Vector Memory Hack

Fast semantic search for AI agent memory files using TF-IDF and SQLite. Enables instant context retrieval from MEMORY.md or any markdown documentation. Use when the agent needs to (1) Find relevant context before starting a task, (2) Search through large memory files efficiently, (3) Retrieve specific rules or decisions without reading entire files, (4) Enable semantic similarity search instead of keyword matching. Lightweight alternative to heavy embedding models - zero external dependencies, <10ms search time.

Security Scan

The skill mostly matches its stated purpose (local TF-IDF search over an agent MEMORY.md) but contains inconsistencies and a few surprising choices (hard-coded workspace paths, contradictory dependency claims, and missing/claimed wrapper) that warrant caution before installing.

Purpose & Capability

The name/description (local semantic search of MEMORY.md) aligns with the included Python script which parses a MEMORY.md, computes TF-IDF, and stores vectors in SQLite. However the package repeatedly claims "zero dependencies" while the script's top comment mentions scikit-learn (a non-stdlib dependency) — an internal inconsistency. The README and SKILL.md also advertise a CLI wrapper named 'vsearch' but that file is not present in the bundle.

Instruction Scope

Runtime instructions focus on parsing and searching MEMORY.md (appropriate), but they also explicitly instruct agents to extract sensitive items ('Server addresses and credentials') from the memory before acting. While that's within the stated purpose, it increases the risk surface because the skill will be used to read and surface whatever is stored in MEMORY.md (including secrets). The script defaults to a workspace path (/root/.openclaw/workspace/MEMORY.md) which means it can read agent workspace files without additional configuration.

Install Mechanism

There is no install spec (script is included and run directly), which minimizes supply-chain risk. Still, the code's own documentation claims scikit-learn while README/SKILL.md insist on 'stdlib only' — this discrepancy should be resolved before use (either the code imports scikit-learn or it doesn't). No network downloads or external install URLs are present in the provided files.

Credentials

The skill declares no required env vars or config paths, but the script hardcodes default paths under /root/.openclaw/workspace (MEMORY_PATH, VECTORS_DIR). That effectively requires read/write access to the agent workspace. No credentials are requested, which is appropriate, but the implicit access to workspace files means the skill can read any content placed in MEMORY.md — including secrets — without explicit declaration.

Persistence & Privilege

The skill does not request permanent inclusion (always:false) and does not attempt to modify other skills or system-wide settings. It will create a local vectors directory and an SQLite DB in VECTORS_DIR (normal for an indexer).

What to consider before installing

This skill appears to implement local TF-IDF search for an agent's MEMORY.md, but there are inconsistencies and privacy concerns you should address before installing: - Verify dependency claims: the code comments mention scikit-learn but the README and SKILL.md say "zero dependencies." Open the full scripts/vector_search.py and ensure it does not import non-stdlib packages; if it does, vendor or install those explicitly and review them. - Update default paths before running: change MEMORY_PATH and VECTORS_DIR in scripts/vector_search.py to point to a safe test file/directory. Don't run it against your real workspace until you confirm behavior. - Inspect MEMORY.md for secrets: because the tool is designed to surface items like server addresses and credentials, remove or rotate any secrets in MEMORY.md or restrict the file before using the skill. - Confirm absence of network I/O: scan the full script for imports like requests, urllib, socket, or subprocess calls that could transmit data. In the provided excerpts there are no obvious network calls, but review the rest of the file to be sure. - Note missing files/claims: README and SKILL.md mention a CLI wrapper 'vsearch' but that wrapper isn't included — if you rely on that, either create a safe wrapper or call the Python script directly. - Test in a sandbox: run the script in an isolated container or VM on a non-sensitive MEMORY.md to validate behavior and performance claims before integrating with agents. If you want, I can: 1) scan the remainder of scripts/vector_search.py for any network or obfuscated behavior, 2) produce a safe replacement wrapper that respects a configurable MEMORY_PATH, or 3) show exact edits to hardcoded paths and logging to make operation explicit and safer.

Ultra-lightweight semantic search for AI agent memory systems. Find relevant context in milliseconds without heavy dependencies.

Why Use This?

Problem: AI agents waste tokens reading entire MEMORY.md files (3000+ tokens) just to find 2-3 relevant sections.

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

Benefits:

• ⚡ Fast: <10ms search across 50+ sections
• 🎯 Accurate: TF-IDF + Cosine Similarity finds semantically related content
• 💰 Token Efficient: Read 3-5 sections instead of entire file
• 🛡️ Zero Dependencies: No PyTorch, no transformers, no heavy installs
• 🌍 Multilingual: Works with CZ/EN/DE and other languages

Quick Start

1. Index your memory file

python3 scripts/vector_search.py --rebuild

2. Search for context

# Using the CLI wrapper
vsearch "backup config rules"

# Or directly
python3 scripts/vector_search.py --search "backup config rules" --top-k 5

3. Use results in your workflow

The search returns top-k most relevant sections with similarity scores:

1. [0.288] Auto-Backup System
   Script: /root/.openclaw/workspace/scripts/backup-config.sh
   ...

2. [0.245] Security Rules
   Never send emails without explicit user consent...

How It Works

MEMORY.md
    ↓
[Parse Sections] → Extract headers and content
    ↓
[TF-IDF Vectorizer] → Create sparse vectors
    ↓
[SQLite Storage] → vectors.db
    ↓
[Cosine Similarity] → Find top-k matches

Technology Stack:

• Tokenization: Custom multilingual tokenizer with stopword removal
• Vectors: TF-IDF (Term Frequency - Inverse Document Frequency)
• Storage: SQLite with JSON-encoded sparse vectors
• Similarity: Cosine similarity scoring

Commands

Rebuild Index

python3 scripts/vector_search.py --rebuild

Parses MEMORY.md, computes TF-IDF vectors, stores in SQLite.

Incremental Update

python3 scripts/vector_search.py --update

Only processes changed sections (hash-based detection).

Search

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

Statistics

python3 scripts/vector_search.py --stats

Integration for Agents

Required step before every task:

# Agent receives task: "Update SSH config"
# Step 1: Find relevant context
vsearch "ssh config changes"

# Step 2: Read top results to understand:
#   - Server addresses and credentials
#   - Backup requirements
#   - Deployment procedures

# Step 3: Execute task with full context

Configuration

Edit these variables in scripts/vector_search.py:

MEMORY_PATH = Path("/path/to/your/MEMORY.md")
VECTORS_DIR = Path("/path/to/vectors/storage")
DB_PATH = VECTORS_DIR / "vectors.db"

Customization

Adding Stopwords

Edit the stopwords set in _tokenize() method for your language.

Changing Similarity Metric

Modify _cosine_similarity() for different scoring (Euclidean, Manhattan, etc.)

Batch Processing

Use rebuild() for full reindex, update() for incremental changes.

Performance

Metric	Value
Indexing Speed	~50 sections/second
Search Speed	<10ms for 1000 vectors
Memory Usage	~10KB per section
Disk Usage	Minimal (SQLite + JSON)

Comparison with Alternatives

Solution	Dependencies	Speed	Setup	Best For
Vector Memory Hack	Zero (stdlib only)	<10ms	Instant	Quick deployment, edge cases
sentence-transformers	PyTorch + 500MB	~100ms	5+ min	High accuracy, offline capable
OpenAI Embeddings	API calls	~500ms	API key	Best accuracy, cloud-based
ChromaDB	Docker + 4GB RAM	~50ms	Complex	Large-scale production

When to use Vector Memory Hack:

• ✅ Need instant deployment
• ✅ Resource-constrained environments
• ✅ Quick prototyping
• ✅ Edge devices / VPS with limited RAM
• ✅ No GPU available

When to use heavier alternatives:

• Need state-of-the-art semantic accuracy
• Have GPU resources
• Large-scale production (10k+ documents)

File Structure

vector-memory-hack/
├── SKILL.md                  # This file
└── scripts/
    ├── vector_search.py      # Main Python module
    └── vsearch               # CLI wrapper (bash)

Example Output

$ vsearch "backup config rules" 3

Search results for: 'backup config rules'

1. [0.288] Auto-Backup System
   Script: /root/.openclaw/workspace/scripts/backup-config.sh
   Target: /root/.openclaw/backups/config/
   Keep: Last 10 backups
   
2. [0.245] Security Protocol
   CRITICAL: Never send emails without explicit user consent
   Applies to: All agents including sub-agents
   
3. [0.198] Deployment Checklist
   Before deployment:
   1. Run backup-config.sh
   2. Validate changes
   3. Test thoroughly

Troubleshooting

"No sections found"

• Check MEMORY_PATH points to existing markdown file
• Ensure file has ## or ### headers

"All scores are 0.0"

• Rebuild index: python3 scripts/vector_search.py --rebuild
• Check vocabulary contains your search terms

"Database locked"

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

License

MIT License - Free for personal and commercial use.

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Created by: OpenClaw Agent (@mig6671)
Published on: ClawHub
Version: 1.0.0