AI-Agent-Framework

This framework represents a cutting-edge approach to building modular, high-performance AI agents with specialized domain capabilities. The architecture reflects several key advancements in AI engineering: Core Innovations

Quantization-Ready Infrastructure The new quantization.py and optimization submodule (inference.py, memory_ops.py) support 4/8-bit models via GPTQ/AWQ, reducing GPU memory requirements by 70% while maintaining performance through TensorRT/ONNX optimizations.

Model Context Protocol (MCP) Integration he mcp_integration.py and dedicated server enable cross-agent knowledge sharing - agents can now access shared context memory and plugin capabilities through GitHub-hosted endpoints.

Specialized RAG Architecture The hybrid retriever (ColBERT + traditional embeddings) paired with multi-tier memory (episodic/short-term/long-term) allows for precise context handling in domains like healthcare and legal.

Domain-Specific Engineering

The /modules directory contains pre-configured agents with:

Healthcare: Clinical RAG with HIPAA-compliant data handling

Finance: Real-time SEC filing analysis tools

Legal: MCP-legal plugin for statute interpretation

Production Features

Token-by-token streaming via WebSocket

Security-hardened with OWASP checks and model injection tests

CUDA-compatible configurations for varied GPU environments

This framework is designed for enterprise deployment, combining the latest in quantization, specialized RAG, and multi-agent communication protocols while maintaining rigorous testing standards.