This repository contains practical, step-by-step examples to learn and build with LangGraph — a powerful framework for composing multi-step agentic workflows over LLMs.
Get familiar with core LangGraph constructs: nodes, edges, and conditional routing.
Agent_01_SingleNodeGraph.ipynb: Minimal example with a single processing node.Agent_02_SequentialGraph.ipynb: Linear sequence of tasks using node chaining.Agent_03_ConditionalGraph.ipynb: Branching logic using conditional flows.Agent_04_LoopingGraph.ipynb: Implement loops and iterative reasoning over LangGraph.
Progressively build intelligent agents using LangGraph with memory, tools, and ReAct patterns.
Agent_01_SimpleBot.py: Stateless bot using basic prompt logic.Agent_02_MemoryChatBot.py: Memory-enabled chatbot using LangGraph.Agent_03_ReActAgent.py: Reasoning + acting agent with multi-step execution.Agent_04_EmailDrafter.py: Tool-using agent to draft custom emails.Agent_02_ChatLog.txt: Sample conversation log for agent memory testing.
- Python
- LangGraph / LangChain / LangSmith
- OpenAI / Ollama
- Tool usage, memory, control flow
Each section is self-contained with modular, runnable examples.
- Clone the repo
- Set up a Python environment
- Explore each notebook/script in order
Perfect for developers exploring LangGraph for LLM-based agents, workflows, and tool orchestration.
Contributions welcome!