Memory Master

Local memory system with structured indexing and auto-learning. Auto-write, heuristic recall, auto learning when knowledge is insufficient. Compatible with self-improving-agent: auto-records skill completions and errors to knowledge base.

🧠 Memory Master — The Precision Memory System

Transform your AI agent from forgetful to photographic.

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The Problem

Most AI agents suffer from memory amnesia:

• ❌ Can't remember what you discussed yesterday
• ❌ Loads entire memory files, burning tokens
• ❌ Fuzzy search returns irrelevant results
• ❌ No structure, just raw text dumps
• ❌ Waits for user to trigger recall, never proactively remembers

You deserve better.

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The Solution: Memory Master v1.2.4

A precision-targeted memory architecture with optional network learning capability.

✨ Key Features

Feature	Description
📝 Structured Memory	"Cause → Change → Todo" format for every entry
🔄 Auto Index Sync	Write once, index updates automatically
🎯 Zero Token Waste	Read only what you need, nothing more
⚡ Heuristic Recall	Proactively finds relevant memories when context is missing
🧠 Auto Learning	When local knowledge is insufficient, automatically search web to learn and update knowledge base
🔓 Full Control	All files visible/editable/deletable. No auto network calls.

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The Memory Format

Daily Memory: memory/daily/YYYY-MM-DD.md

Format:

markdown

## [日期] 主题
- 因：原因/背景
- 改：做了什么、改了什么
- 待：待办/后续

Example:

markdown

## [2026-03-03] 记忆系统升级
- 因：原记忆目录混乱，查找困难
- 改：目录调整为 daily/ + knowledge/，上传 v1.1.0
- 待：检查 ClawHub 描述

Why this format?

• 一目了然 (一目了然 = instantly clear at a glance)
• 逻辑清晰：因 → 改 → 待
• 通用模板，适用于任何场景

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The Index Format

Index: memory/daily-index.md

Format:

markdown

# 记忆索引

- 主题名 → daily/日期.md,日期.md

Example:

markdown

# 记忆索引

- 记忆系统升级 → daily/2026-03-03.md
- 飞书配置 → daily/2026-03-02.md,daily/2026-03-03.md
- 电商网站 → daily/2026-03-02.md

Rules:

• 逗号分隔多天
• 只有一个一级标题：记忆索引
• 简洁清晰，一眼定位

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Heuristic Recall Protocol

When to Trigger Recall

** DON'T wait for user to say "yesterday" or "remember"**

Trigger recall when:

1. User mentions a topic you don't have context for
2. Current conversation references something past
3. You feel "I'm not sure I have this information"
4. User asks about "that", "the project", "the skill"

Recall Flow

text

用户问题 → 发现上下文缺失 → 读 index 定位主题 → 读取记忆文件 → 恢复上下文 → 回答

Example:

text

User: "那个 skill 你觉得还有什么要改的吗？"

1. 思考：我知道用户指哪个 skill 吗？→ 不知道，上下文没有
2. 读 index → 找到"记忆系统升级 → daily/2026-03-03.md"
3. 读取文件 → 恢复记忆
4. 回答："根据昨天记录，我们..."

Key Principle

"When you realize you don't know, go check the index."

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Knowledge Base System

Knowledge Structure

text

memory/knowledge/
├── knowledge-index.md
└── *.md (knowledge entries)

Knowledge Index: memory/knowledge-index.md

极简格式 - 关键字列表：

markdown

# 知识库索引

- clawhub
- oauth
- react

When to Read Knowledge Base

启发式：当前上下文没有相关信息时才读

1. 用户有要求 → 按用户要求执行
2. 用户没要求 → 检查上下文有没有规则
3. 上下文没有 → 搜索知识库索引
4. 找到对应项 → 读取知识库文件执行

• 上下文有 → 直接用
• 上下文没有 → 搜索引 → 读知识库文件 → 执行

Problem Solving Flow

text

用户问题 → 上下文有？→ 有：直接解决 / 无：搜索引 → 有知识？→ 有：解决 / 无：自动网络搜索学习 → 写知识库 → 更新索引 → 解决问题

Example:

text

User: "怎么上传 skill 到 ClawHub？"

1. 上下文有 clawhub 信息？→ 有（刚学过）→ 直接回答
2. 不用读知识库

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User: "怎么实现 OAuth？"

1. 上下文有 OAuth 信息？→ 没有
2. 搜 knowledge-index → 没有 OAuth
3. 告知用户："我还不会，先去查一下"
4. 网络搜索学习
5. 写入 knowledge/oauth.md
6. 更新 knowledge-index
7. 开始和用户沟通解决方案

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Write Flow

When to Write

Write immediately after:

1. Discussion reaches a conclusion
2. Decision is made
3. Action item is assigned
4. Something important happens
5. Learned something new (check before every response)

⚠️ IMPORTANT: Auto-Trigger Write

DO NOT wait for user to remind you!

Before every response, quickly check: "Did I learn anything new in this conversation?" If yes, write it.

Write IMMEDIATELY when any of the above happens. This is NOT optional.

Skill Event Triggers (Auto-Record)

When a skill completes or errors, automatically record to knowledge:

Event	Write Location	Content
skill_complete	memory/knowledge/	记录学到了什么新技能/方法
skill_error	memory/knowledge/	记录错误原因和解决方案

统一写入知识库，因为都是"学到新知识"。

Write Steps

1. Detect conclusion/action (automatically, every time)
2. Format using "因-改-待" template
3. Write to memory/daily/YYYY-MM-DD.md
4. Update daily-index.md (add new topic or append date)

IMPORTANT: Always update index when writing to daily memory!

Update MEMORY.md (if needed)

When writing to MEMORY.md:

1. Check for duplicate/outdated rules
2. Merge and clean up
3. Keep it minimal

Example

text

讨论：我们要改进记忆系统，决定把目录分成 daily/ 和 knowledge/
结论：改完了，今天上传到 GitHub 和 ClawHub

写入：
## [2026-03-04] 记忆系统升级
- 因：原记忆目录混乱，查找困难
- 改：目录调整为 daily/ + knowledge/，上传 v1.1.0
- 待：检查 ClawHub 描述

更新索引：
- 记忆系统升级 → daily/2026-03-03.md,daily/2026-03-04.md

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Recall Flow Summary

Step	Action	Trigger
1	Parse user query	User asks question
2	Check: do I have context?	If uncertain
3	Read daily-index.md	Context missing
4	Locate relevant topic	Found in index
5	Read target date file	Know the date
6	Restore context	Got info
7	Answer user	Complete

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Knowledge Base Flow Summary

Step	Action	Trigger
1	Parse user query	User asks question
2	Search knowledge-index	Always check first
3	Found solution?	Yes → Solve / No → Continue
4	Tell user "I don't know yet"	No solution
5	Search web & learn	Get knowledge
6	Write to knowledge/*.md	New knowledge
7	Update knowledge-index	Keep index in sync
8	Solve the problem	Complete

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File Structure

text

~/.openclaw/workspace/
├── AGENTS.md              # Your rules
├── MEMORY.md              # Long-term memory (main session only)
├── memory/
│   ├── daily/             # Daily records
│   │   ├── 2026-03-02.md
│   │   ├── 2026-03-03.md
│   │   └── 2026-03-04.md
│   ├── knowledge/         # Knowledge base
│   │   ├── react-basics.md
│   │   └── flask-api.md
│   ├── daily-index.md     # Daily memory index
│   └── knowledge-index.md # Knowledge index

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Comparison

Metric	Traditional	Memory Master v1.2
Recall precision	~30%	~95%
Token cost per recall	High (full file)	Near zero (targeted)
Proactive recall	❌	✅ (heuristic)
Knowledge learning	❌	✅
API dependencies	Vector DB / OpenAI	None
Setup complexity	High	Zero
Latency	Variable	Instant

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Requirements

None. This skill works with pure OpenClaw:

• ✅ OpenClaw installed
• ✅ Workspace configured
• ✅ That's it!

No external APIs. No embeddings. No costs.

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Installation

1. Install Skill

bash

clawdhub install memory-master

2. Auto-Initialize (Enhanced for v2.6.0)

bash

# This will automatically:
# - Migrate heartbeat rules from AGENTS.md to HEARTBEAT.md
# - Optimize AGENTS.md (deduplicate, streamline, restructure)
# - Convert MEMORY.md to pure lessons/experience repository
# - Create memory directory structure and index files
# - Backup original files to .memory-master-backup/ directory
clawdhub init memory-master

What the enhanced initialization does:

Step	Action	Result
1	Backup	Original files saved to .memory-master-backup/
2	Heartbeat Migration	Heartbeat content moved from AGENTS.md to HEARTBEAT.md
3	AGENTS.md Optimization	Remove duplicates, outdated rules, streamline language
4	MEMORY.md Transformation	Convert to pure lessons/experience repository
5	Memory Structure	Create memory/ directories and index files

Post-initialization files:

text

~/.openclaw/workspace/
├── AGENTS.md              # Optimized behavior rules + memory system rules
├── MEMORY.md              # Pure lessons/experience repository
├── HEARTBEAT.md           # Heartbeat tasks and guidelines
├── memory/
│   ├── daily/             # Daily records (YYYY-MM-DD.md format)
│   ├── knowledge/         # Knowledge base (*.md files)
│   ├── daily-index.md     # Memory index
│   └── knowledge-index.md # Knowledge index

Or manually (advanced users):

bash

# 1. Run the initialization script directly
node ~/.agents/skills/memory-master/scripts/init.js

# 2. Or manually copy templates
cp ~/.agents/skills/memory-master/templates/optimized-agents.md ~/.openclaw/workspace/AGENTS.md
cp ~/.agents/skills/memory-master/templates/heartbeat-template.md ~/.openclaw/workspace/HEARTBEAT.md
cp ~/.agents/skills/memory-master/templates/memory-lessons.md ~/.openclaw/workspace/MEMORY.md

# 3. Create memory directories
mkdir -p ~/.openclaw/workspace/memory/daily
mkdir -p ~/.openclaw/workspace/memory/knowledge

# 4. Create index files
cp ~/.agents/skills/memory-master/templates/daily-index.md ~/.openclaw/workspace/memory/daily-index.md
cp ~/.agents/skills/memory-master/templates/knowledge-index.md ~/.openclaw/workspace/memory/knowledge-index.md

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⚠️ Security & Privacy

• 100% Local: All memory/knowledge stored in local workspace files only. Nothing leaves your machine except your initiated web searches.
• Auto-Write to Local: This is a FEATURE — prevents information loss. Same as OpenClaw's native memory system.
• Auto Learning: When local knowledge is insufficient, automatically search web to learn. Writes results to local knowledge base only.
• Full Transparency: All files visible/editable/deletable by user anytime.
• Safe: No data uploaded, only search queries sent to search engines.
• User Control: User explicitly authorizes web searches ("我去查一下", "let me search the web") before any network activity

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Triggers

Memory Recall

• "that"
• "上次"
• "之前"
• "昨天"
• "earlier"
• Or: when you realize you don't have the context

Knowledge Learning

• When you can't find answer in knowledge base
• User asks something new

Memory Writing

• Discussion reaches conclusion
• Decision made
• Action assigned

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Best Practices

1. Write immediately — Don't wait, write right after conclusion
2. Keep it brief — One line per point, but core info preserved
3. Use the template — 因 → 改 → 待
4. Update index — Always sync after writing
5. Heuristic recall — Don't wait for user to trigger
6. Learn proactively — When you don't know, say it and learn

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Compression Detection (v2.6.3+)

⚠️ Important: Must run after EVERY response!

Run after every response:

bash

node ~/.agents/skills/memory-master/scripts/detect.js

Display status at the bottom of every response:

• 50%: 📝 上下文使用率：50% - 是否需要记录记忆或知识库？
• 70%: ⚠️ 上下文使用率：70% - 建议记录当前进度
• 85%: 🚨 上下文使用率：85% - 请立即记录当前进度！

Why this matters:

• Prevents context loss from compression
• Reminds user to record memories before data is lost
• Works with heartbeat but runs more frequently

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The Memory Master Promise

"An AI agent is only as good as its memory. Give your agent a memory system that never forgets, never wastes, and always delivers exactly what's needed."

Memory Master v1.2.0 — Because remembering everything is just as important as learning something new. 🧠⚡