/install claude-memory-optimizer
Claude Memory Optimizer
Structured memory system for OpenClaw with 4-type classification and automated migration.
When to Use
- Setting up memory for the first time in OpenClaw
- Migrating from unstructured
memory/*.mdto organized categories - Improving memory recall with semantic frontmatter
- Implementing Claude Code-style memory architecture
Features
- 4-Type Classification: user, feedback, project, reference
- Frontmatter Metadata: structured name/description/type for semantic search
- Auto-Migration: one-command refactor of existing memory files
- Log Mode: optional append-only daily logs (KAIROS style)
Quick Start
Install
clawhub install claude-memory-optimizer
Run Migration
# Auto-detect workspace
node ~/.openclaw/skills/claude-memory-optimizer/scripts/refactor-memory.js
# Or specify explicitly
node ~/.openclaw/skills/claude-memory-optimizer/scripts/refactor-memory.js ~/.openclaw/workspace
Verify
ls -la ~/.openclaw/workspace/memory/
cat ~/.openclaw/workspace/MEMORY.md
Memory Types
| Type | Purpose | Example |
|---|---|---|
| user | User role, preferences, skills | "Data scientist, prefers concise replies" |
| feedback | Behavior corrections/confirmations | "No trailing summaries — user can read diffs" |
| project | Project context, decisions, deadlines | "Thesis deadline: 2026-06-01" |
| reference | External system pointers | "Kaggle: https://kaggle.com/chenziong" |
Directory Structure
memory/
├── user/ # User information
├── feedback/ # Behavior guidance
├── project/ # Project context
├── reference/ # External references
└── logs/ # Append-only logs (optional)
└── YYYY/
└── MM/
└── YYYY-MM-DD.md
Memory File Format
Each memory file uses frontmatter metadata:
---
name: Data Science Background
description: User is a data scientist focused on observability and LLMs
type: user
---
User studies at Beijing University of Technology & UCD, GPA 3.95/4.2.
Research: LLM, AI Agents, MCP.
**Skills:** Python, PyTorch, Transformers, NLP
**How to apply:** Use data science terminology, assume ML background.
What NOT to Save
- Code patterns, architecture, file paths (derivable from codebase)
- Git history, recent changes (use
git log) - Debugging solutions (fix is in the code)
- Content already in CLAUDE.md
- Ephemeral task details (only useful in current session)
Configuration
OpenClaw Config
{
"agents": {
"defaults": {
"memorySearch": {
"enabled": true,
"provider": "local",
"maxResults": 20,
"minScore": 0.3
},
"compaction": {
"memoryFlush": {
"enabled": true,
"softThresholdTokens": 4000
}
}
}
}
}
Usage Examples
Save User Preference
User: "Remember, I prefer concise replies without trailing summaries."
AI: Saves to memory/feedback/reply-style.md:
---
name: Reply Style Preference
description: User wants concise replies, no trailing summaries
type: feedback
---
**Rule:** Keep replies concise, no trailing summaries.
**Why:** User said "I can read the diff myself."
**How to apply:** End responses directly after completing work.
Retrieve Memory
User: "What did I say about database testing?"
AI: Runs memory_search query="database testing" → returns memory/feedback/db-testing.md
Verify Memory
User: "Is the experiment design in memory/project/dong-thesis.md still current?"
AI: Runs grep to verify → detects outdated info → updates memory file.
Migration Guide
Before
memory/
├── 2026-03-21.md
├── 2026-03-28.md
├── research-memory.md
└── video-memory.md
After
memory/
├── project/
│ ├── 2026-03-21-.md
│ ├── 2026-03-28-.md
│ └── research-memory.md
├── reference/
│ └── video-memory.md
└── logs/2026/04/2026-04-02.md
Advanced Features
Semantic Retrieval (Future)
async function findRelevantMemories(query: string, memoryDir: string) {
const memories = await scanMemoryFiles(memoryDir);
const selected = await selectRelevantMemories(query, memories);
return selected.slice(0, 5); // Top 5 relevant memories
}
Verification on Recall (Future)
Before recommending from memory:
- If memory names a file →
lsto verify existence - If memory names a function →
grepto confirm - If memory conflicts with current state → trust current observation, update memory
"Memory says X exists" ≠ "X exists now"
Maintenance
Daily (Heartbeat)
- Append to
memory/YYYY-MM-DD.md - Record decisions, conversations, learnings
Weekly (Review)
- Read daily notes
- Distill important info to
MEMORY.md - Remove outdated entries
Monthly (Audit)
- Review project progress
- Update long-term goals
- Check
.learnings/records
Troubleshooting
Memory Not Loaded
- Ensure
MEMORY.mdexists in workspace root - Check
agents.defaults.memorySearch.enabled = true - Restart OpenClaw gateway
Poor Recall Quality
- Add specific
descriptionin frontmatter - Use consistent keywords
- Adjust
minScore(lower = broader matches)
Migration Fails
- Backup
memory/directory first - Run script with
--dry-run(if available) - Check file permissions
References
- Claude Code:
src/memdir/(memdir.ts, memoryTypes.ts, findRelevantMemories.ts) - OpenClaw Docs:
docs/concepts/memory.md - Related Skills:
memory-setup-openclaw,elite-longterm-memory
License
MIT-0
License
MIT-0
- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install claude-memory-optimizer - 安装完成后,直接呼叫该 Skill 的名称或使用
/claude-memory-optimizer触发 - 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
Claude Memory Optimizer 是什么?
Structured memory system with 4-type classification (user/feedback/project/reference), frontmatter metadata, and automated migration. Based on Claude Code me... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 133 次。
如何安装 Claude Memory Optimizer?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install claude-memory-optimizer」即可一键安装,无需额外配置。
Claude Memory Optimizer 是免费的吗?
是的,Claude Memory Optimizer 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。
Claude Memory Optimizer 支持哪些平台?
Claude Memory Optimizer 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。
谁开发了 Claude Memory Optimizer?
由 陈子昂(@mystour)开发并维护,当前版本 v1.3.0。