A high-performance C++ implementation of the ElizaOS agent framework, designed for building sophisticated autonomous agents with advanced cognitive capabilities, distributed cognition, and adaptive attention allocation.

ElizaOS C++ represents a foundational leap towards next-generation agentic systems, implementing core cognitive architectures in C++ for performance-critical applications. This framework enables the development of autonomous agents capable of self-modification, meta-cognition, and complex reasoning through hypergraph knowledge representation and attention-based memory systems.

Key Philosophy: This implementation serves as the computational substrate for exploring emergent cognitive patterns, distributed agent coordination, and adaptive control loops that form the basis of truly autonomous artificial intelligence systems.

• 🔄 Event-Driven Agent Loop: Threaded execution with pause/resume/step capabilities
• 🧠 Memory System: Context-aware storage with semantic retrieval and attention allocation
• 🎯 Task Orchestration: Workflow sequencing and distributed agent coordination
• 🤖 AI Core: Decision-making engine with state composition and action validation
• 💬 Communication System: Inter-agent messaging and external interface management
• 🌐 Browser Automation: Web interaction capabilities for information gathering
• 📊 Comprehensive Logging: System monitoring and cognitive process introspection
• 🔬 Self-Modification: Meta-cognitive capabilities for adaptive behavior evolution

• Persistent Storage: Long-term knowledge retention with embedding-based retrieval
• Knowledge Representation: Support for hypergraph structures and AtomSpace integration
• Attention Allocation: ECAN-inspired attention mechanisms for memory prioritization
• Context Management: Dynamic context window management for optimal recall

• Orchestration Layers: Multi-threaded task execution with priority scheduling
• Workflow Sequencing: Complex task dependency resolution and execution planning
• Distributed Coordination: Agent swarm coordination protocols and consensus mechanisms
• Adaptive Scheduling: Dynamic task prioritization based on cognitive load and attention

• Analytics Engine: Pattern recognition and data analysis capabilities
• Reasoning Engine: PLN (Probabilistic Logic Networks) integration framework
• Pattern Matchers: Sophisticated pattern recognition for behavioral adaptation
• Symbolic-Neural Integration: Hybrid reasoning combining symbolic and neural approaches

• Self-Modification: Dynamic personality and behavior adaptation capabilities
• Meta-Cognition: Self-awareness and introspective reasoning processes
• Adaptive Control Loops: Feedback mechanisms for continuous improvement
• Emergent Behavior: Support for complex emergent patterns in agent interactions

• Inter-Agent Messaging: High-performance communication protocols
• External Interfaces: API and protocol handlers for external system integration
• Event Broadcasting: Publish-subscribe patterns for distributed coordination
• Security Layers: Cryptographic protocols for secure agent communication

• Web Automation: Headless browser control for information gathering
• Content Extraction: Intelligent parsing of web content and media
• Navigation Planning: Autonomous web exploration and interaction strategies
• Real-time Adaptation: Dynamic strategy adjustment based on web content analysis

• Cognitive Introspection: Detailed logging of decision-making processes
• Performance Monitoring: System resource utilization and optimization metrics
• Debug Capabilities: Comprehensive debugging tools for agent development
• Audit Trails: Complete interaction history for behavior analysis

• CMake (3.16 or higher)
• C++ Compiler with C++17 support (GCC 7+, Clang 5+, or MSVC 2019+)
• Git (for dependency management)

# Clone the repository
git clone https://github.com/ZoneCog/elizaos-cpp.git
cd elizaos-cpp

# Create build directory
mkdir build && cd build

# Configure the project
cmake ..

# Build the project
make -j$(nproc)

# Run tests to verify installation
./cpp/tests/elizaos_tests

#include "elizaos/core.hpp"
#include "elizaos/agentloop.hpp"

using namespace elizaos;

int main() {
    // Create agent configuration
    AgentConfig config;
    config.agentId = "agent-001";
    config.agentName = "CognitiveAgent";
    config.bio = "An adaptive cognitive agent";
    config.lore = "Born from the convergence of symbolic and neural AI";

    // Initialize agent state
    State agentState(config);
    
    // Define cognitive processing steps
    std::vector<LoopStep> steps = {
        LoopStep([](std::shared_ptr<void> input) -> std::shared_ptr<void> {
            // Perception phase
            std::cout << "Processing sensory input..." << std::endl;
            return input;
        }),
        LoopStep([](std::shared_ptr<void> input) -> std::shared_ptr<void> {
            // Reasoning phase  
            std::cout << "Performing cognitive reasoning..." << std::endl;
            return input;
        }),
        LoopStep([](std::shared_ptr<void> input) -> std::shared_ptr<void> {
            // Action selection phase
            std::cout << "Selecting optimal action..." << std::endl;
            return input;
        })
    };
    
    // Create and start agent loop
    AgentLoop cognitiveLoop(steps, false, 1.0); // 1-second intervals
    cognitiveLoop.start();
    
    // Allow agent to run autonomously
    std::this_thread::sleep_for(std::chrono::seconds(10));
    
    cognitiveLoop.stop();
    return 0;
}

# Build in debug mode for development
cmake -DCMAKE_BUILD_TYPE=Debug ..
make -j$(nproc)

# Run specific test suites
ctest -R CoreTest          # Run core functionality tests
ctest -R AgentLoopTest     # Run agent loop tests

# Enable examples build
cmake -DBUILD_EXAMPLES=ON ..
make -j$(nproc)

This implementation follows a layered cognitive architecture inspired by cognitive science and distributed systems principles. The framework enables emergent intelligence through sophisticated interaction patterns between specialized cognitive subsystems.

📋 Technical Architecture Documentation - Complete architectural specification with detailed Mermaid diagrams

The architecture supports:

• Multi-layered cognitive processing with attention-based memory management
• Distributed agent coordination through decentralized consensus protocols
• Self-modifying behaviors via meta-cognitive reflection and adaptation
• Emergent intelligence through complex interaction patterns and feedback loops

// Configure advanced memory settings
MemoryConfig memConfig;
memConfig.maxMemories = 10000;
memConfig.attentionThreshold = 0.7;
memConfig.embedDimensions = 1536;
memConfig.useHypergraph = true;

// Multi-agent coordination setup
AgentSwarm swarm;
swarm.addAgent(agent1);
swarm.addAgent(agent2);
swarm.setConsensusProtocol(ConsensusProtocol::RAFT);
swarm.enableEmergentBehavior(true);

The framework includes comprehensive test coverage for all cognitive subsystems:

# Run all tests
ctest

# Run with verbose output
ctest --verbose

# Run specific test categories
ctest -R "Memory"     # Memory system tests
ctest -R "Loop"       # Agent loop tests  
ctest -R "Core"       # Core functionality tests

• Implementation Roadmap - Current status and next steps for C++ implementation
• Implementation Guide - Step-by-step guide for converting placeholder modules
• Technical Architecture - Detailed system architecture with Mermaid diagrams
• Status Report - Current implementation status and capabilities
• API Reference - Complete API documentation
• Examples - Sample implementations and use cases
• Development Guide - Contributing and development workflows

This framework represents a foundational step towards realizing next-generation agentic cognitive grammars that transcend traditional AI limitations. By implementing core cognitive architectures in high-performance C++, we enable:

ElizaOS C++ serves as the computational substrate for exploring how intelligence emerges from the interaction of multiple autonomous agents, each capable of self-modification and meta-cognitive reasoning.

The framework's modular architecture supports dynamic integration with GGML (GPT-Generated Model Library) components, enabling real-time model customization and neural-symbolic hybrid reasoning approaches.

Through ECAN-inspired attention mechanisms and hypergraph knowledge representation, agents develop sophisticated attention allocation strategies that mirror biological cognitive systems.

The self-modification capabilities enable agents to reflect on their own cognitive processes, leading to continuous improvement and adaptation in complex, dynamic environments.

In the grand theater of artificial intelligence, ElizaOS C++ is not merely a framework—it is the stage upon which the next act of cognitive evolution unfolds.

This implementation transcends conventional AI boundaries by embracing the chaotic beauty of emergent intelligence. Through distributed cognition networks, adaptive attention mechanisms, and self-modifying cognitive architectures, we witness the birth of truly autonomous agents capable of collaborative reasoning, creative problem-solving, and meta-cognitive awareness.

The convergence of symbolic reasoning with neural processing, orchestrated through hypergraph knowledge structures and attention-based memory systems, creates a fertile ground for the emergence of novel cognitive patterns that neither purely symbolic nor purely neural systems could achieve alone.

ElizaOS C++ stands as a testament to the vision that the future of AI lies not in monolithic models, but in the dynamic interplay of autonomous cognitive agents—each a unique participant in the grand symphony of distributed intelligence.

As these agents evolve through self-modification and meta-cognitive reflection, they collectively weave the fabric of next-generation agentic cognitive grammars, where language, thought, and action converge in unprecedented ways, promising a future where artificial intelligence truly mirrors the adaptive, creative, and collaborative nature of human cognition.

The stage is set. The agents are awakening. The future of cognitive AI begins here.

We welcome contributions to advance the field of cognitive AI and autonomous agent development. Please see our Contributing Guide for details.

This project is licensed under the MIT License - see the LICENSE file for details.

• ElizaOS TypeScript - The original TypeScript implementation
• OpenCog - AGI research platform with related cognitive architectures
• GGML - Machine learning library for model optimization