Advanced Multi-Agent AI Framework - Professional Team Coordination with 80+ Prompt Engineering Techniques
Transform your AI development workflow with a production-ready multi-agent framework combining advanced prompt engineering, structured coordination, and professional team management.
🔗 Quick Links: Kilo Code Platform | Master Prompt Engineering Techniques
If this project helps you build better AI systems and you'd like to show your appreciation:
- Buy Me a Coffee: https://buymeacoffee.com/mnehmos
Professional AI Team Management - Deploy specialized AI agents with enterprise-grade coordination, advanced prompt engineering, and systematic workflow automation for superior development outcomes.
- ⚡ 80+ Advanced Prompt Engineering Techniques - Integrated cutting-edge methods for superior AI performance
- 🔄 Multi-Agent Coordination - SPARC framework with agentic boomerang pattern for reliable task delegation
- 📈 Performance Optimization - Token-efficient operations with "scalpel, not hammer" resource management
- 🏗️ Production-Ready Architecture - Structured documentation, traceability, and enterprise workflow patterns
- 🛠️ Framework Extensibility - Customizable modes and prompt engineering technique integration
- Kilo Code AI Platform (recommended) or compatible AI assistant with custom modes
- Basic understanding of multi-agent AI systems
- Project requiring systematic AI team coordination
1. Clone the Framework
git clone https://github.com/Mnehmos/Advanced-Multi-Agent-AI-Framework.git
cd Advanced-Multi-Agent-AI-Framework2. Configure AI Team Modes
# Copy configuration templates
cp templates/custom_modes.yaml ./
cp templates/custom-instructions-for-all-modes.md ./
cp templates/enhance-prompt-template.md ./3. Deploy to Kilo Code
- Open Kilo Code → "Modes" → "Edit Project Modes" or "Global Modes"
- copy
custom_modes.yamlconfiguration from template and paste into kilocode settings - Configure custom instructions for all modes by copy and pasting into the Teams settings "Custom Instructions for all Modes"
- Do the same for enhance prompt template into the prompts tab of the srttings window.
- Save and activate framework
4. Start Orchestrating
- Switch to Orchestrator Mode
- Describe your project requirements
- Generate Task Map using enhance prompt (✨ button)
- Let the AI team execute with full coordination
| Mode | Specialization | Advanced Techniques |
|---|---|---|
| 🔄 Orchestrator | Project Management & Task Delegation | workflow-template-prompting, boomerang-task-delegation |
| 🏗️ Architect | System Design & Architecture | visual-documentation-generation, tree-of-thoughts |
| 📅 Planner | Product Planning & Requirements | user-story-prompting, stakeholder-perspective-analysis |
| Mode | Specialization | Advanced Techniques |
|---|---|---|
| ⚒️ Builder | Software Development & Testing | code-generation-agents, test-based-iterative-flow |
| 💻 Code | Advanced Coding & Optimization | 'modular-code-generation, (https://github.com/chonghin33/lcm-1.13-whitepaper)' 'language-construct-modeling` |
| 🔒 Guardian | Infrastructure & CI/CD | automated-development-workflows, semantic-guardrails |
| Mode | Specialization | Advanced Techniques |
|---|---|---|
| ❓ Ask | Information Discovery | rag, iterative-retrieval-augmentation |
| 🔎 Deep Research | Comprehensive Analysis | multi-perspective-analysis, systematic-literature-review |
| 🔬 Deep Scope | Issue Analysis & Scoping | codebase-impact-mapping, hypothetical-scenario-modeling |
| Mode | Specialization | Advanced Techniques |
|---|---|---|
| 🐛 Debug | Technical Diagnostics | five-whys-prompting, chain-of-verification |
| 📁 Memory | Knowledge Management | semantic-clustering, knowledge-graph-construction |
- Complex application architecture planning
- Multi-team coordination and workflow automation
- Advanced code generation with quality assurance
- Systematic debugging and performance optimization
- Literature review and competitive analysis
- Hypothesis formation and testing workflows
- Knowledge management and documentation systems
- Multi-perspective research synthesis
- User story creation and requirement analysis
- Feature planning with stakeholder perspective analysis
- Technical implementation with architectural guidance
- Quality assurance and testing automation
- CI/CD pipeline design and automation
- Security implementation and monitoring
- Performance optimization and scaling
- Documentation and knowledge preservation
Specification → Pseudocode → Architecture → Refinement → Completion
- Task Creation - Orchestrator generates structured tasks from project requirements
- Specialist Assignment - Tasks delegated to most appropriate AI agent
- Advanced Execution - Specialists apply 80+ prompt engineering techniques
- Quality Integration - Results validated and integrated into project workflow
- Iterative Improvement - Continuous optimization through feedback loops
- Context window utilization kept below 40%
- Cognitive primitive optimization (start small, scale up)
- Specialized mode selection for minimal resource usage
- "Scalpel, not Hammer" resource management philosophy
- Structured task validation and success criteria
- Cross-mode verification and error checking
- Comprehensive documentation and traceability
- Automated workflow optimization
- Modular architecture supporting team expansion
- Customizable prompt engineering technique integration
- Enterprise workflow pattern implementation
- Professional project management capabilities
Detailed documentation for each AI specialist:
# Project: Advanced AI System Development
## Phase 1: Architecture Planning
- [ ] **Task 1.1**: System design and architecture planning
- **Agent**: Architect
- **Dependencies**: None
- **Outputs**: [architecture_diagram.md, technical_specifications.md]
- **Validation**: Architecture review completed with stakeholder approval
- **Human Checkpoint**: YES
- **Scope**: Complete system architecture design using visual-documentation-generation and tree-of-thoughts techniques- Structured documentation for audit trails
- Role-based task assignment and validation
- Quality gates and automated verification
- Professional workflow management
- GitHub integration for issue and PR management
- CI/CD pipeline automation
- Knowledge management system integration
- Custom prompt engineering technique deployment
- Comprehensive error handling and debugging
- Performance monitoring and optimization
- Documentation generation and maintenance
- Continuous improvement through feedback integration
- Documentation: Complete framework guides and tutorials
- Issues: Report bugs or request features via GitHub Issues
- Discussions: Join community discussions for best practices
- Professional Support: Contact for enterprise implementation assistance
- ⭐ Star this repository to help others discover the framework
- 🤝 Contribute improvements and new prompt engineering techniques
- ☕ Buy Me a Coffee: Support Development
- 🔬 Advanced Research: Explore Vario Research for custom AI analysis
- Additional prompt engineering technique integration
- Enhanced multi-modal AI support
- Extended enterprise workflow patterns
- Advanced performance analytics and monitoring
- Latest prompt engineering research incorporation
- Multi-agent coordination optimization
- Framework scalability improvements
- Advanced AI reasoning technique integration
MIT License - Open source framework for professional and commercial use.
- SPARC Framework development community
- Multi-agent AI research contributors
- Kilo Code platform development team
- Advanced prompt engineering research community
- Framework users providing feedback and improvements
- Vincent Shing Hin Chong for their work into Language Construct Modeling | https://osf.io/q6cyp/
- 20+ research papers sources listed here: https://mnehmos.github.io/Prompt-Engineering/sources.html
Deploy this professional multi-agent AI framework today and experience:
- ⚡ Faster Development with coordinated AI specialists
- 🎯 Higher Quality through advanced prompt engineering
- 📈 Better Outcomes with systematic workflow management
- 🏗️ Scalable Architecture for growing project needs
This framework represents the cutting edge of multi-agent AI coordination, integrating 80+ advanced prompt engineering techniques with proven enterprise workflow patterns for superior development outcomes.