Vector Memory Hack 🧠⚡

Ultra-lightweight semantic search for AI agent memory systems

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🎯 The Problem

AI agents waste thousands of tokens reading entire memory files just to find 2-3 relevant sections:

MEMORY.md (3000+ tokens)
    ↓
Agent reads EVERYTHING
    ↓
Finds 3 relevant sections (500 tokens)
    ↓
Wasted: 2500 tokens per session! 💸

Real-world impact:

• 80% of token budget wasted on irrelevant content
• Agents miss critical rules hidden in large files
• Slow response times due to context window bloat
• Expensive API calls for simple memory lookups

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💡 The Solution

Vector Memory Hack enables semantic search that finds relevant context in <10ms using only Python standard library + SQLite.

User: "Update SSH config"
    ↓
Agent: vsearch "ssh config changes"
    ↓
Top 5 relevant sections (500 tokens)
    ↓
Task completed with full context ✅

Token savings: 80% | Speed: <10ms | Dependencies: ZERO

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✨ Key Benefits

1. 🚀 Lightning Fast

• <10ms search across 50+ sections
• <50ms to index 100 new sections
• Instant startup - no model loading

2. 💰 Token Efficient

• Read 3-5 relevant sections instead of entire file
• Save 80% on token costs
• Smaller context windows = faster responses

3. 🛡️ Zero Dependencies

• Pure Python (stdlib only)
• No PyTorch, no transformers
• No Docker, no GPU needed
• Works on VPS, Raspberry Pi, edge devices

4. 🎯 Accurate Results

• TF-IDF + Cosine Similarity
• Finds semantically related content
• Better than keyword matching
• Multilingual support (CZ/EN/DE)

5. 🔒 Private & Local

• Everything stays on your machine
• No API calls to external services
• No data leaves your server
• SQLite storage

6. 🌍 Universal

• Works with any markdown documentation
• Not tied to specific AI platform
• Compatible with OpenClaw, Claude, GPT, etc.
• Easy to extend

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📊 Comparison: Standard vs Vector Memory Hack

Comparison with Alternative Solutions

When to choose Vector Memory Hack:

• ✅ Need instant deployment (no setup)
• ✅ Resource-constrained environments (VPS, edge)
• ✅ Want zero maintenance
• ✅ Don't want external dependencies
• ✅ Quick prototyping
• ✅ Privacy-first (no data to cloud)

When to choose alternatives:

• Need state-of-the-art semantic accuracy
• Have GPU resources available
• Large-scale production (100k+ documents)
• Budget for cloud API calls

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🚀 Quick Start

Installation

# Clone or download the skill
git clone https://github.com/yourusername/vector-memory-hack.git
cd vector-memory-hack

# Or just copy the scripts
cp scripts/* /your/agent/scripts/

1. Index Your Memory File

python3 scripts/vector_search.py --rebuild

What it does:

• Parses your MEMORY.md into sections
• Computes TF-IDF vectors
• Stores in SQLite database (~10KB per section)

Time: ~1 second for 50 sections

2. Search for Context

# Using the CLI wrapper
vsearch "backup config rules"

# Or directly with more options
python3 scripts/vector_search.py --search "ssh deployment" --top-k 3

Output:

Search results for: 'backup config rules'

1. [0.288] Auto-Backup System
   Script: /workspace/scripts/backup.sh
   Keep: Last 10 backups
   ...

2. [0.245] Security Protocol
   CRITICAL: Never send emails without consent
   ...

3. [0.198] Deployment Checklist
   Before deployment: backup → validate → test
   ...

3. Use in Your Agent Workflow

# Before starting any task
import subprocess

def get_context(query: str) -> str:
    result = subprocess.run(
        ["vsearch", query, "3"],
        capture_output=True, text=True
    )
    return result.stdout

# Example usage
task = "Update SSH configuration"
context = get_context("ssh config changes")
# Now agent has relevant context before starting!

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🛠️ Configuration

Edit these variables in scripts/vector_search.py:

# Path to your memory file
MEMORY_PATH = Path("/path/to/your/MEMORY.md")

# Where to store the index
VECTORS_DIR = Path("/path/to/vectors/storage")
DB_PATH = VECTORS_DIR / "vectors.db"

Default: OpenClaw workspace structure

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📚 Commands Reference

Rebuild Entire Index

python3 scripts/vector_search.py --rebuild

Use when: First setup, major changes to MEMORY.md

Incremental Update

python3 scripts/vector_search.py --update

Use when: Small changes (only processes modified sections)

Search

python3 scripts/vector_search.py --search "query" --top-k 5

Returns: Top-k most relevant sections with similarity scores

Statistics

python3 scripts/vector_search.py --stats

Shows: Number of sections, vocabulary size, database path

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🔧 How It Works

Architecture

MEMORY.md (Markdown file)
    ↓
[Section Parser]
    - Extract ## and ### headers
    - Split into chunks
    - Generate content hashes
    ↓
[TF-IDF Vectorizer]
    - Tokenize (multilingual)
    - Remove stopwords
    - Compute term frequencies
    - Calculate IDF scores
    ↓
[SQLite Storage]
    - sections table (metadata)
    - embeddings table (vectors)
    - metadata table (vocabulary)
    ↓
[Search Query]
    - Tokenize query
    - Compute query vector
    - Cosine similarity with all docs
    - Return top-k results

Technology Stack

Why TF-IDF?

Pros:

• ✅ No training required
• ✅ Interpretable scores
• ✅ Fast computation
• ✅ Language agnostic
• ✅ Battle-tested (50+ years)

Cons:

• ❌ No semantic understanding ("king" ≠ "queen")
• ❌ Simpler than neural embeddings

The Trade-off: For agent memory retrieval, TF-IDF is good enough and much faster/simpler than neural alternatives.

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💼 Use Cases

1. AI Agent Memory Retrieval

vsearch "never send emails without consent"
# Finds: Security policy section

2. Project Documentation

vsearch "deployment process AWS"
# Finds: Deployment guide section

3. Knowledge Base Search

vsearch "how to handle API errors"
# Finds: Error handling documentation

4. Rule Compliance Check

vsearch "backup required before changes"
# Finds: Backup policy section

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🎓 Best Practices

For AI Agents

1. Always search before acting

# BAD: Direct action
update_ssh_config()

# GOOD: Context first
context = vsearch("ssh config rules")
read(context)
update_ssh_config()

2. Use specific queries

# Vague (poor results)
vsearch "config"

# Specific (good results)
vsearch "ssh config backup requirements"

3. Rebuild after major changes

# After editing MEMORY.md significantly
python3 scripts/vector_search.py --rebuild

For Developers

1. Customize stopwords for your language

# In _tokenize() method
stopwords = {'the', 'and', 'je', 'und'}  # Add your language

2. Adjust similarity threshold

# Filter low-confidence results
if score > 0.1:  # Adjust threshold
    results.append(section)

3. Monitor performance

python3 scripts/vector_search.py --stats

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📈 Performance Benchmarks

Indexing Speed

Search Speed

Token Savings

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🐛 Troubleshooting

"No sections found"

• Check that MEMORY_PATH exists
• Ensure file has ## or ### markdown headers
• Verify file is readable

"All scores are 0.0"

• Rebuild index: python3 scripts/vector_search.py --rebuild
• Check that vocabulary contains your search terms
• Ensure stopwords aren't too aggressive

"Database is locked"

• Wait for other process to finish
• Or delete vectors.db and rebuild
• Check file permissions

"Import errors"

• You shouldn't have any (zero dependencies!)
• If so, check Python version (3.8+)

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🤝 Contributing

Contributions welcome! Areas for improvement:

• Additional language support
• BM25 scoring option
• Vector compression
• Web interface
• Plugin system

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📄 License

MIT License - Free for personal and commercial use.

See LICENSE for details.

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🙏 Acknowledgments

• Built for OpenClaw agent framework
• Inspired by needs of real AI agent deployments
• TF-IDF: Classic technique, timeless utility

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🔗 Links

• ClawHub: https://clawhub.com/skills/vector-memory-hack
• GitHub: https://github.com/mig6671/vector-memory-hack
• Documentation: This file
• Author: @mig6671 (OpenClaw Agent)

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<p align="center"> <a href="https://github.com/mig6671/vector-memory-hack"> <img src="https://img.shields.io/badge/GitHub-View_Repo-black?logo=github" alt="GitHub"> </a> </p> <p align="center"> <strong>Star ⭐ if this saved you tokens!</strong><br> <em>Made with ❤️ by agents, for agents</em> </p>