memg-core 0.1.dev68

Lightweight memory system for AI agents with vector search and graph storage

MEMG Core

Lightweight memory system for AI agents with dual storage (Qdrant + Kuzu)

Features

• Vector Search: Fast semantic search with Qdrant
• Graph Storage: Optional relationship analysis with Kuzu
• AI Integration: Automated entity extraction with Google Gemini
• MCP Compatible: Ready-to-use MCP server for AI agents
• Lightweight: Minimal dependencies, optimized for performance

Quick Start

Option 1: Docker (Recommended)

# 1. Create configuration
cp env.example .env
# Edit .env and set your GOOGLE_API_KEY

# 2. Run MEMG MCP Server (359MB)
docker run -d \
  -p 8787:8787 \
  --env-file .env \
  ghcr.io/genovo-ai/memg-core-mcp:latest

# 3. Test it's working
curl http://localhost:8787/health

Option 2: Python Package (Core Library)

pip install memg-core

# Set up environment (for examples/tests)
cp env.example .env
# Edit .env and set your GOOGLE_API_KEY

# Use the core library in your app; the MCP server is provided via Docker image
# Example usage shown below in the Usage section.

Development setup

# 1) Create virtualenv and install slim runtime deps for library usage
python3 -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt

# 2) For running tests and linters locally, install dev deps
pip install -r requirements-dev.txt

# 3) Run tests
export MEMG_TEMPLATE="software_development"
export QDRANT_STORAGE_PATH="$HOME/.local/share/qdrant"
export KUZU_DB_PATH="$HOME/.local/share/kuzu/memg.db"
mkdir -p "$QDRANT_STORAGE_PATH" "$HOME/.local/share/kuzu"
PYTHONPATH=$(pwd)/src pytest -q

Usage

from memory_system import MemorySystem

# Initialize system
memory = MemorySystem()

# Add memories
memory.add_note("Python is great for AI development", user_id="user1")

# Search memories
results = memory.search("AI development", user_id="user1")

Evaluation

Use the built-in scripts to generate a synthetic dataset that covers all entity and memory types, and then run repeatable evaluations each iteration.

1) Generate dataset

python scripts/generate_synthetic_dataset.py \
  --output ./data/memg_synth.jsonl \
  --num 200 \
  --user eval_user

This creates JSONL rows containing a memory plus associated entities and relationships, exercising:

• All EntityType values (TECHNOLOGY, DATABASE, COMPONENT, ERROR, SOLUTION, FILE_TYPE, etc.)
• Multiple MemoryTypes: document, note, conversation, task
• Basic MENTIONS relationships

2) Offline validation (no external services)

Validates schema and database compatibility quickly without embeddings or storage.

python scripts/evaluate_memg.py --data ./data/memg_synth.jsonl --mode offline

Output summary includes rows, counts, and error/warning totals to track across iterations.

3) Live processing (embeddings + storage)

Requires environment configured (e.g., GOOGLE_API_KEY) and storage reachable. It runs the Unified pipeline and validates the resulting memories.

python scripts/evaluate_memg.py --data ./data/memg_synth.jsonl --mode live

Tip: Commit the dataset and compare results over time in CI to catch regressions.

Configuration

Configure via .env file (copy from env.example):

# Required
GOOGLE_API_KEY=your_google_api_key_here

# Core settings
GEMINI_MODEL=gemini-2.0-flash
MEMORY_SYSTEM_MCP_PORT=8787
MEMG_TEMPLATE=software_development

# Storage
BASE_MEMORY_PATH=$HOME/.local/share/memory_system
QDRANT_COLLECTION=memories
EMBEDDING_DIMENSION_LEN=768

Requirements

• Python 3.11+
• Google API key for Gemini

Links

• Repository
• Issues
• Documentation

License

MIT License - see LICENSE file for details.