NagaAgent is a versatile AI assistant designed for seamless conversational AI experiences, targeting developers and power users. It offers multi-agent capabilities, streaming voice interaction, and a GRAG knowledge graph memory system, simplifying complex AI task management.

How It Works

NagaAgent employs a multi-agent architecture with a unified tool-calling mechanism. It supports both MCP (Multi-Agent Communication Protocol) and custom Agent types, allowing for flexible task delegation and execution. The GRAG (Graph Retrieval Augmented Generation) system leverages Neo4j for knowledge graph memory, enabling intelligent context recall and data extraction from conversations.

Quick Start & Requirements

• Installation: Clone the repository and run .\setup.ps1 (Windows) or chmod +x quick_deploy_mac.sh && ./quick_deploy_mac.sh (Mac).
• Prerequisites: Python 3.8+ (3.11 recommended), PowerShell 5.1+ (Windows), Homebrew (Mac), Playwright for browser automation. GPU/CUDA not explicitly mentioned but recommended for LLM performance.
• Setup: The scripts automate virtual environment creation and dependency installation.
• Links: Project Repository

Highlighted Details

• Supports multi-LLM provider integration (OpenAI, DeepSeek, Anthropic).
• Features a GRAG knowledge graph memory system for persistent context.
• Includes a RESTful API server for integration with external applications.
• Offers streaming voice interaction and PyQt5-based UI.

Maintenance & Community

The project appears actively maintained with detailed documentation and clear versioning (e.g., v3.0.4 updates). Community interaction channels are not explicitly listed in the README.

Licensing & Compatibility

The project is released under the MIT License, permitting commercial use and integration with closed-source projects.

Limitations & Caveats

The setup process requires specific OS versions and potentially manual installation of build tools for certain dependencies like PyAudio. While cross-platform, detailed troubleshooting for environment issues is primarily covered in the README.