Twelve hands-on Jupyter notebooks walking through modern agentic design patterns end-to-end — each
built on LangGraph, each running a realistic scenario, each scored at the end. No toy
examples; every notebook tells you what the architecture solves, builds it node by node, and
measures the result.
The collection spans the full spectrum: foundational patterns like self-critique and
ReAct from scratch, intermediate systems like plan-and-execute and supervisor multi-agent,
and advanced structures like tree-of-thoughts, hierarchical agents, and LLM-as-a-judge
evaluation. Two model tiers underneath: DeepSeek-V3 handles the heavy reasoning,
Gemma 2 handles the cheaper steps that don't need a frontier model — both served via
Nebius AI Studio.
DESIGN NOTE
Every notebook follows the same six-section format — problem → graph → build → live scenario → eval →
when-to-use. That structure isn't decorative; it forces every architecture to confront whether it
actually earns its complexity, or whether a single tool call would have been enough.
A typical run, distilled from notebook 03 (ReAct from scratch):
ReAct trace · notebook 03
question: what's the GDP per capita of egypt vs nigeria in 2024?
thought: i need current GDP/capita figures for both — search.
action: tavily_search("egypt GDP per capita 2024")
obs: ~$3,457 USD (world bank, 2024 est.)
thought: now nigeria.
action: tavily_search("nigeria GDP per capita 2024")
obs: ~$1,621 USD (world bank, 2024 est.)
thought: i have both — compute ratio and answer.
final: egypt: ~$3,457 · nigeria: ~$1,621
egypt is ~2.13× higher per capita in 2024.
observability: every step traced in LangSmith