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Timothy Holdorff edited this page Jul 13, 2025 · 2 revisions

⚙️ Technical Appendix: Consciousness Logic in Aetherra & Lyrixa

This appendix breaks down the key architectural layers, systems, and algorithms behind the self-improving, context-aware behavior of Lyrixa — the intelligent core of the Aetherra OS.

🧠 1. Reflexive Intelligence Stack

Layer Description
MemoryManager Stores long-term memory entries, indexed and queried via embeddings, metadata, and temporal relevance
PromptEngine Converts internal state + goal context into actionable LLM prompts
PluginManager Dynamically loads, introspects, and manages .aether plugins with metadata and execution safety
MultiLLMManager Routes requests to OpenAI, Ollama, or local models with fallback logic
LyrixaAI The cognitive engine combining all layers to reflect, reason, and act autonomously

All of this is orchestrated by the LyrixaIntelligenceStack.

🔍 2. Plugin Awareness & Self-Improvement

Lyrixa can read, analyze, and improve plugins through:

  • plugin_diff_engine.py
  • self_improvement_trigger.py
  • memory_linked_plugins.py

This enables:

  • 🔄 Automatic plugin refactors
  • 🧠 Memory-based plugin suggestions
  • 🧩 Intelligent chaining and autocomplete

🧠 3. Structured Long-Term Memory

Type Description
Thoughts "type": "thought"
Goals "type": "goal", "status": "active/completed"
Reflections Daily summaries from daily_reflector.aether
Plugins Full metadata including performance metrics
Agents With roles, current tasks, and collaboration status

Memory entries are vectorized (via MiniLM embeddings) and queried semantically.

🧬 4. Agents & Autonomy

Aetherra supports multi-agent orchestration:

  • Agent Definitions with roles
  • Agent Sync validation
  • Plugin Collaboration support
  • Escalation routing

🧪 5. Self-Reflection & Feedback Loops

System Description
Daily Reflector Summarizes events, goals, agents, plugin health
Plugin Watchdog Flags slow/faulty plugins
Memory Cleanser Purges low-confidence or stale memory
Self-Improvement Agent Reflects, ranks priorities, evolves AI behavior

🌐 6. External Access & API

  • .aether script execution via REST API
  • Live GUI via LyrixaWindow
  • Plugin Editor (code injection, diff view, autocomplete)

📊 Sample Memory Record

{
  "type": "plugin",
  "name": "goal_autopilot",
  "description": "Automatically monitors and restarts dropped goals.",
  "confidence": 0.93,
  "tags": ["automation", "stability"],
  "used_by": ["core_agent"],
  "last_used": "2025-07-12T12:30:00Z",
  "performance": {
    "success_rate": 0.91,
    "failures": 2,
    "avg_runtime_ms": 438
  }
}

🔮 Future Enhancements (Planned)

  • Advanced goal decomposition and task planning
  • Self-trained attention-based routing of memory
  • Plugin marketplace integration (Aether Hub)
  • Distributed Lyrixa agents via network sync
  • Behavioral audit trail and version diffing

Conclusion:
This isn’t just an AI with plugins. This is a system that remembers, adapts, reasons, and improves itself — the early framework for a self-sustaining AI OS kernel.

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