This repository contains the OpenCog ecosystem components, comprehensive build automation, and advanced introspection & ontogenesis frameworks.

Launch a complete OpenCog development environment in your browser with zero configuration:

• 🧠 Complete OpenCog Ecosystem - AtomSpace, CogServer, and all core components
• 🐍 Python 3.10 Environment - Pre-configured with scientific computing libraries
• 📦 Guix Package Manager - Reproducible builds with automatic fallback
• 🔧 Automated Build System - One-command build of entire ecosystem
• 🌐 Port Forwarding - Direct browser access to OpenCog services
• 📚 Comprehensive Documentation - Guides, troubleshooting, and examples
• 🛠️ VS Code Integration - Full IDE with C++, Python, and Guile support

1. Click the Gitpod button above ⬆️
2. Wait 2-3 minutes for automatic environment setup
3. Run opencog_status to verify installation
4. Run build-opencog to build all components
5. Run demos to see available examples

Once deployed, access OpenCog services via Gitpod's port forwarding:

• Complete Gitpod Guide - Comprehensive deployment documentation
• Troubleshooting Guide - Solutions to common issues
• OpenCog Wiki - Official OpenCog documentation

We've implemented a comprehensive GitHub Actions workflow that builds and installs all OpenCog components according to dependency diagrams with matrix discovery of hidden dependencies.

• 40+ Component Coverage: Builds the entire OpenCog ecosystem
• 9 Dependency Layers: Foundation → Core → Logic → Cognitive → Advanced → Learning → Language → Robotics → Integration
• Fail-Never Approach: Discovers all possible build issues without stopping
• Matrix Build Strategy: Parallel builds within dependency categories
• Hidden Dependency Discovery: Systematic identification of undocumented dependencies
• Comprehensive Reporting: Automated issue creation and detailed build summaries

1. Go to Actions tab
2. Select "OpenCog Dependency Build Matrix"
3. Click "Run workflow"
4. Choose build category (or "all" for complete build)

• Complete Documentation
• Quick Start Guide
• Dependency Diagrams

NEW: Self-aware, self-evolving systems based on recursive introspection and genetic kernels.

• Introspection Framework: Recursive self-awareness for copilot agents

  • Meta-cognitive introspection: self.copilot(n) = introspection.self.copilot(n-1)
  • Self-optimization through grip maximization
  • Differential operators (chain, product, quotient rules)
  • Ontogenetic development stages

• Ontogenesis Framework: Self-generating mathematical kernels

  • Self-generation via chain rule composition
  • Genetic operators (crossover, mutation)
  • Population evolution with fitness evaluation
  • Domain-specific kernels (consciousness, physics, mathematics)

# Install dependencies
pip3 install numpy

# Run introspection example
python3 examples/introspection/basic_introspection.py

# Run ontogenesis example
python3 examples/ontogenesis/self_generation.py

# Run evolution example
python3 examples/ontogenesis/evolution_example.py

• Introspection Agent Specification
• Ontogenesis Agent Specification
• Examples and Usage
• Introspection Analysis

This repository contains OpenCog components organized by functional categories as defined in the dependency diagrams.