Local Vector Memory Skill

Zero-cloud vector memory using Ollama embeddings + Qdrant local storage.

Prerequisites

# Ollama with embedding model
ollama pull qwen3-embedding:4b

# Install the package
pip install local-vector-memory

Quick Reference

lvm init                    # Initialize database
lvm add "text to remember"  # Store a memory
lvm search "query"          # Semantic search
lvm search "query" --limit 3 --json  # Structured output
lvm stats                   # Show stats
lvm reindex --dir ~/notes   # Reindex markdown files
lvm delete "source_name"    # Delete by source

Python Library Usage

from local_vector_memory.core import LocalVectorMemory

lvm = LocalVectorMemory()  # uses env defaults
lvm.add("OpenClaw baseUrl must not end with /v1")
results = lvm.search("how to configure ollama")
for r in results:
    print(f"[{r['score']}] {r['source']}: {r['text'][:100]}")

Configuration

Embedding Model Selection

Integration Patterns

With OpenClaw

Add to HEARTBEAT.md or cron for periodic reindexing:

lvm reindex --dir ~/.openclaw/workspace/memory

As a backup search layer

When memory_search doesn't find what you need:

lvm search "query" --json

Security

• Ollama URL restricted to localhost only (SSRF protection)
• Path traversal blocked in reindex glob patterns
• Input length limits enforced (100K text, 10K query)
• All data stored locally, no network calls except to local Ollama

Links

• PyPI: https://pypi.org/project/local-vector-memory/
• GitHub: https://github.com/JanCong/local-vector-memory