A powerful memory management system powered by ReMe that provides persistent cross-session memory, automatic user preference application, and intelligent context compression for OpenClaw.

Memory-reme - ReMe Memory Management

A memory management system powered by ReMe that provides persistent cross-session memory, automatic user preference application, and intelligent context compression.

When to Use This Skill

Activate this skill when:

Core Concepts

Three-Level Memory

1. Long-term Memory (MEMORY.md)

2. Daily Memory (memory/YYYY-MM-DD.md)

3. In-Memory Context

Memory Types

| Type | Purpose | Example | |-------|---------|----------| | Personal | User preferences, habits | "Prefer concise code", "Always send files" | | Task | Execution experience, patterns | "Python scripts should include error handling" | | Tool | Tool usage experience | "web_fetch needs timeout 30s for this site" |

Quick Start

Installation (One-time setup)

pip install reme-ai

Session Initialization

At the start of EVERY session:

1. Initialize ReMe 2. Retrieve user preferences 3. Apply to current context

# Initialize
from reme.reme_light import ReMeLight
reme = ReMeLight(working_dir=".reme", language="zh")
await reme.start()
Retrieve preferences
prefs = await reme.memory_search(
    query="用户偏好 文件发送",
    max_results=5
)
Apply
if prefs and "必须发送" in prefs[0]['content']:
    auto_send_files = True

Workflow

Phase 1: Session Start (0-5s)

┌─────────────────────────────────┐
│  1. Initialize ReMe            │
│  2. Load MEMORY.md            │
│  3. Search for user prefs     │
│  4. Apply to current context   │
└─────────────────────────────────┘

Action:

python3 C:\path\to\memory-reme\scripts\init_reme.py

Expected Output:

✓ ReMe initialized
📖 Retrieved 3 preferences
  - User prefers concise code
  - Files must be sent automatically
  - Prefer markdown over plain text
✓ Preferences applied

Phase 2: During Session

Check before actions:

1. Before generating files: - Search for file handling preferences - Apply formatting preferences

2. Before using tools: - Search for tool-specific preferences - Apply timeout/retry settings

3. User feedback: - Extract new rules - Add to MEMORY.md

Example:

User: "你怎么总是忘记发送文件？记住，生成文件后必须直接发送！"

Action:

# Learn from feedback
await reme.add_memory(
    memory_content="用户偏好：生成文件后必须使用message工具直接发送文件，不接受链接地址。原因：用户需要直观可见的内容。",
    user_name="阿伟",
    memory_type="personal"
)

Phase 3: Session End

┌─────────────────────────────────┐
│  1. Extract key events        │
│  2. Generate summary          │
│  3. Write to memory/          │
│  4. Update MEMORY.md         │
│  5. Cleanup tool results       │
│  6. Close ReMe               │
└─────────────────────────────────┘

Action:

python3 C:\path\to\memory-reme\scripts\save_summary.py

Output:

💾 Summary saved to memory/2026-03-06.md
✓ MEMORY.md updated
✓ Tool results cleaned
✓ ReMe closed

Common Use Cases

Use Case 1: File Generation

Trigger: User requests a file to be created

Workflow: 1. Check for file preferences 2. Generate file with correct format 3. Send automatically if required 4. Learn if user corrects

Example:

📖 Retrieved: "Send files automatically"
User: 生成AI日报
✓ Generated: AI日报_2026-03-06.md
📤 Sending file...
✓ Sent successfully

Use Case 2: Code Style Preferences

Trigger: User asks to write code

Workflow: 1. Search for style preferences 2. Apply conventions 3. Format accordingly

Example:

📖 Retrieved: "Prefer concise, well-commented code"
User: 写个Python函数
✓ Applied: Concise style with docstrings

Use Case 3: Preventing Repeated Mistakes

Trigger: User corrects your behavior

Workflow: 1. Accept feedback 2. Extract rule 3. Add to memory 4. Verify next time

Example:

User: Why do you keep forgetting to send files?
🧠 Learning...
✓ Rule recorded: "Always send files automatically"
✓ Will apply next time

Use Case 4: Context Overflow

Trigger: Conversation approaches 70% of token limit

Workflow: 1. ReMe automatically triggers 2. Compresses history to summary 3. Keeps critical information 4. Continues conversation

Automatic - no action needed.

Search Patterns

Common Search Queries

| Goal | Query | |-------|--------| | File preferences | "文件发送 偏好 自动发送" | | Code style | "代码风格 简洁 注释" | | Tool settings | "工具 超时 重试" | | User habits | "用户习惯 偏好" | | Past errors | "错误 避免 重复" |

Search Results Processing

Always: 1. Review returned memories 2. Filter by relevance and recency 3. Apply to current context 4. Document what was applied

Example:

results = await reme.memory_search(query="文件发送 偏好", max_results=3)
for i, result in enumerate(results, 1):
    print(f"{i}. {result['content']}")
    if "必须发送" in result['content']:
        self.auto_send_files = True
print(f"✓ Applied: auto_send_files = {self.auto_send_files}")

Memory File Structure

MEMORY.md

# MEMORY.md - Long-term Memory
User Profile
Name: 阿伟
Role: 90后程序员、AI博主
Preferences
File Handling
Rule: 生成文件后必须使用message工具直接发送
Reason: 用户需要直观可见的内容
Status: Active
Learned: 2026-03-06
Code Style
Rule: 代码要简洁，有注释
Reason: 便于维护和理解
Status: Active
Learned: 2026-03-05
Tool Usage
web_fetch
Timeout: 30s
Retry: 3 times
Reason: 某些网站响应慢
browser
Timeout: 60s
Wait time: 3s for page load
Reason: 确保页面完全加载

memory/YYYY-MM-DD.md

# 2026-03-06 Session Summary
Session 1 - AI News Aggregation
User Request
"给我今天的AI资讯"
Processing
Scraped 8 sources
Filtered 20+ articles
Selected 14 items
Output
File: AI日报_2026-03-06.md
Size: 3611 bytes
Sent: ✓
User Feedback
"你怎么总是忘记发送文件？记住，生成文件后必须直接发送！"
Learning
✓ New rule: Auto-send files
✓ Updated MEMORY.md
Session 2 - ReMe Integration
User Request
"接入ReMe后工作流程是怎样的"
Processing
Analyzed ReMe documentation
Designed workflow
Created integration plan
Output
File: ReMe工作流程设计.md
File: ReMe存在形式与影响.md
Sent: ✓
No User Feedback
Learning
No new rules

Best Practices

1. Always Start Sessions with Memory Retrieval

Bad:

# Start without memory
user_request = get_user_input()
process_request(user_request)

Good:

# Start with memory
reme = await init_reme()
prefs = await reme.memory_search(query="用户偏好")
apply_preferences(prefs)
user_request = get_user_input()
process_request(user_request)

2. Learn from Every Correction

When user says "You forgot X": 1. Acknowledge immediately 2. Extract the rule 3. Add to memory 4. Verify application

Example:

User: 你总是忘记发送文件！
Me: ✓ 已记住：生成文件后必须发送文件
   正在添加到 MEMORY.md...
Next file generation:
✓ File created
📤 Auto-sending...
✓ Sent

3. Be Specific in Memory Records

Bad:

- User prefers good code

Good:

- User prefers concise, well-commented Python code
  - Use docstrings for functions
  - Maximum 3 levels of nesting
  - Prefer list comprehensions over loops

4. Update Memory Regularly

Daily tasks:

Weekly tasks:

5. Use Semantic Search Effectively

Bad queries:

Good queries:

Why: Specific queries return more relevant results.

Troubleshooting

Problem: Memory Not Retrieved

Symptoms:

Solutions: 1. Check if ReMe is initialized 2. Verify search query matches stored content 3. Check MEMORY.md exists and is not empty 4. Try broader search terms

# Debug search
results = await reme.memory_search(query="文件")
print(f"Found {len(results)} results")
for r in results:
    print(f"  - {r['content'][:50]}...")

Problem: Old Information Used

Symptoms:

Solutions: 1. Add timestamp to memory entries 2. Sort results by time_created (reverse) 3. Manually update outdated entries in MEMORY.md 4. Consider expiration for time-sensitive rules

Problem: Memory File Too Large

Symptoms:

Solutions: 1. Archive old entries to memory/archive/ 2. Merge similar preferences 3. Remove redundant info 4. Use categories to organize

Integration with Existing Skills

Combining with docx skill

Workflow:

1. Search memory for docx preferences
2. Apply formatting rules
3. Generate document with docx skill
4. Check if auto-send required
5. Send if needed

Combining with coding-agent skill

Workflow:

1. Search memory for coding preferences
2. Apply style conventions
3. Generate code with coding-agent
4. Check for auto-review rules
5. Review if needed

Performance Considerations

Time Overhead

| Operation | Time | Impact | |-----------|-------|---------| | Session start | ~500ms | Negligible | | Memory search | ~200ms | Negligible | | File operations | ~100ms | Negligible | | Summary generation | ~300ms | Negligible | | Total per session | ~1s | Minimal |

Space Usage

.reme/
├── MEMORY.md          ~10KB
├── memory/           ~150KB (30 days)
├── tool_result/       ~5MB (auto-cleanup)
└── .embeddings/       ~1MB
Total: ~6MB (1 month)

Advanced Features

Conditional Application

Only apply when relevant:

prefs = await reme.memory_search(query="文件发送")
if file_generated and prefs:
    # Apply file preferences
    if "必须发送" in prefs[0]['content']:
        await send_file(file_path)

Context-Aware Retrieval

Consider current task:

if task_type == "coding":
    query = "代码风格 Python"
elif task_type == "writing":
    query = "写作风格 简洁"
elif task_type == "file_generation":
    query = "文件发送 偏好"

Memory Cleanup

Automatic cleanup:

Manual cleanup:

# Archive old sessions
mv memory/2026-01-*.md memory/archive/
Compress large files
gzip MEMORY.md

See Also

Summary

This skill enables persistent memory, automatic preference application, and intelligent context management. Use it to:

Key principle: Memory is only useful when it's retrieved and applied. Always start sessions with memory retrieval, and verify application throughout the conversation.

Installation (One-time setup)

pip install reme-ai

Session Initialization

At the start of EVERY session:

1. Initialize ReMe 2. Retrieve user preferences 3. Apply to current context

# Initialize
from reme.reme_light import ReMeLight
reme = ReMeLight(working_dir=".reme", language="zh")
await reme.start()
Retrieve preferences
prefs = await reme.memory_search(
    query="用户偏好 文件发送",
    max_results=5
)
Apply
if prefs and "必须发送" in prefs[0]['content']:
    auto_send_files = True

1. Always Start Sessions with Memory Retrieval

Bad:

# Start without memory
user_request = get_user_input()
process_request(user_request)

Good:

# Start with memory
reme = await init_reme()
prefs = await reme.memory_search(query="用户偏好")
apply_preferences(prefs)
user_request = get_user_input()
process_request(user_request)

2. Learn from Every Correction

When user says "You forgot X": 1. Acknowledge immediately 2. Extract the rule 3. Add to memory 4. Verify application

Example:

User: 你总是忘记发送文件！
Me: ✓ 已记住：生成文件后必须发送文件
   正在添加到 MEMORY.md...
Next file generation:
✓ File created
📤 Auto-sending...
✓ Sent

3. Be Specific in Memory Records

Bad:

- User prefers good code

Good:

- User prefers concise, well-commented Python code
  - Use docstrings for functions
  - Maximum 3 levels of nesting
  - Prefer list comprehensions over loops

4. Update Memory Regularly

Daily tasks:

Weekly tasks:

5. Use Semantic Search Effectively

Bad queries:

Good queries:

Why: Specific queries return more relevant results.