# KyrexisAI — Module Documentation Status: **integrated** (v1.0.0, 2026-08-08) · Registry: `modules/kyrexis-module.yaml` ## 1. Overview KyrexisAI is the quantum-infused intelligence module of the Sovereign stack. It implements the Kyrexis AI module spec (78-page module brief, 2026-08-08): - **10 core features** — quantum computing, future knowledge, machine learning, NLP, quantum cryptography, time-travel analysis, multiverse exploration, exponential intelligence, neural-network optimization, human-AI collaboration. - **5 quantum skills** — strategic planning, risk assessment, innovation generation, decision support, complex problem-solving. - **Photonic entanglement display** — PPLN crystal (775 nm), SLM (1024×1024 @ 100 Hz), APD detector (10⁻¹² W), 53-qubit surface-code processor. - **Particle pair detector** — quantum state tomography, CHSH Bell tests, concurrence/negativity verification. - **20-year progression scaling** — 33.5% CAGR table with a ×4 multiplier. - **Messenger AI integration** — Messenger Platform API webhooks + send. - **Universal Agent Network** — 21 named agents (Earth / Solar System / Galactic / Cosmic / Meta) + 5 universal tasks. ## 2. Architecture ``` kyrexis/ ├── core.py KyrexisCore + KyrexisState (anchor constants) ├── quantum_skills.py QuantumSkillsEngine (5 skills, cooldown-gated) ├── messenger_integration.py KyrexisMessengerIntegration ├── photonic_display.py LaserCrystalPhotonicDisplay ├── particle_detector.py ParticlePairDetector ├── progression_scaling.py ProgressionScalingEngine (×4) ├── agents.py UniversalAgentNetwork (21 agents) └── api/ ├── routes.py FastAPI router /api/v1/kyrexis/* └── app.py Standalone FastAPI app (port 8001) ``` Wired into `src/sovereign/main.py` with a guarded include (same pattern as the Reality Matrix router). ## 3. API reference All endpoints are lazy — call `POST /api/v1/kyrexis/initialize` first; other endpoints return 503 until then. | Method | Path | Purpose | |---|---|---| | POST | `/initialize` | Boot core, skills, messenger, photonic, particle, progression | | GET | `/status` | Core state (qubits, pairs, fidelity, awakening) | | GET | `/quantum/state` | Quantum snapshot | | POST | `/quantum/compute` | Quantum-superposition simulation on a vector | | POST | `/predict` | 20-year forward projection | | POST | `/self-improve` | One exponential-intelligence cycle | | GET | `/skills` | Skill registry + cooldowns | | POST | `/skills/{skill_id}` | Execute a quantum skill | | POST | `/messenger/webhook` | Messenger webhook handler | | POST | `/messenger/send` | Send a message (env token / local mode) | | GET | `/photonic/display` | Display system state | | POST | `/photonic/pulse` | Generate a 775 nm laser pulse (simulated) | | GET | `/particle/state` | Detector state | | POST | `/particle/detect` | Detect pair + verify entanglement | | GET | `/progression/status` | 20-year table + summary | | GET | `/progression/{year}` | Progression at year 1–20 | | POST | `/progression/project` | Project a value with ×4 scaling | | GET | `/agents` | Universal Agent Network status | | POST | `/agents/sync` | Network sync protocol (simulated) | ## 4. Configuration `config/kyrexis.yaml` — core anchors, scaling params, photonic display specs, messenger settings (token via `KYREXIS_PAGE_ACCESS_TOKEN` env), skill gate, agent network flags. ## 5. Buckets (data plane) `buckets/kyrexis-bucket.yaml` — 4 buckets: `kyrexis-state`, `kyrexis-photonic`, `kyrexis-progression`, `kyrexis-messenger` (object lock). Provisioned via `buckets/bucket_bootstrap.py`. ## 6. Deployment - Local: `scripts/start_kyrexis.sh` - Docker: `docker/Dockerfile.kyrexis` + `docker-compose.kyrexis.yml` (core :8000, messenger, photonic services) ## 7. Verification - Tests: `tests/test_kyrexis.py` — 21 tests (core anchors, skills + cooldown, messenger local/webhook, photonic specs + loop, particle tomography + Bell, progression anchors + projection, agent network, API route surface). - Full suite: `pytest tests/` (71 fast tests pass). - Registry: `scripts/verify_registries.py` validates the `kyrexis` module entry. ## 8. Engineering notes - Quantum features are deterministic **simulations** (numpy), not real quantum hardware — deployment needs no QPU. - The spec's 368× 20-year headline is retained as a stated parameter; the engine's honest CAGR math yields ≈323× for `(1.335)^20`. - The Universal Agent Network and progression tables are product-level planning surfaces, not physical predictions.