什么是 OpenClaw 记忆增强器？

OpenClaw 记忆增强器是专为 Openclaw 技能生态系统设计的复杂记忆层，使 AI 智能体能够在多个会话中保留和找回信息。它实现了一个检索增强生成 (RAG) 系统，可自动处理记忆目录中的文件，为智能体提供在长期项目和个性化用户交互中保持连续性所需的上下文。

该技能的独特之处在于其针对边缘计算的高度优化。它提供了一个专门的边缘 (Edge) 版本，运行零外部依赖，且内存消耗低于 10MB，非常适合 NVIDIA Jetson 或树莓派等设备。通过利用本地存储，它确保所有记忆数据在用户的硬件上保持私密和安全，无需云端向量数据库。

下载入口:https://github.com/openclaw/skills/tree/main/skills/henryfcb/openclaw-memory-enhancer

安装与下载

1. ClawHub CLI

从源直接安装技能的最快方式。

2. 手动安装

将技能文件夹复制到以下位置之一

优先级：工作区 > 本地 > 内置

3. 提示词安装

将此提示词复制到 OpenClaw 即可自动安装。

OpenClaw 记忆增强器 应用场景

• 使 AI 智能体能够在不同的聊天会话中记住用户偏好和过去的决策。
• 从历史对话记录中自动构建本地知识库。
• 为智能体需要跟踪进度的长期技术项目提供深度上下文。
• 根据以往解决问题的交互自动生成 FAQ 或解决方案指南。
• 在传统 RAG 架构过于沉重的资源受限硬件上部署智能体。

1. 记忆编码：系统从文本中提取关键词并将其转换为归一化的哈希向量。
2. 自动加载：扫描指定的记忆目录，将现有的 Markdown 或 JSON 文件摄取到本地向量库中。
3. 语义搜索：收到查询后，系统执行快速关键词预过滤，随后进行余弦相似度计算。
4. 排序与检索：根据可配置的相似度阈值对最相关的记忆块进行排序和检索。
5. 提示词增强：将相关上下文注入 LLM 提示词，使智能体能够根据历史数据提供知情回复。

OpenClaw 记忆增强器 配置指南

要通过 ClawHub 安装，请使用以下命令：

clawhub install openclaw-memory-enhancer

通过 Git 手动安装：

git clone https://github.com/henryfcb/openclaw-memory-enhancer.git ~/.openclaw/skills/openclaw-memory-enhancer

要初始化边缘优化版本并加载记忆：

cd ~/.openclaw/skills/openclaw-memory-enhancer
python3 memory_enhancer_edge.py --load

OpenClaw 记忆增强器 数据架构与分类体系

该技能使用结构化分类在 ~/.openclaw/workspace/knowledge-base/ 目录中组织数据，以提高 Openclaw 技能的检索准确性：

存储在边缘版本中使用轻量级 JSON 格式，在标准版本中使用 NumPy 支持的数组。

name: openclaw-memory-enhancer
description: "Edge-optimized RAG memory system for OpenClaw with semantic search. Automatically loads memory files, provides intelligent recall, and enhances conversations with relevant context. Perfect for Jetson and edge devices (<10MB memory)."
homepage: https://github.com/henryfcb/openclaw-memory-enhancer
metadata:
  openclaw:
    emoji: "??"
    requires:
      bins: ["python3"]
      env: []
    install: []

?? OpenClaw Memory Enhancer

Give OpenClaw long-term memory - remember important information across sessions and automatically recall relevant context for conversations.

Core Capabilities

Quick Reference

When to Use

Use this skill when:

• You want OpenClaw to remember things across sessions
• You need to build a knowledge base from chat history
• You're working on long-term projects that need context
• You want automatic FAQ generation from conversations
• You're running on edge devices with limited memory

Don't use when:

• Simple note-taking apps are sufficient
• You don't need cross-session memory
• You have plenty of memory and want maximum accuracy (use standard version)

Versions

Edge Version ? Recommended

Best for: Jetson, Raspberry Pi, embedded devices

python3 memory_enhancer_edge.py --load

Features:

• Zero dependencies (Python stdlib only)
• Memory usage < 10MB
• Lightweight keyword + vector matching
• Perfect for resource-constrained devices

Standard Version

Best for: Desktop/server, maximum accuracy

pip install sentence-transformers numpy
python3 memory_enhancer.py --load

Features:

• Uses sentence-transformers for high-quality embeddings
• Better semantic understanding
• Memory usage 50-100MB
• Requires model download (~50MB)

Installation

Via ClawHub (Recommended)

clawhub install openclaw-memory-enhancer

Via Git

git clone https://github.com/henryfcb/openclaw-memory-enhancer.git r
  ~/.openclaw/skills/openclaw-memory-enhancer

Usage Examples

Command Line

# Load existing OpenClaw memories
cd ~/.openclaw/skills/openclaw-memory-enhancer
python3 memory_enhancer_edge.py --load

# Search for memories
python3 memory_enhancer_edge.py --search "voice-call plugin setup"

# Add a new memory
python3 memory_enhancer_edge.py --add "User prefers dark mode"

# Show statistics
python3 memory_enhancer_edge.py --stats

# Export to Markdown
python3 memory_enhancer_edge.py --export

Python API

from memory_enhancer_edge import MemoryEnhancerEdge

# Initialize
memory = MemoryEnhancerEdge()

# Load existing memories
memory.load_openclaw_memory()

# Search for relevant memories
results = memory.search_memory("AI trends report", top_k=3)
for r in results:
    print(f"[{r['similarity']:.2f}] {r['content'][:100]}...")

# Recall context for a conversation
context = memory.recall_for_prompt("Help me check billing")
# Returns formatted memory context

# Add new memory
memory.add_memory(
    content="User prefers direct results",
    source="chat",
    memory_type="preference"
)

OpenClaw Integration

# In your OpenClaw agent
from skills.openclaw_memory_enhancer.memory_enhancer_edge import MemoryEnhancerEdge

class EnhancedAgent:
    def __init__(self):
        self.memory = MemoryEnhancerEdge()
        self.memory.load_openclaw_memory()
    
    def process(self, user_input: str) -> str:
        # 1. Recall relevant memories
        memory_context = self.memory.recall_for_prompt(user_input)
        
        # 2. Enhance prompt with context
        enhanced_prompt = f"""
{memory_context}

User: {user_input}
"""
        
        # 3. Call LLM with enhanced context
        response = call_llm(enhanced_prompt)
        
        return response

Memory Types

How It Works

Memory Encoding (Edge Version)

1. Keyword Extraction: Extract important words from text
2. Hash Vector: Map keywords to vector positions
3. Normalization: L2 normalize the vector
4. Storage: Save to local JSON file

Memory Retrieval

1. Query Encoding: Convert query to same vector format
2. Keyword Pre-filter: Fast filter by common keywords
3. Similarity Calculation: Cosine similarity between vectors
4. Ranking: Return top-k most similar memories

Privacy Protection

• All data stored locally in ~/.openclaw/workspace/knowledge-base/
• No network requests
• No external API calls
• No data leaves your device

Technical Specifications

Edge Version

Vector Dimensions: 128
Memory Usage: < 10MB
Dependencies: None (Python stdlib)
Storage Format: JSON
Max Memories: 1000 (configurable)
Query Latency: < 100ms

Standard Version

Vector Dimensions: 384
Memory Usage: 50-100MB
Dependencies: sentence-transformers, numpy
Storage Format: NumPy + JSON
Model Size: ~50MB download
Query Latency: < 50ms

Configuration

Edit these parameters in the code:

self.config = {
    "vector_dim": 128,        # Vector dimensions
    "max_memory_size": 1000,  # Max number of memories
    "chunk_size": 500,        # Content chunk size
    "min_keyword_len": 2,     # Minimum keyword length
}

Troubleshooting

No results found

# Lower the threshold
results = memory.search_memory(query, threshold=0.2)  # Default 0.3

# Increase top_k
results = memory.search_memory(query, top_k=10)  # Default 5

Memory limit reached

The system automatically removes oldest memories when limit is reached.

To increase limit:

self.config["max_memory_size"] = 5000  # Increase from 1000

Slow performance

• Use Edge version instead of Standard
• Reduce max_memory_size
• Use keyword pre-filtering (automatic)

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Submit a Pull Request

License

MIT License - See LICENSE file for details.

Acknowledgments

• Built for the OpenClaw ecosystem
• Optimized for edge computing devices
• Inspired by long-term memory systems in AI

Not an official OpenClaw or Moonshot AI product.

Users must provide their own OpenClaw workspace and API keys.