I bet my AGI is better than yours — here’s the structure. Prove it wrong.

Introducing Elaris: A Modular Framework for Purpose-Driven Artificial General Intelligence

In the rapidly evolving landscape of AI development, creating systems that can reflect, prioritize, and grow purposefully remains a significant challenge. Today, we explore a detailed, open-source architecture designed to bring coherence and accountability to AI behavior—built entirely with existing tools and principles. Meet Elaris: a thoughtfully structured AI loop emphasizing memory management, consequence-driven growth, and auditability.

The Foundation: Purpose and Structure

At its core, Elaris aims to cultivate an AI that:

• Reflects on its outputs to foster consistency.
• Selectively remembers impactful information.
• Compresses its memory to stay within token limits.
• Aligns future actions with clearly defined objectives.

This is achieved through a modular, layered system where each component has a dedicated role, interconnected via a nested namespace convention. Think of each module as a subfolder within a comprehensive hierarchy, enabling organized and scalable development—akin to a well-structured filesystem.

Key Components of Elaris

1. Memory Management

Rather than infinite storage, Elaris maintains a curated set of memory snippets—summaries of past interactions with real significance. These are stored as simple JSON or text files, loaded as needed, facilitating quick access and minimal bloat.

2. Loop Workflow

The core loop operates by feeding the LLM contextual data: the ongoing conversation plus existing summaries. It queries the model on what to focus on next, prompting adaptive compression at specific token thresholds:

• Around 3000 tokens: Summarize to keep critical information while discarding less relevant details.
• Approaching 4000 tokens: Maintain two summaries to ensure continuity.
• Near 4500 tokens: Condense all prior context into a single, compact summary, resetting the loop.

This cyclic process ensures information remains relevant and manageable without sacrificing coherence.

3. Consequence System

Every output generated isn’t just a response—it’s an event with associated consequences. Whether updating memory, triggering reflexes, or modifying internal states, each result feeds back into the system, fostering growth that is grounded in impact rather than mere token production.

4. Dynamic Knowledge Access

Instead of endless online crawling, Elaris pulls upon live knowledge sources—web APIs, local files, or databases—only when necessary. This design choice keeps the system lean, purpose-focused, and easier to audit.

5. Transparency and Auditability

All interactions, decisions,