://open.bigmodel.cn/api/paas/v4"
export OPENAI_MODEL="glm-5"
Global KG path (optional, defaults to ~/.openclaw/workspace/memory/ontology)
Configure via KG_DIR environment variable

KG Sharing Across Agents

Multi-agent setups can share a single Knowledge Graph for collaborative memory.

Setup

# 1. Create shared directory
mkdir -p ~/.openclaw/shared-kg
2. Create symlink to main KG
ln -sf ~/.openclaw/workspace/memory/ontology/graph.jsonl ~/.openclaw/shared-kg/main-kg.jsonl

Usage by Agents

Each agent should reference the shared KG in their TOOLS.md:

## Knowledge Graph (KG)
共享 KG: ~/.openclaw/shared-kg/main-kg.jsonl
包含所有重要的长期记忆、决策、经验教训

Script Usage with Shared KG

# Direct KG path
KG_DIR=~/.openclaw/shared-kg python3 scripts/memory_ontology.py query --tags "#decision"
Or set in .env
KG_DIR=~/.openclaw/shared-kg/

Benefits:

OpenClaw Skill Invocation

When the user types /xmo, OpenClaw automatically invokes this memory-optimization skill.

Add the following to OpenClaw's settings.json or skills.json:

{
  "skills": {
    "xmo": {
      "path": "./memory-optimization",
      "description": "Memory optimization skill for AI agents"
    }
  }
}

After configuration, the user can activate this skill by typing /xmo.

Next Steps

1. Run test script: ./memory/test-memory-system.sh 2. Verify TL;DR exists in today's log 3. Start using KG for important decisions 4. Run daily cleanup each day

For complete implementation details, see references/implementation.md.

TL;DR Summary System

Add to each daily log (memory/YYYY-MM-DD.md):

```markdown

# 1. Create shared directory
mkdir -p ~/.openclaw/shared-kg
2. Create symlink to main KG
ln -sf ~/.openclaw/workspace/memory/ontology/graph.jsonl ~/.openclaw/shared-kg/main-kg.jsonl

Usage by Agents

Each agent should reference the shared KG in their TOOLS.md:

```markdown