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Advanced Multi Agent AI Framework

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Basic Information

This repository provides a production-ready framework for building and running coordinated multi-agent AI teams that apply advanced prompt engineering and structured workflows. It is designed to help project leads and developers deploy specialized AI modes such as Orchestrator, Architect, Planner, Builder, Code, Guardian, and research/support specialists to manage complex software, research, product, and infrastructure projects. The framework codifies the SPARC process (Specification, Pseudocode, Architecture, Refinement, Completion) and a boomerang task delegation pattern so the orchestrator can generate, assign, validate, and iteratively refine tasks. It includes configuration templates for custom modes and prompts, documentation for team member profiles and task management, and recommended deployment to the Kilo Code platform. The intent is to provide enterprise-grade coordination, traceability, and extensibility for systematic multi-agent workflows without presuming a particular LLM provider.

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App URL
https://github.com/Mnehmos/Advanced-Multi-Agent-AI-Framework
Github Repository
https://github.com/Mnehmos/Advanced-Multi-Agent-AI-Framework/blob/main/README.md

App Details

Features
The README highlights 80+ advanced prompt engineering techniques integrated into the framework, explicit multi-agent coordination via the SPARC framework, and a boomerang task-delegation pattern for reliable handoffs and iterative improvement. It defines specialized modes for orchestration, architecture, planning, building, coding, research, debugging, memory and governance. Performance features include token-efficiency guidance, context-window management, and a ‘‘scalpel, not hammer’’ resource philosophy. Quality assurance features cover structured task validation, cross-mode verification, traceability, role-based validation and audit trails. The project ships configuration templates (custom_modes.yaml, custom instructions, enhance-prompt templates), documentation for team profiles, GitHub and CI/CD integration points, and guidance for deploying modes into the Kilo Code platform.
Use Cases
The framework is useful for enterprise software development, AI research projects, product development, and infrastructure management by automating task decomposition, specialist assignment, and quality gates. Teams can generate task maps, run coordinated implementation and testing flows, and apply systematic debugging and knowledge management through designated memory and debug modes. The SPARC and boomerang patterns support iterative refinement and continuous improvement with human checkpoints and auditability. Token-efficient operation and modular modes help scale teams while controlling compute cost. Integration notes and templates reduce setup time for Kilo Code deployment and for linking agents to GitHub, CI/CD, and knowledge systems, enabling faster delivery, higher-quality outputs, and better project traceability.

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