🚀 LangGraph Playground

A collection of Jupyter notebooks exploring LangGraph — from basic state graphs to more advanced agentic workflows.

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📦 Setup

This project uses uv for dependency management:

uv sync

Run Jupyter:

uv run jupyter notebook

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📘 Concepts & Notes

Core concepts and explanations are documented in: notes.ipynb

It covers:

• State & TypedDict
• Nodes & Edges
• Conditional routing
• Tool calling
• Agent patterns
• Memory & checkpointing
• RAG graphs
• Agentic workflows

Start there if you're new to LangGraph.

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📂 Repository Structure

The repository mainly contains .ipynb files of varying complexity:

• Basic graphs – linear flows and minimal state examples
• Conditional graphs – branching logic and tool integration
• Advanced workflows – agentic systems and RAG-based graphs

Each notebook can be run independently.

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🎯 Purpose

This is a hands-on sandbox for understanding:

• Graph-based LLM orchestration
• Stateful AI workflows
• Controlled agent execution
• Scalable agent architectures

Focus: engineering clarity over LLM “magic”.