memg-core 0.5.0

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

💾 memg-core

The foundation of structured memory for AI agents.

memg-core is the deterministic, schema-driven memory engine at the heart of the larger MEMG system. It gives AI developers a fast, reliable, testable memory layer powered by:

• YAML-based schema definition (for custom memory types)
• Dual-store backend (Qdrant for vectors, Kuzu for graph queries)
• Public Python API for all memory operations
• Built-in support for auditability, structured workflows, and self-managed memory loops

It's designed for AI agents that build, debug, and improve themselves — and for humans who demand clean, explainable, memory-driven systems.

🧩 This is just the core. The full memg system builds on this to add multi-agent coordination, long-term memory policies, and deeper retrieval pipelines — currently in progress.

Features

• Vector Search: Fast semantic search with Qdrant
• Graph Storage: Optional relationship analysis with Kuzu
• Offline-First: 100% local embeddings with FastEmbed - no API keys needed
• Type-Agnostic: Configurable memory types via YAML schemas
• See Also Discovery: Knowledge graph-style associative memory retrieval
• Lightweight: Minimal dependencies, optimized for performance

Quick Start

Python Package

pip install memg-core

# Set up environment variables for storage paths
export QDRANT_STORAGE_PATH="/path/to/qdrant"
export KUZU_DB_PATH="/path/to/kuzu.db"

# Use the core library in your app
# 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 memg_core.api.public import add_note, add_document, add_task, search

# Add a note
note = add_note(
    text="Set up Postgres with Docker for local development",
    user_id="demo_user",
    title="Docker Postgres Setup",
    tags=["docker", "postgres", "dev"],
)

# Search (GraphRAG-first pipeline)
results = search("postgres performance", user_id="demo_user", limit=5)
for r in results:
    print(f"[{r.memory.memory_type.value}] {r.memory.title} - Score: {r.score:.2f}")

# Search with "See Also" discovery (finds semantically related memories)
results = search("postgres setup", user_id="demo_user", limit=10, include_see_also=True)
for r in results:
    source = r.source  # 'qdrant' for primary, 'see_also_bug' for related
    print(f"[{source}] {r.memory.memory_type.value}: {r.memory.title} - Score: {r.score:.2f}")

YAML Schema Examples

Core ships with example schemas under config/:

• core.memo.yaml: Basic memory types (memo, note, document, task)
• software_dev.yaml: Enhanced schema with bug and solution types for development workflows
• core.test.yaml: Test configuration for development

Enable:

export MEMG_ENABLE_YAML_SCHEMA=true
export MEMG_YAML_SCHEMA=$(pwd)/config/core.memo.yaml

Embedding Configuration

MEMG Core uses FastEmbed for 100% offline, local embeddings. By default, it uses the highly efficient Snowflake Arctic model:

# Optional: Configure a different FastEmbed model
export EMBEDDER_MODEL="Snowflake/snowflake-arctic-embed-xs"  # Default
# Other options: intfloat/e5-small, BAAI/bge-small-en-v1.5, etc.

Configuration

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

# Core settings
MEMORY_SYSTEM_MCP_PORT=8787  # Change for multiple instances
MEMG_TEMPLATE=software_development

# Embeddings (optional)
EMBEDDER_MODEL=Snowflake/snowflake-arctic-embed-xs

# Storage
BASE_MEMORY_PATH=$HOME/.local/share/memory_system
QDRANT_COLLECTION=memories
EMBEDDING_DIMENSION_LEN=384

Requirements

• Python 3.11+
• No API keys required!

Architecture

memg-core provides a deterministic, YAML-driven memory layer with dual storage:

• YAML-driven schema engine - Define custom memory types with zero hardcoded fields
• Qdrant/Kuzu dual-store - Vector similarity + graph relationships
• Public Python API - Clean interface for all memory operations
• Configurable schemas - Examples in config/ for different use cases

In Scope

• ✅ YAML schema definition and validation
• ✅ Memory CRUD operations with dual storage
• ✅ Semantic search and "see also" discovery
• ✅ Public Python API with full functionality

Coming in Full MEMG System

• 🔄 Schema contracts and multi-agent coordination
• 🔄 Async job processing and bulk operations
• 🔄 Advanced memory policies and retention
• 🔄 Multi-agent memory orchestration

Links

• Repository
• Issues
• Documentation

License

MIT License - see LICENSE file for details.