🧠 Project Mnemosyne

Building the Memory Layer for Next-Generation AI Systems

Project Mnemosyne is an open-source AI Memory Operating System designed to provide lifelong, structured memory for Large Language Models (LLMs).

Rather than being another Retrieval-Augmented Generation (RAG) application, Project Mnemosyne aims to become a complete memory architecture capable of ingesting, organizing, connecting, retrieving, and reasoning over a continuously evolving knowledge base.

The long-term goal is to separate memory from reasoning, allowing any compatible LLM—ChatGPT, Claude, Gemini, local models, or future foundation models—to access the same persistent knowledge layer.

---

Why Mnemosyne?

In Greek mythology, Mnemosyne is the Titaness of Memory and the mother of the Muses.

The project takes its name from the idea that memory is the foundation of intelligence. Just as human reasoning depends on accumulated experience, Project Mnemosyne aims to provide AI systems with a persistent external memory that grows over time.

---

Why?

Modern LLMs reason well but forget almost everything between conversations.

They cannot naturally remember:

• Previous projects
• Research papers
• Books
• Notes
• Code
• Learning history
• Experiments
• Mistakes
• Long-term goals

Every conversation starts with limited context.

Project Mnemosyne exists to solve this problem.

---

Objectives

Project Mnemosyne aims to:

• Build a modular AI memory operating system.
• Decouple memory from reasoning.
• Support multiple LLM providers through a unified memory layer.
• Serve as both an engineering project and a research platform.
• Remain fully open source and extensible.

---

Non-Goals

Project Mnemosyne is not:

• A PDF chatbot
• A ChatGPT wrapper
• A LangChain demo
• A replacement for existing LLMs
• A note-taking application

Its purpose is to build the memory layer beneath modern AI systems.

---

Core Philosophy

Reasoning belongs to the LLM. Memory belongs to Project Mnemosyne.

Four principles drive the design:

1. Everything becomes a Document
2. Knowledge is a Graph
3. Retrieval is Intelligence
4. Memory is LLM-Agnostic

---

High-Level Architecture

                User
                  │
                  ▼
           Query Interface
                  │
                  ▼
          Retrieval Engine
     ┌───────────┼───────────┐
     ▼           ▼           ▼
Vector DB   Knowledge Graph Metadata DB
     │           │           │
     └───────────┼───────────┘
                 ▼
          Document Repository
                 ▼
       Ingestion & Processing

---

Core Components

Component	Purpose
Document Engine	Unified document abstraction
Chunking Engine	Multiple chunking strategies
Embedding Engine	Semantic representations
Vector Database	Semantic retrieval
Knowledge Graph	Relationship modeling
Metadata Engine	Structured metadata
Retrieval Engine	Hybrid retrieval
Agent Framework	Specialized AI agents
Memory System	Long-term structured memory

---

Tech Stack

Layer	Technology
Language	Python
Backend	FastAPI
Vector DB	Qdrant
Knowledge Graph	Neo4j
Database	SQLite → PostgreSQL
Embeddings	Sentence Transformers
Infrastructure	Docker
Testing	pytest
Formatting	Ruff + Black
Type Checking	mypy

---

Current Status

🚧 Active Development

Current Phase:

Phase 1 — Core Architecture

Version:

v0.1.0-alpha

---

Roadmap

• ✅ Phase 0 — Project Foundation
• 🚧 Phase 1 — Core Architecture
• ⏳ Phase 2 — Document Ingestion
• ⏳ Phase 3 — Embeddings & Vector Search
• ⏳ Phase 4 — Hybrid Retrieval
• ⏳ Phase 5 — Knowledge Graph
• ⏳ Phase 6 — Agent Framework
• ⏳ Future: UI, Cloud Deployment, Enterprise Integrations

Detailed roadmap available in docs/ROADMAP.md

---

Engineering Principles

Project Mnemosyne is intentionally built from first principles.

Understand
      ↓
Design
      ↓
Prototype
      ↓
Implement
      ↓
Test
      ↓
Document
      ↓
Benchmark
      ↓
Commit

Whenever possible:

• Build before importing.
• Understand before optimizing.
• Measure before scaling.

Production libraries are introduced only after understanding the problem they solve.

---

Getting Started

git clone https://github.com/Ultronious/Project-Mnemosyne.git

cd Project-Mnemosyne

python -m venv .venv

# Windows
.\.venv\Scripts\Activate.ps1

# Linux / macOS
source .venv/bin/activate

---

Documentation

Detailed documentation is available in the docs/ directory.

• Vision
• Architecture
• Roadmap
• Document Model
• Retrieval
• Knowledge Graph
• Research Notes

---

Long-Term Vision

Project Mnemosyne is not designed to compete with language models.

Instead, it provides the missing layer beneath them.

As reasoning engines continue to evolve, models will change.

Memory should not.

The long-term goal is to build an open, modular memory architecture that enables AI systems to organize, retrieve, and reason over a lifetime of accumulated knowledge.

---

License

MIT License — see LICENSE.

---

Building a lifelong memory system for AI, one component at a time.