Python

The authors present a deterministic 3‑Tiered Graph‑RAG architecture that outperforms a conventional vector‑based RAG pipeline on fact‑dense queries, reporting a 35 % reduction in hallucination rates as measured by the FactCheck‑Metric. The Graph‑RAG system leverages a hierarchical knowledge graph for retrieval, a rule‑based fusion layer, and a lightweight language model for final generation, achieving a 12 ms per‑query latency on a single A100 GPU. While the approach improves factual fidelity, it requires a pre‑built graph index and incurs higher storage overhead compared to flat vector indices. The benchmark demonstrates that Graph‑RAG can be a viable alternative when accuracy outweighs index‑building cost.

The article demonstrates how to construct a minimal AI agent entirely in plain Python, leveraging the Anthropic API for all LLM interactions. It walks through initializing the client, crafting a prompt loop, and managing conversation state without external frameworks, illustrating a clean, dependency‑free baseline. The tutorial highlights the trade‑off of manual state handling versus the convenience of higher‑level libraries, and shows how to integrate the agent into existing Python