memg 0.5.5

True memory for AI - lightweight, generalist, AI-made, AI-focused

MEMG 🧠⚡

True memory for AI - lightweight, generalist, AI-made, AI-focused

MEMG is a lightweight memory management system that provides persistent memory capabilities for AI agents and applications. Built on top of the robust memg-core foundation, MEMG adds enhanced APIs, integration tools, and seamless development experience for structured memory operations.

🚀 Quick Start

pip install memg

import memg

# Add memories with structured data
memory = memg.add_memory("task", {
    "statement": "Implement user authentication system",
    "details": "Need to add JWT-based auth with role management",
    "priority": "high"
}, user_id="your_user_id")

# Search memories with keywords
results = memg.search("authentication", user_id="your_user_id")

# Create memory configuration from YAML
memory_config = memg.create_memory_from_yaml("config/my_config.yaml")

✨ Key Features

🎯 Structured Memory Management

• Vector Search: Semantic search capabilities with relevance scoring
• Graph Storage: Efficient relationship tracking between memories
• Schema Validation: YAML-based memory schemas with type safety

🏗️ Built on Solid Foundation

• memg-core Integration: Leverages battle-tested core memory management
• Production Ready: Comprehensive testing, quality tools, and CI/CD
• Modern Architecture: Clean separation between core storage and enhanced features

🔌 Flexible Integration

• Python SDK: Clean Python API built on memg-core
• YAML Configuration: Flexible schema definition and management
• Cross-platform: Works on all major operating systems

🛠️ Developer Experience

• Rich Configuration: YAML-based schemas and flexible setup
• Comprehensive Testing: Unit and integration test suites
• Quality Tools: Ruff, MyPy, Bandit for code quality
• Type Safety: Full type hints and runtime validation

📦 Architecture

MEMG Ecosystem
├── memg-core (PyPI)          # Foundation: storage, search, schemas
└── MEMG (this package)       # Enhanced APIs and utilities

Core Components

• memg.core: Integration layer with memg-core
• memg.search: Search orchestration and utilities
• memg.api: High-level API interfaces
• memg.utils: Utilities and schema management

🎮 Usage Examples

Basic Memory Operations

import memg

# Create and store memories
memory = memg.add_memory("note", {
    "statement": "API design patterns research",
    "details": "Investigated REST vs GraphQL for user management API",
    "project": "web-app"
}, user_id="your_user_id")

# Search memories by keyword
results = memg.search("API design", user_id="your_user_id")

# Delete memories when no longer needed
memg.delete_memory(memory.memory_id, user_id="your_user_id")

Configuration and Schema Management

import memg

# Get current memory configuration
config = memg.get_config()

# Load configuration from YAML file
memory_system = memg.create_memory_from_yaml("config/custom_schema.yaml")

# Work with memory objects
for result in memg.search("project tasks", user_id="your_user_id"):
    print(f"Memory: {result.payload['statement']}")
    print(f"Score: {result.score}")

Working with Search Results

import memg

# Search returns structured results
results = memg.search("authentication tasks", user_id="your_user_id")

for result in results:
    print(f"Type: {result.memory_type}")
    print(f"Content: {result.payload['statement']}")
    print(f"Relevance Score: {result.score}")
    print(f"Memory ID: {result.memory_id}")

🏁 Getting Started

Installation

# Install MEMG
pip install memg

# For development setup
git clone https://github.com/genovo-ai/memg.git
cd memg
pip install -e ".[dev]"

Configuration

Create a memory configuration:

# config/my_config.yaml
entities:
  task:
    required: [statement]
    optional: [assignee, priority, status, due_date]
  note:
    required: [statement, details]
    optional: [project, tags]

import memg

# Initialize with custom configuration
memory = memg.create_memory_from_yaml("config/my_config.yaml")

Memory Server

For development and testing:

# Install in development mode
pip install -e ".[dev]"

# Run tests to verify installation
python -m pytest tests/

🔧 Development

Quality Tools

# Run all quality checks
make quality-check

# Individual tools
make lint          # Ruff linting
make typecheck     # MyPy type checking
make security      # Bandit security scan
make test          # Full test suite

Testing

# Run tests
make test-all

# Fast tests only
make test-fast

# Integration tests
make test-integration

# Coverage report
make test-coverage

🤝 Contributing

We welcome contributions! Please see our development workflow:

1. Fork & Clone: Fork the repository and clone locally
2. Setup: pip install -e ".[dev]" for development dependencies
3. Quality: Run make quality-check before committing
4. Test: Ensure make test-all passes
5. PR: Submit a pull request with clear description

Development Standards

• Code Quality: Ruff formatting and linting
• Type Safety: MyPy type checking required
• Security: Bandit security scanning
• Testing: Comprehensive test coverage
• Documentation: Clear docstrings and examples

📚 Documentation

• API Reference: Coming soon
• Architecture Guide: See src/memg/ for component structure
• Integration Guide: Coming soon
• Configuration: Explore config/ directory for examples

🛡️ Security

• Bandit Scanning: Automated security vulnerability detection
• Dependency Management: Regular security updates
• Input Validation: Comprehensive data validation
• Safe Defaults: Secure-by-default configuration

📄 License

MIT License - see LICENSE file for details.

🌟 Related Projects

• memg-core: Foundation memory management system

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Built with ❤️ by the MEMG Team

True memory for AI - making intelligent agents truly intelligent 🧠✨