Nexus is a revolutionary framework that empowers Large Language Models (LLMs) to discover and optimize solutions through collaborative reasoning and machine-native operations. Unlike traditional approaches that force LLMs to mimic human behavior, Nexus enables models to leverage their inherent strengths in processing speed, parallel operations, and backend-oriented task execution.
- Multi-Agent Collaboration: Specialized agents work together to discover and refine optimal solutions
- Advanced Web Content Extraction: HTML-aware query tools for efficient information gathering
- Access Pattern Optimization: Reusable, adaptable patterns that capture and improve solution strategies
- Machine-Native Operations: Direct backend interactions instead of simulated human behavior
- Iterative Refinement: Continuous improvement through collaborative agent feedback
Nexus employs a sophisticated multi-agent system with specialized roles:
- Task Execution Agent: Implements initial solutions
- Speed Optimization Agent: Identifies faster execution methods
- Efficiency Enhancement Agent: Minimizes resource usage
- Guidance and Context Agent: Gathers relevant information and documentation
// Initialize the Nexus framework
nexus := framework.New()
// Create specialized agents
taskAgent := agents.NewTaskExecutor()
speedAgent := agents.NewSpeedOptimizer()
efficiencyAgent := agents.NewEfficiencyEnhancer()
contextAgent := agents.NewGuidanceProvider()
// Configure collaboration
nexus.AddAgents(taskAgent, speedAgent, efficiencyAgent, contextAgent)
// Execute a task with optimization
result := nexus.ExecuteWithOptimization(task)- Components: Detailed system architecture
- Contributing: Development guidelines
- Change Log: Project history and decisions
go get github.com/gavinvolpe/nexus- Web Data Extraction: Efficient gathering of structured data from websites
- API Optimization: Finding the most efficient ways to interact with services
- Process Automation: Creating optimized workflows for complex tasks
- Knowledge Discovery: Uncovering novel insights from diverse data sources
Traditional LLM implementations often force models to mimic human behavior, limiting their potential. Nexus breaks free from this paradigm by:
- Enabling direct backend operations instead of UI simulation
- Leveraging collaborative agent specialization
- Focusing on machine-speed task execution
- Building reusable, optimized access patterns
Nexus is under active development. Current focus areas:
- Implementing specialized agent interfaces
- Developing advanced HTML query tools
- Creating the agent collaboration protocol
- Building the pattern optimization pipeline
We welcome contributions! See CONTRIBUTING.md for guidelines.
MIT License - see LICENSE for details.
- Anthropic for the Model Context Protocol
- Groq for their high-performance LLM inference platform
- Ollama for local LLM deployment capabilities
- The Go Team for the excellent programming language and tools
- Gorilla Websocket for robust WebSocket implementation
- The AI research community for advancing LLM capabilities
- The Go community for their excellent packages and tools
- The open-source community for their invaluable contributions
- The mentor-mentee paradigm in software development
- Research on multi-agent AI systems
- Work on knowledge graphs and pattern recognition
- Studies on LLM optimization and efficiency
- jq for inspiring our HTML query approach
- ripgrep for efficient code search
- sonic for high-performance JSON serialization
- websocket for WebSocket implementation
- Anthropic for the Model Context Protocol
- Groq for their high-performance LLM inference platform
- Ollama for local LLM deployment capabilities
- The Go Team for the excellent programming language and tools 6428
Special thanks to all contributors who have helped shape and improve this project.