12 agent patterns, built from zero. Each project teaches one core agentic idea, hand-rolled with the raw OpenAI-compatible SDK — no heavy framework until it is genuinely needed. Progressive: every project builds on the ideas of the last.
See ROADMAP.md for the full 12-project plan.
- Language: Python 3.10+
- LLM: NVIDIA NIM free API — an OpenAI-compatible endpoint (
https://integrate.api.nvidia.com/v1) that hosts Llama-3.3, Nemotron, DeepSeek, Qwen and more, with tool-calling + JSON support. Free key, no card, for personal use. - Client: one swappable OpenAI-compatible client in
common/client.py— point it at any free provider (Groq, Gemini, OpenRouter) by changing two env vars. - No local compute — everything is a hosted free API.
# 1. clone / cd into the repo
cd agentic-ai-from-zero
# 2. create a virtual environment
python -m venv .venv
# Windows:
.venv\Scripts\activate
# macOS / Linux:
source .venv/bin/activate
# 3. install dependencies
pip install -r requirements.txt
# 4. add your NVIDIA NIM key
cp .env.example .env
# then edit .env and paste your key:
# NVIDIA_API_KEY=nvapi-xxxxxxxx
# get one free at https://build.nvidia.comEach project is a self-contained folder with its own run.py and README.md:
python 01-structured-output/run.py| # | Project | Teaches |
|---|---|---|
| 01 | Structured Output Agent | schema-first agents, the parse→retry loop, typed I/O |
| 02 | RAG Agent — citation grounding | retrieval → grounded generation, confidence |
| 03 | ReAct Planning Agent | observe→think→act→reflect, bounded iteration |
| 04 | Multi-Tool Orchestrator | dynamic tool registry, parallel execution |
| … | (see ROADMAP) |
| Var | Default | Purpose |
|---|---|---|
NVIDIA_API_KEY |
(required) | your NVIDIA NIM key |
NIM_BASE_URL |
https://integrate.api.nvidia.com/v1 |
OpenAI-compatible base URL |
NIM_MODEL |
meta/llama-3.1-8b-instruct |
default model id (warm/fast on the free tier) |
Secrets live in .env (gitignored). Never commit your key.