Research-grade Self-Correcting RAG agent built with LangGraph that retrieves knowledge, generates answers, evaluates grounding/relevance/completeness, and iteratively self-improves with confidence scoring and memory.

🚀 Enterprise Agentic RAG: LangGraph + Gemini 2.5 Flash. Stateful self-correcting loops with hallucination grading (94% accuracy). Seamless OpenAI-to-Google migration: 25% latency reduction & 40% cost-efficiency. Built on Python 3.14 for high-performance async orchestration.

Non von Neumann virtual architecture with isomorphic physical substrate for stabilizing AI, reducing system errors, and establishing reversible virtual topological logic