Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · Developer Tools
Openclaw Memory Enhancer
Edge-optimized RAG memory system for OpenClaw with semantic search. Automatically loads memory files, provides intelligent recall, and enhances conversations...
skill openclawclawhub Free
Edge-optimized RAG memory system for OpenClaw with semantic search. Automatically loads memory files, provides intelligent recall, and enhances conversations...
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 0.1.0
Provenance
- Publisher
- henryfcb
- Source page
- View original listing
- Canonical URL
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Documentation
🧠 OpenClaw Memory Enhancer
Give OpenClaw long-term memory - remember important information across sessions and automatically recall relevant context for conversations.
Core Capabilities
CapabilityDescription🔍 Semantic SearchVector similarity search, understanding intent not just keywords📂 Auto LoadAutomatically reads all files from memory/ directory💡 Smart RecallFinds relevant historical memory during conversations🔗 Memory GraphBuilds connections between related memories💾 Local Storage100% local, no cloud, complete privacy🚀 Edge Optimized<10MB memory, runs on Jetson/Raspberry Pi
Quick Reference
TaskCommand (Edge Version)Command (Standard Version)Load memoriespython3 memory_enhancer_edge.py --loadpython3 memory_enhancer.py --loadSearch--search "query"--search "query"Add memory--add "content"--add "content"Export--export--exportStats--stats--stats
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)
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)
Via ClawHub (Recommended)
clawhub install openclaw-memory-enhancer
Via Git
git clone https://github.com/henryfcb/openclaw-memory-enhancer.git \ ~/.openclaw/skills/openclaw-memory-enhancer
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
TypeDescriptionExampledaily_logDaily memory filesmemory/2026-02-22.mdcapabilityCapability recordsSkills, toolscore_memoryCore conventionsImportant rulesqaQuestion & AnswerQ: How to... A: You should...instructionDirect instructions"Remember: always do X"solutionTechnical solutionsStep-by-step guidespreferenceUser preferences"User likes dark mode"
Memory Encoding (Edge Version)
Keyword Extraction: Extract important words from text Hash Vector: Map keywords to vector positions Normalization: L2 normalize the vector Storage: Save to local JSON file
Memory Retrieval
Query Encoding: Convert query to same vector format Keyword Pre-filter: Fast filter by common keywords Similarity Calculation: Cosine similarity between vectors 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
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 }
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
Fork the repository Create a feature branch Make your changes 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.
Category context
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
1 Docs
- SKILL.md