engram-db 0.1.0

High-performance memory database for LLMs

🧠 Engram: Fast, Local-First Memory for LLMs

Engram is a high-performance, local-first memory database designed specifically for AI agents and RAG (Retrieval-Augmented Generation) applications. It is built in Rust for speed and provides native Python bindings for ease of use.

Zero Config. Zero API Keys. Zero Latency.

✨ Features

• 🚀 Blazing Fast: Core engine written in Rust with HNSW indexing for sub-10ms retrieval.
• 🔒 Privacy First: Everything stays on your machine. No data is sent to external embedding providers.
• 📦 All-in-One: Integrated vector storage, metadata management, and local embeddings (via FastEmbed).
• 🔋 Battery Included: Auto-downloads and manages optimized ONNX embedding models locally.

🛠️ Installation

# Clone the repository
git clone https://github.com/yourusername/engram
cd engram

### Python
```bash
pip install maturin
maturin develop --features python

Node.js

npx napi build --release --features node

📖 Quick Start (RAG in 30 Seconds)

import engram

# 1. Initialize the database (Saves to a local folder)
db = engram.EngramDB("./my_knowledge_base")

# 2. Store documents with metadata
db.store(
    "Engram is a memory database written in Rust.", 
    {"source": "docs", "priority": "high"}
)

# 3. Recall based on semantic meaning
results = db.recall("How is Engram built?", limit=1)

for content, metadata in results:
    print(f"Retrieved: {content}")
    print(f"Metadata: {metadata}")

🏗️ Architecture

Engram uses a custom binary storage engine called Mnemo combined with HNSW (Hierarchical Navigable Small World) for ultra-fast vector search.

1. Mnemo Engine: A low-level, append-only binary log that ensures your data is persisted safely to disk.
2. HNSW Index: An in-memory graph structure rebuilt from disk on startup for lightning-fast nearest neighbor search.
3. Local Embeddings: Uses fastembed-rs to run optimized ONNX models like all-MiniLM-L6-v2 locally on your CPU/GPU.

🤝 Contributing

We welcome contributions! Please feel free to submit a Pull Request.

📄 License

MIT License. See LICENSE for details.