A comprehensive collection of research, experiments, and production-ready implementations in AI/ML agents, decentralized computing, blockchain integration, and GPU performance optimization.
- Autonomous Agent Architectures: Design patterns for self-managing AI agents
- Multi-Agent Collaboration: Distributed decision-making and consensus mechanisms
- Agent Learning & Adaptation: Reinforcement learning and knowledge management systems
- Natural Language Processing: Advanced NLP for agent communication and reasoning
- AI Billing & Payment Systems: Decentralized billing with cryptographic verification
- Distributed Systems Architecture: Scalable, fault-tolerant system design
- Blockchain Integration: Smart contracts, DeFi protocols, and Web3 applications
- IPFS Storage Solutions: Decentralized content addressing and storage
- Decentralized Identity: Self-sovereign identity and authentication systems
- Cross-Chain Interoperability: Multi-blockchain communication protocols
- LLM Performance Analysis: Real-time GPU profitability calculations
- Model Optimization: Token-per-second (TPS) optimization and cost analysis
- GPU Resource Management: Multi-GPU configurations and cost optimization
- Cloud GPU Integration: Nebula Block and other cloud GPU providers
research/
├── agent/ # AI Agent Research & Documentation
│ ├── AI_Billing_Agent_Pipeline.md # Implementation pipeline for AI billing agents
│ ├── Future_of_AI_Billing_Agent.md # Future research directions and roadmap
│ └── Research_Roadmap.md # Comprehensive research roadmap and phases
├── sample_code/ # Production-Ready Implementations
│ ├── blockchain/ # Ethereum blockchain utilities
│ │ ├── eth_helper.py # Ethereum node health checker
│ │ ├── README.md # Blockchain documentation
│ │ └── requirements.txt # Blockchain dependencies
│ ├── llm_perfomance/ # LLM Performance Calculator
│ │ ├── gpu_profit_calculator.py # Main FastAPI application
│ │ ├── gpu_profit_calculator_clean.py # Clean version
│ │ ├── update_model_pricing.py # Model pricing updates
│ │ ├── update_openrouter_pricing.py # OpenRouter integration
│ │ ├── scripts/ # Database and utility scripts
│ │ ├── docs/ # Comprehensive documentation
│ │ ├── templates/ # Web interface templates
│ │ └── requirements.txt # LLM calculator dependencies
│ ├── mcp_nebula_block/ # MCP Server for Nebula Block
│ │ ├── src/mcp_server_nebula_block/ # MCP server implementation
│ │ ├── Dockerfile # Container configuration
│ │ ├── pyproject.toml # Project configuration
│ │ └── README.md # MCP server documentation
│ ├── nebula_block_storage/ # Nebula Block Object Storage
│ │ ├── nebula_block_example.py # S3-compatible storage client
│ │ ├── config.py # Configuration management
│ │ ├── requirements.txt # Storage dependencies
│ │ └── README.md # Storage documentation
│ ├── team_billing/ # Team Billing API
│ │ ├── server.py # FastAPI team billing application
│ │ ├── test_server.py # Testing utilities
│ │ ├── Dockerfile # Container configuration
│ │ ├── requirements.txt # Billing dependencies
│ │ └── readme.md # Billing documentation
│ └── webhook/ # Webhook Integration Server
│ ├── webhook_server.py # Main webhook server
│ ├── developer_server.py # Developer verification server
│ ├── mock_mission_request.py # Mock request utilities
│ ├── requirements.txt # Webhook dependencies
│ └── readme.md # Webhook documentation
├── LICENSE # Project license
└── README.md # This file
- Python 3.8+
- Git
- Docker (optional, for containerized deployments)
-
Clone the repository:
git clone https://github.com/flyworker/research.git cd research -
Set up Python environment:
python -m venv venv source venv/bin/activate # On Unix/macOS # or .\venv\Scripts\activate # On Windows
-
Install project dependencies:
# Install dependencies for specific components as needed pip install -r sample_code/[component]/requirements.txt
-
LLM Performance Calculator:
cd sample_code/llm_perfomance pip install -r requirements.txt python gpu_profit_calculator.py # http://localhost:8001
-
Team Billing API:
cd sample_code/team_billing pip install -r requirements.txt python server.py # http://localhost:8000
-
Webhook Server:
cd sample_code/webhook pip install -r requirements.txt export ACCESS_TOKEN=demo-access-token # optional; defaults to this value python webhook_server.py # http://localhost:8000
-
Ethereum Blockchain Helper:
cd sample_code/blockchain pip install -r requirements.txt python eth_helper.py -
Nebula Block Storage Example:
cd sample_code/nebula_block_storage pip install -r requirements.txt cp sample_env .env && $EDITOR .env python nebula_block_example.py
-
MCP Nebula Block server: See
sample_code/mcp_nebula_block/README.mdfor UV/Docker usage.
- Autonomous Architecture: Self-managing agents with state persistence
- Multi-Agent Communication: Event-driven communication protocols
- Learning & Adaptation: Reinforcement learning with memory systems
- Decentralized Billing: Cryptographic payment verification systems
- Ethereum Node Health: Comprehensive RPC endpoint testing
- Smart Contract Integration: DeFi protocol interactions
- Cross-Chain Operations: Multi-blockchain support
- Web3 Security: Secure transaction handling and verification
- Real-time Profitability: Live GPU cost-benefit analysis
- Multi-GPU Support: H100, A100, RTX 3090, RTX 3080 configurations
- Model Optimization: TPS calculations and cost optimization
- Cloud Integration: Nebula Block and other cloud GPU providers
- IPFS Integration: Content-addressed storage solutions
- S3-Compatible APIs: Nebula Block object storage
- Data Persistence: Distributed data management
- Content Addressing: Immutable data references
- AI Billing Agent Pipeline - Comprehensive implementation guide for decentralized billing systems
- Future of AI Billing Agent - Research roadmap and future directions
- Research Roadmap - Detailed research phases and deliverables
- LLM Performance Calculator - GPU profitability and performance analysis
- Blockchain Utilities - Ethereum node health and blockchain integration
- Nebula Block Storage - S3-compatible object storage implementation
- Team Billing API - FastAPI team management and billing system
- Webhook Server - Event-driven webhook integration patterns
- MCP Nebula Block - Model Context Protocol server for GPU resources
FastAPI-based GPU profitability analysis system
- Real-time profit/loss calculations for LLM inference
- Multi-GPU support (H100, A100, RTX 3090, RTX 3080)
- Model database with pricing and TPS data
- OpenRouter API integration for live pricing
- SQLite database with comprehensive model configurations
Key Features:
- GPU cost optimization and comparison
- Model performance analysis
- Real-time pricing updates
- Web-based interface with settings management
Ethereum blockchain interaction and health monitoring
- Comprehensive RPC endpoint testing
- Network identification and chain ID verification
- Wallet balance and block information queries
- RPC method validation and health reporting
Key Features:
- Multi-network support (Mainnet, Testnet, etc.)
- Detailed health reports with broken function detection
- Wallet balance monitoring
- Block information retrieval
FastAPI team management and billing system
- Team creation and member management
- Usage tracking and resource monitoring
- Invoice generation and payment processing
- SQLModel ORM with SQLite database
Key Features:
- Team invitation system
- Usage-based billing calculations
- Invoice generation and management
- RESTful API with comprehensive endpoints
Event-driven webhook server for agent communication
- User verification endpoints
- Developer verification with enhanced features
- Health monitoring and status checking
- Bearer token authentication
Key Features:
- Verification request/response models
- Developer-specific verification workflows
- Health check endpoints
- Mock request utilities for testing
S3-compatible object storage client
- File upload and download operations
- Bucket listing and management
- Presigned URL generation
- Environment-based configuration
Key Features:
- S3-compatible API interface
- Secure file operations
- Temporary access URL generation
- Comprehensive error handling
Model Context Protocol server for GPU resources
- GPU instance discovery and management
- Region-based GPU filtering
- GPU type-specific queries
- FastMCP server implementation
Key Features:
- GPU resource enumeration
- Regional availability checking
- GPU type filtering
- MCP protocol compliance
This repository serves as a comprehensive platform for:
- Autonomous agent architecture design and implementation
- Basic blockchain integration and smart contract development
- IPFS implementation and content addressing strategies
- Agent learning systems and adaptation mechanisms
- Multi-agent collaboration and consensus mechanisms
- Distributed decision-making and conflict resolution
- Decentralized identity management and authentication
- Advanced agent communication protocols
- Cross-platform integration and API standardization
- Performance optimization and caching mechanisms
- AI agent marketplace architecture
- Advanced scaling strategies
- Security hardening and audit mechanisms
- Monitoring systems and maintenance procedures
- Production deployment strategies
- Support processes and documentation
- Backend: FastAPI, SQLModel, SQLite, PostgreSQL
- Blockchain: Web3.py, Ethereum, Smart Contracts
- Storage: IPFS, S3-compatible APIs, Nebula Block
- GPU: CUDA, PyTorch, TensorFlow, Cloud GPU providers
- Deployment: Docker, Uvicorn, ASGI servers
- Follow FastAPI best practices for API development
- Use async/await patterns for I/O operations
- Implement comprehensive error handling
- Maintain type hints throughout the codebase
- Write comprehensive documentation for all components
We welcome contributions from researchers, developers, and enthusiasts! This is a research-focused repository, and we encourage:
- Research Findings: Share new discoveries and insights
- Sample Implementations: Contribute working examples
- Documentation: Improve guides and tutorials
- Bug Fixes: Help maintain code quality
- Feature Requests: Suggest new research directions
- Fork the repository
- Create a feature branch (
git checkout -b feature/amazing-feature) - Commit your changes (
git commit -m 'Add amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request
- Share research findings and methodologies
- Collaborate on experimental implementations
- Discuss innovative approaches to AI agent development
- Contribute to the research roadmap
This project is licensed under the terms included in the LICENSE file. All research findings and implementations are shared for educational and research purposes.
- Research Phase: Active development across all focus areas
- Documentation: Comprehensive guides and tutorials available
- Implementations: Production-ready examples for all major components
- Community: Open for contributions and collaboration
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