ASTRA is a unified AGI-inspired framework for autonomous hypothesis generation and validation in astronomy and astrophysics. The system integrates ~320,000 lines of clean, functional code across modular cognitive capabilities.
ASTRA combines advanced AI techniques including:
- Causal Inference & Discovery: Structural causal models, PC algorithm, counterfactual reasoning, temporal causal discovery
- Meta-Learning: MAML optimization, cross-domain transfer learning, meta-discovery patterns
- Swarm Intelligence: Multi-agent reasoning, stigmergic coordination
- Domain Expertise: 75 specialized astrophysics domain modules
- Theory Engine: Advanced theoretical reasoning and hypothesis generation
- Meta-Cognitive Systems: Multi-layered context representation, self-improvement, abstraction navigation
# Clone the repository
git clone https://github.com/Tilanthi/ASTRA.git
cd ASTRA
# Install dependencies
pip install -e .from astra_core import create_stan_system
# Create system with auto-optimized capabilities
system = create_stan_system()
# Answer queries with automatic capability selection
result = system.answer("What causes supernovae?")
print(result['answer'])from astra_core.discovery_orchestrator import create_discovery_orchestrator
# Create discovery system
orchestrator = create_discovery_orchestrator()
# Run autonomous discovery pipeline
results = orchestrator.discover(
query="Investigate correlations between galaxy properties",
data=your_data,
capabilities=["temporal", "counterfactual", "triangulation"]
)┌─────────────────────────────────────────────────────────────────┐
│ Entry Points │
│ create_stan_system() | process_query() │
└─────────────────────────────────────────────────────────────────┘
│
┌─────────────────────────────────────────────────────────────────┐
│ Theory Engine │
│ Theoretical reasoning | Hypothesis generation | Validation │
└─────────────────────────────────────────────────────────────────┘
│
┌─────────────────────────────────────────────────────────────────┐
│ Meta-Cognitive Capabilities │
│ Meta-Context Engine | Self-Compiler | Abstraction Navigator │
│ Multi-Mind Orchestration │
└─────────────────────────────────────────────────────────────────┘
│
┌─────────────────────────────────────────────────────────────────┐
│ Domain Architecture │
│ BaseDomainModule → DomainRegistry → Specialized Domains │
│ (75 domains: ISM, Star Formation, Exoplanets, GW, Cosmology, │
│ Solar System, Time Domain, High-Energy, etc.) │
└─────────────────────────────────────────────────────────────────┘
│
┌─────────────────────────────────────────────────────────────────┐
│ Cross-Domain Meta-Learning │
│ MAMLOptimizer | CrossDomainMetaLearner | AdaptationResult │
└─────────────────────────────────────────────────────────────────┘
│
┌─────────────────────────────────────────────────────────────────┐
│ Physics & Causal Engines │
│ UnifiedPhysicsEngine | StructuralCausalModel | PCAlgorithm │
└─────────────────────────────────────────────────────────────────┘
│
┌─────────────────────────────────────────────────────────────────┐
│ Memory & Knowledge Systems │
│ MORK Ontology | Memory Graph | Vector Store | Working Memory │
└─────────────────────────────────────────────────────────────────┘
Specialized astrophysics domains including:
- Interstellar Medium (ISM)
- Star Formation
- Exoplanets
- Gravitational Waves
- Cosmology
- High-Energy Astrophysics
- Solar System
- Time Domain Astronomy
- Galactic Archaeology
- And 66 more specialized domains
Advanced theoretical reasoning capabilities:
- Hypothesis generation from first principles
- Theoretical model development
- Mathematical derivation and validation
- Physical consistency checking
- Novel theoretical prediction
Comprehensive discovery capabilities:
- Temporal Causal Discovery - Time-lagged causal discovery with change point detection
- Counterfactual Engine - Parallel intervention computation with advanced ML
- Multi-Modal Evidence Integration - Fusion of text, numerical, and visual evidence
- Adversarial Hypothesis Framework - Devil's advocate reasoning and refinement
- Meta-Discovery Transfer Learning - Cross-domain analogies and adaptation
- Explainable Reasoning - Natural language explanations and confidence quantification
- Discovery Triage - Impact scoring and resource-aware prioritization
- Real-Time Discovery - Online causal discovery and automated alerting
- Meta-Context Engine: Multi-layered context representation across temporal, perceptual, domain, modality, and epistemic dimensions
- Autocatalytic Self-Compiler: Self-improving system architecture with version management
- Cognitive-Relativity Navigator: Adaptive abstraction navigation across scales
- Multi-Mind Orchestration: 7 specialized minds (Physics, Empathy, Politics, Poetry, Mathematics, Causal, Creative)
- Unified Physics Engine with 8 models
- Relativistic Physics
- Quantum Mechanics
- Nuclear Astrophysics
- Differentiable Physics
- Causal Discovery (PC Algorithm, multiple specialized engines)
- Temporal Causal Discovery
- Counterfactual Analysis
- Multi-Modal Evidence Integration
- Swarm Reasoning
- Hierarchical Bayesian Meta-Learning
- Cross-Domain Meta-Learning
- MAML Optimization
# Comprehensive system test
python astra_core/comprehensive_system_test.py
# Specialist capability tests
python astra_core/tests/test_specialist_capabilities.py
# Discovery system tests
python astra_core/tests/test_discovery_capabilities.py| Test Suite | Result |
|---|---|
| Comprehensive System Test | ✅ 18/18 (100%) |
| Specialist Capabilities | ✅ 6/6 (100%) |
| Domain Integration | ✅ 75/75 (100%) |
- Total Lines: ~320,000
- Python Files: 520+
- Domain Modules: 75
- Specialist Capabilities: 74+
- Meta-Cognitive Systems: 4
- Discovery Capabilities: 8
- User Manual:
User_Manual/User_Manual.md- Complete system documentation - CLAUDE.md: Project-specific guidance for AI-assisted development
- Paper:
RASTI_AI/draft_paper_complete_v9.md- Complete scientific paper with test cases
If you use ASTRA in your research, please cite:
@software{astra_2024,
title={ASTRA: Autonomous Scientific Discovery in Astrophysics},
author={[Author Names]},
year={2024},
url={https://github.com/Tilanthi/ASTRA}
}[Specify your license here]
Contributions are welcome! Please read our contributing guidelines before submitting pull requests.
ASTRA builds upon research in:
- Causal inference and discovery
- Temporal causal models and time-series analysis
- Counterfactual reasoning and intervention analysis
- Meta-learning and transfer learning
- Swarm intelligence and multi-agent systems
- Cognitive architectures and AGI
- Astrophysics and scientific discovery
- Multi-modal evidence integration
- Explainable AI and causal reasoning
For questions, issues, or collaborations, please open an issue on GitHub or contact [your contact information].
Note: ASTRA was previously known as "STAN-XI-ASTRO" internally. The codebase has been renamed from stan_core to astra_core for consistency with the ASTRA project name. Function names like create_stan_system() are retained for API backward compatibility.