Python

Most AI agent tutorials end at “hello world.” You build a single agent with one or two tools, it answers a few questions, and that is it. The gap between that tutorial and a production system with multiple agents, authentication, observability, and a real frontend is enormous.

Revised with .NET examples — A newer version of this article, covering both Python and .NET, is available as part of the MAF v1: Python and .NET series: MAF v1 — 01-first-agent, MAF v1 — 02-add-tools.

Revised with .NET examples — A newer version of this article, covering both Python and .NET, is available as part of the MAF v1: Python and .NET series: MAF v1 — 02-add-tools.

Revised with .NET examples — A newer version of this article, covering both Python and .NET, is available as part of the MAF v1: Python and .NET series: MAF v1 — 14-handoff-orchestration, MAF v1 — Putting it all together (Ch21).

Stream tokens as they arrive and reuse a session across turns so the LLM sees the full conversation. ~60 lines of code per language.

Serialize an AgentSession to JSON, persist it, reload in a fresh process, and have the agent pick up exactly where it left off.

Build the smallest useful Microsoft Agent Framework program: one LLM call, one response, and the same shape in Python and .NET.

Install uv, .NET 9, Docker, OpenAI keys, and a verify script so Chapter 1 starts with both Python and .NET environments ready.

Give an agent a callable function, let the LLM decide when to use it, and compare Python and .NET tool loops side by side.

TL;DR: This guide walks you through building a production-ready RAG system using FastAPI, ChromaDB, MinIO, and OpenAI. Learn document chunking, vector embeddings, hybrid search, and real-world deployment strategies. Introduction # As a .NET developer watching the AI space move fast, I found myself both excited and skeptical. When tools like Claude.ai and ChatGPT started offering out-of-the-box RAG solutions, I wanted to build my own system with full control over the implementation.