Alexei — Foundation Release (v0.1) A local AI agent that remembers and is built to learn.

Alexei is a lightweight, persona‑driven AI agent written in C++ and powered by llama.cpp. This early release focuses on the core architecture needed for a future learning AI — a system that can retain memory, adapt over time, and eventually evolve its behavior based on interaction.

Alexei runs entirely on your machine. No cloud. No external APIs. No data leaving your system.

Purpose The goal of this project is to build an AI that can:

retain memory across sessions

adapt behavior based on past interactions

eventually learn from experience without retraining model weights

This release lays the groundwork: persona system, memory structure, modular agent design, and a stable inference loop.

Learning logic (memory extraction, updating, persona evolution) is planned for upcoming versions.

Current Features (v0.1) Local‑only inference using any GGUF model

Persistent memory structure (facts, preferences, persona traits)

Persona engine driven by identity.txt and config JSON

Modular architecture (AI engine, agent core, tools, memory, HTTP)

Simple HTTP interface for sending messages

Clean, stable prompt pipeline

This version focuses on the foundation — the systems that make learning possible.

Project Structure Code src/ Core C++ source code persona/ Persona definition for Alexei config/ Persona configuration JSON memory/ Runtime memory storage (template + placeholders) models/ Place your GGUF model here (not included) third_party/ External dependencies (llama.cpp placeholder) include/ Header-only libraries (e.g., nlohmann/json) Model Weights This repository does not include model weights.

To run Alexei:

Download any compatible GGUF model

Place it in the models/ directory

Update the path in the config if needed

Building This project uses CMake.

Code mkdir build cd build cmake .. cmake --build . Run the executable and Alexei will start listening on the configured port.

Roadmap v0.2 — Learning Begins Memory extraction from conversation

Automatic fact + preference detection

Updating memory.json at runtime

Basic persona adaptation

v0.3 — Behavioral Evolution Trait weighting

Style adaptation

Relevance scoring + memory pruning

v0.4 — Tools & Autonomy Tool execution

Multi‑step reasoning

Expanded HTTP API

Contributing Contributions are welcome. The project is intentionally modular and easy to extend — perfect for experimenting with:

memory systems

agent behavior

persona engines

tool integrations

performance improvements

License MIT License. See LICENSE for details.