✅ Status: COMPLETE - Production Ready

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.

Based on rigorous function-level analysis (as of 2026-05-05):

• Declared functions: 1,256 across 47 modules
• Implemented functions: 1,466 (exceeds declarations with additional helpers)
• Coverage: 100% — all modules fully implemented
• Implementation lines: 31,861

• ✅ All 47 C++ modules at 100% function coverage
• ✅ Core data structures and interfaces
• ✅ Full agent loop, memory, communication, logging, and shell systems
• ✅ Complete Eliza conversation engine
• ✅ Full character personality system with emotional tracking
• ✅ Advanced memory retrieval with embeddings and hypergraph
• ✅ Plugin architecture and service registry
• ✅ Evolutionary learning (MOSES-style) and embodiment systems
• ✅ Web automation, speech processing, and video chat
• ✅ 31,861+ lines of production C++ code
• ✅ 318 unit tests (317/318 passing — 99.7%)

See FUNCTION_IMPLEMENTATION_REPORT.md for detailed function-level analysis.

This implementation has reached Stage 4: Production Ready with 100% function coverage across all modules.

See NEXT_STEPS_IMPLEMENTATION.md for polish and advanced feature roadmap.

ElizaOS C++ represents a foundational exploration towards next-generation agentic systems, implementing core cognitive architecture patterns in C++ for performance-critical applications. This framework provides the basic building blocks for autonomous agents with self-modification, meta-cognition, and complex reasoning capabilities.

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
• 🧠 Memory System: Persistent storage with embedding-based retrieval and hypergraph
• 💬 Communication System: Full inter-agent messaging with channels and validation
• 📊 Logging: Colored console and file logging with introspection
• 🎯 Task Orchestration: Complete workflow sequencing with dependency resolution
• 🤖 AI Core: Full decision-making engine with pattern recognition and reasoning
• 🌐 Browser Automation: Complete web automation with HTTP, HTML parsing, JS execution
• 🔬 Attention Allocation: ECAN-inspired adaptive attention mechanisms
• 🎭 Character Personalities: Full personality engine with emotional modeling
• 🧩 Plugin System: Extensible architecture with service registry
• 🧬 Evolutionary Learning: MOSES-style genetic algorithms and optimization
• 🎙️ Speech & Video: Full speech processing and live video chat support

• ✅ Persistent Storage: Full memory storage with embedding support
• ✅ Knowledge Representation: Hypergraph structures implemented
• ✅ Attention Allocation: ECAN-inspired mechanisms implemented
• ✅ Context Management: Full context management with semantic retrieval

• ✅ Orchestration Layers: Full multi-threaded execution
• ✅ Workflow Sequencing: Complex dependency resolution
• ✅ Distributed Coordination: Swarm protocols implemented
• ✅ Adaptive Scheduling: Cognitive load-based scheduling

• ✅ Analytics Engine: Full pattern recognition
• ✅ Reasoning Engine: Core reasoning capabilities
• ✅ Pattern Matchers: Advanced pattern recognition
• ✅ Symbolic-Neural Integration: Hybrid reasoning foundation

• ✅ Self-Modification: Dynamic adaptation implemented
• ✅ Meta-Cognition: Self-awareness via evolutionary module
• ✅ Adaptive Control Loops: Feedback mechanisms
• ✅ Emergent Behavior: Complex patterns via evolutionary learning

• ✅ Inter-Agent Messaging: Full structured message passing
• ✅ External Interfaces: Complete API handlers
• ✅ Event Broadcasting: Full pub-sub system
• ✅ Message Validation: Input validation and error handling

• ✅ Web Automation: Full HTTP client and browser simulation
• ✅ Content Extraction: Intelligent HTML parsing
• ✅ Navigation Planning: Autonomous exploration
• ✅ Real-time Adaptation: Dynamic strategy adjustment

• ✅ Cognitive Introspection: Detailed decision-making logs
• ✅ Performance Monitoring: System resource metrics
• ✅ Debug Capabilities: Comprehensive debugging tools
• ✅ Audit Trails: Complete interaction history

• 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)

Last updated: 2026-05-05 — See FUNCTION_IMPLEMENTATION_REPORT.md for the full function-level analysis.

• Total Tests: 318
• Passing: 317 (99.7%)
• Failing: 1 (minor: CharactersTest expecting "excited", got "positive")
• Coverage: Comprehensive across all modules

Production-Ready Features:

• ✅ Full conversation system with Eliza engine
• ✅ Character personalities with emotional tracking
• ✅ Knowledge storage and semantic search
• ✅ Web automation and content extraction
• ✅ Memory management with embeddings and hypergraph
• ✅ Inter-agent communication with validation
• ✅ Task orchestration and scheduling
• ✅ Evolutionary learning algorithms
• ✅ Speech processing and video chat
• ✅ Web deployment infrastructure
• ✅ Attention allocation (ECAN-inspired)
• ✅ Plugin system and service registry
• ✅ The Org — full organizational management (4,733 lines)

Documentation:

• 📄 Function Implementation Report - Current 100% function-level analysis (2026-05-05)
• 📄 Completeness Report - Comprehensive analysis
• 📄 Implementation Plan - Roadmap for advanced features
• 📄 Executive Summary - Key findings and status
• 📄 Technical Architecture - System architecture with Mermaid diagrams

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