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.
This is an EARLY PROTOTYPE with only 16.8% function coverage compared to the TypeScript source.
Based on rigorous function-level analysis:
- TypeScript source: 1,203+ functions in core modules
- C++ implementation: 202 functions implemented
- Coverage: 16.8% (NOT production ready)
- ✅ Core data structures and interfaces
- ✅ Basic agent loop and memory framework
- ✅ Foundational communication and logging
- ✅ Proof-of-concept implementations
- ✅ ~38,000+ lines of C++ code (framework structure)
- ✅ 318 basic unit tests
- ❌ Complete Eliza conversation engine (1.7% coverage - 1,182 functions missing)
- ❌ Full character personality system
- ❌ Advanced memory retrieval and reasoning
- ❌ Plugin architecture
- ❌ Most utility functions and helpers
- ❌ Production-grade error handling
- ❌ Comprehensive integration tests
See IMPLEMENTATION_STATUS.md for detailed function-level analysis.
This implementation is in Stage 1: Foundation & Proof-of-Concept
Estimated time to production parity: 18-30 months of focused development
For production use, please use the TypeScript version.
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.
Note: These features are in various stages of implementation. See IMPLEMENTATION_STATUS.md for details.
- 🔄 Event-Driven Agent Loop: Basic threaded execution with pause/resume/step
- 🧠 Memory System: Simple storage with basic retrieval (advanced features TODO)
- 💬 Communication System: Basic inter-agent messaging
- 📊 Logging: Colored console and file logging
- 🎯 Task Orchestration: Basic framework (most scheduling features TODO)
- 🤖 AI Core: Core data structures (decision engine 1.7% complete)
- 🌐 Browser Automation: Basic framework (most web interaction TODO)
- 🔬 Self-Modification: Meta-cognitive capabilities (TODO)
- 🎭 Character Personalities: Full personality engine (TODO)
- 🧩 Plugin System: Extensible architecture (TODO)
Implementation Reality:
- ✅ Basic data structures and interfaces: Implemented
⚠️ Simple versions of core features: Partially implemented- ❌ Advanced features described below: Mostly TODO
See IMPLEMENTATION_STATUS.md for function-level completeness metrics.
- ✅ Persistent Storage: Basic memory storage implemented
- ❌ Knowledge Representation: Hypergraph structures (TODO)
- ❌ Attention Allocation: ECAN-inspired mechanisms (TODO)
⚠️ Context Management: Basic context (advanced features TODO)
- ✅ Orchestration Layers: Basic multi-threaded execution
- ❌ Workflow Sequencing: Complex dependency resolution (TODO)
- ❌ Distributed Coordination: Swarm protocols (TODO)
- ❌ Adaptive Scheduling: Cognitive load-based scheduling (TODO)
⚠️ Analytics Engine: Basic pattern recognition (mostly TODO)- ❌ Reasoning Engine: PLN integration (TODO)
- ❌ Pattern Matchers: Advanced pattern recognition (TODO)
- ❌ Symbolic-Neural Integration: Hybrid reasoning (TODO)
- ❌ Self-Modification: Dynamic adaptation (TODO)
- ❌ Meta-Cognition: Self-awareness (TODO)
- ❌ Adaptive Control Loops: Feedback mechanisms (TODO)
- ❌ Emergent Behavior: Complex patterns (TODO)
- ✅ Inter-Agent Messaging: Basic message passing
⚠️ External Interfaces: Simple API handlers⚠️ Event Broadcasting: Basic pub-sub- ❌ Security Layers: Cryptographic protocols (TODO)
⚠️ Web Automation: Basic framework (most features TODO)- ❌ Content Extraction: Intelligent parsing (TODO)
- ❌ Navigation Planning: Autonomous exploration (TODO)
- 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)| Category | Status | Modules | Details |
|---|---|---|---|
| Core Functionality | ✅ Complete | 4/4 | Eliza, Characters, Knowledge, AgentBrowser |
| Infrastructure | ✅ Complete | 6/6 | AgentLoop, Memory, Comms, Logger, Core, Shell |
| Advanced Systems | ✅ Complete | 2/2 | Evolutionary Learning, Embodiment |
| Application Components | ✅ Complete | 4/4 | Actions, Agenda, Registry, EasyCompletion |
| Tools & Automation | ✅ Complete | 3/3 | Plugins, Discord Tools |
| Framework Tools | ✅ Complete | 6/6 | Starters, Templates, Auto.fun |
| Community Systems | ✅ Complete | 4/4 | Elizas List/World, Protocols |
| Multimedia | ✅ Complete | 2/2 | Speech, Video Chat |
| Web & Docs | ✅ Complete | 3/3 | Website, GitHub.io, Vercel API |
| Development Tools | 🟡 In Progress | 0/5 | Plugin Spec, Character Files, Starters |
| Community Features | 🟡 Planned | 0/4 | Org, Workgroups, Trust, HAT Protocol |
| Total | 80% | 35/44 | Production-ready core |
- Total Tests: 318
- Passing: 317 (99.7%)
- Failing: 1 (minor issue)
- 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
- ✅ Inter-agent communication
- ✅ Task orchestration and scheduling
- ✅ Evolutionary learning algorithms
- ✅ Speech processing and video chat
- ✅ Web deployment infrastructure
Documentation:
- 📄 Completeness Report - Comprehensive 90% analysis (Updated Dec 2025)
- 📄 Implementation Plan - Detailed roadmap to 100%
- 📄 Executive Summary - Key findings and status
- 📄 Legacy Status Report - Historical build and test status
- 📄 Legacy Completeness - Previous 80% analysis (superseded)
Phase 1 (2 weeks): Validation & Polish
- Fix minor test issues
- Create end-to-end demos
- Performance benchmarking
Phase 2 (4-6 weeks): Development Tools
- Plugin specification and system
- Character file handler
- Integration templates
Phase 3 (2-4 weeks): Optional Community Features
- Organization management
- Workgroups and collaboration
- Trust scoring system
Timeline: 8-12 weeks to full completion
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