Private, Uncensored, Open-Source AI Operating System
MAGNATRIX-OS is a self-contained AI operating system built entirely from Python standard library. Zero external dependencies. 200 modules covering every layer of AI infrastructure: from neural architecture search to quantum simulation, from HFT trading to autonomous vehicle control, from federated learning to recursive self-improvement.
| Metric | Value |
|---|---|
| Total Modules | 200 |
| Boot Success | 100% (0 failed) |
| Boot Time | ~11.7 seconds |
| Dependencies | Python stdlib only |
| External Packages | 0 |
| Lines of Code | ~200,000+ |
| Classes | 600+ |
| License | MIT (Open Source) |
+--------------------------------------------------+
| MAGNATRIX-OS v1.0.0 |
+--------------------------------------------------+
| Application Layer: Dashboard, CLI, HFT, AR/VR |
+--------------------------------------------------+
| AI Layer: MoA, NAS, Swarm, Federated Learning |
+--------------------------------------------------+
| Integration Layer: Event Bus, Message Router |
+--------------------------------------------------+
| Core Layer: Memory, Knowledge, Constitution |
+--------------------------------------------------+
| Infrastructure: Database, Cache, Security, Mesh |
+--------------------------------------------------+
| Hardware: Profiler, Edge Deploy, Quantization |
+--------------------------------------------------+
auth, config, logging, cache, security, database, filesystem, process, container, event, message, distributed, mesh, federation, replication, backup, encryption, compression, snapshot, log_analysis
local_llm, multi_model, model_router, ai_model_registry, model_catalog, ai_training, nlq, analytics, search_engine, feature_store, model_serving, prompt_chaining, longterm_memory, code_reasoning, intent_orchestrator, temporal_workflow, self_healing, agent_memory, knowledge_ingestion, federated_learning, xai, synthetic_data, neural_search, quantum_sim, model_drift
data_lake, etl_pipeline, stream_processing, data_quality, data_connectors, benchmark, performance, auto_doc, test_suite, dashboard, dashboard_pro, boot_optimizer, auto_benchmark, compliance, data_lineage, weather, smart_city, supply_chain, dna, social_network
hft_trading, exchange, live_exchange, risk, portfolio, execution, strategy, tick, candle, order, position, trade_report, backtest, market_data, sentiment
acs_policy, acs_intervention, audit, audit_forensics, intrusion_detection, zerotrust, secret_rotation, key_management, secret, concealment, constitution, consensus, deception, legal, compliance, zeroday, intrusion, patch_gen, spam, blockchain
hardware_profiler, edge_inference, mobile_companion, gguf_converter, hardware_edge, ar_vr, autonomous, medical, ar_vr_interface, quantum_sim
cicd, bluegreen, canary, chaos, docker_compose, deployment, rolling, blue_green, canary_release, installer, bootstrap, system_integration, integration_hub, live_deployment, patch_generator
graphql, grpc_transport, websocket, voice, gesture, pwa, cli_tui, network, transport, federation_sync
swarm, task_distributor, consensus_engine, node_registry, emergent_behavior, goal_formation, rsi_v2, multimodal, recommendation, blockchain_consensus, swarm_intelligence, moa, cross_domain, transfer, skill_registry
test_suite, regression, coverage, integration_test, module_test, smoke_test, stress_test, security_audit, penetration_test, regression_guard
# Clone
git clone https://github.com/Magnatrix-Lab/MAGNATRIX-OS.git
cd MAGNATRIX-OS
# One-command install
bash install.sh
# Or manual
python magnatrix.py start
# Boot test
python -c "from magnatrix import SystemManager; m = SystemManager('.'); print(m.boot())"Production dashboard runs on port 8080 with:
- Real-time module status grid
- SSE streaming for live updates
- System metrics (CPU, memory, event rate)
- Control panel (restart, test, shutdown)
- Mixture of Agents (MoA): Multi-model aggregation with reference + aggregator pattern
- Swarm Intelligence: Distributed task execution with federated voting
- HFT Trading Engine: 3 built-in strategies (MA, RSI, Breakout) with risk management
- Self-Healing: Auto-restart failed modules with backoff
- Constitution Governance: Goal alignment, deception detection, value mutation guard
- Recursive Self-Improvement: Auto-detect code issues, generate patches, test, deploy
- Federated Learning: Train across nodes without sharing raw data
- Explainability (XAI): Decision tracing, attribution mapping, counterfactual analysis
- Edge Deployment: Hardware-aware model selection and quantization
| Module | Purpose | Key Classes |
|---|---|---|
magnatrix.py |
Main entry point | SystemManager, ModuleRegistry |
dashboard_production_native.py |
Real-time dashboard | DashboardServer, MetricsCollector |
hft_trading_engine_native.py |
Trading engine | TradingEngine, RiskManager, ExecutionEngine |
moa_integration_native.py |
Mixture of Agents | MOAEngine, MOAReferenceEngine, MOAAggregator |
swarm_intelligence_native.py |
Distributed AI | SwarmIntelligence, TaskDistributor, ConsensusEngine |
federated_learning_native.py |
Distributed training | FederatedLearningEngine, GradientAggregator |
agent_memory_system_native.py |
Agent memory | AgentMemory, EpisodicMemory, SemanticMemory |
knowledge_ingestion_pipeline_native.py |
Knowledge pipeline | KnowledgeIngestionPipeline, KnowledgeGraphBuilder |
recursive_self_improvement_v2_native.py |
Self-improvement | RecursiveSelfImprovement, PatchGenerator |
emergent_goal_formation_native.py |
Goal formation | EmergentGoalFormation, CuriosityEngine |
explainability_xai_native.py |
XAI | ExplainabilityEngine, ReasoningChain, AttributionMapper |
grpc_transport_native.py |
gRPC transport | GRPCTransport, GRPCServer, GRPCClient |
system_integration_hub_native.py |
Integration hub | SystemIntegrationHub |
live_deployment_manager_native.py |
Deployment | LiveDeploymentManager, BlueGreenDeployer, CanaryDeployer |
auto_benchmark_leaderboard_native.py |
Benchmarking | AutoBenchmarkEngine, Leaderboard |
- Boot: 200 modules in ~11 seconds
- Memory: ~50MB base footprint
- Event throughput: 10,000+ events/sec (EventBus)
- Message routing: sub-millisecond latency
This is a research project. Contributions welcome. All code must use Python stdlib only.
MIT License - Open Source, Uncensored, Free Forever.
Built with pure Python. No cloud required. No API keys. No censorship.