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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.
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