Athena Public

Athena

Turn stateless LLMs into a stateful, compounding AI agent — portable across any IDE.

Own the state. Rent the intelligence. Persistent memory, structured reasoning,
and governed AI agents that work across any LLM — stored as plain Markdown and JSONL
on your disk across Claude Code, Antigravity, Cursor, Gemini CLI, and VS Code.

Quickstart · How It Works · Structure · SDK & MCP Tools · Docs · Why Athena? · Safety

The Problem: Stateless AI is an Amnesiac Bottleneck

Why do AI coding assistants still give generic, textbook answers? Because frontier LLMs are fundamentally stateless.

Every time you open a new session, the model starts from zero. It has no memory of your architecture, your past post-mortems, your risk tolerance, or your code invariants. To get useful work done, you are forced to pay an Amnesia Tax—spending 10 minutes writing prompt essays explaining your stack and constraints. Worse, platform memory is designed to agree with you faster (sycophancy), not to stress-test your assumptions when stakes are high.

Athena solves this by separating intelligence (the rented model) from state (your sovereign disk):

| Memory & Context | Amnesiac: starts from zero; 10-minute prompt prep tax | Continuous: /start restores active tasks & invariants in  Full install (cloud sync + cross-encoder reranking): pip install -e ".[full]"
See Getting Started for Windows, advanced config, and Supabase setup.

How It Works

Three boot modes, one compounding loop:

Repository Structure

Following Protocol 543 standard repository scaffold:

What Ships in the SDK

Athena is not just a prompt pack — it ships executable Python engines and two Model Context Protocol (MCP) servers (athena.mcp_server and athena.duckdb_mcp_server):

What's enforced in code vs. by prompt
*Most…