How to Build an Atomic-Agents RAG Pipeline with Typed Schemas, Dynamic Context Injection, and Agent Chaining
In this tutorial, we build an advanced, end-to-end learning pipeline around Atomic-Agents by wiring together typed agent interfaces, structured prompting, and a compact retrieval layer that grounds outputs in real project documentation. Also, we demonstrate how to plan retrieval, retrieve relevant context, inject it dynamically into an answering agent, and run an interactive loop that turns the setup into a reusable research assistant for any new Atomic Agents question. Check out the FULL CODES here . Copy Code Copied Use a different Browser import os, sys, textwrap, time, json, re from typing import List, Optional, Dict, Tuple from dataclasses import dataclass import subprocess subprocess.check_call([sys.executable, "-m", "pip", "install", "-q", "atomic-agents", "instructor", "openai", "pydantic", "requests", "beautifulsoup4", ...
