A collection of hands-on examples demonstrating the core concepts of LangGraph for building stateful, multi-step AI workflows. This repository covers workflow orchestration, state management, persistence, conditional execution, and tool integration using LangGraph.
Learn how to design different workflow patterns in LangGraph.
- Sequential Workflows
- Parallel Workflows
- Conditional Workflows
- Iterative Workflows
Understand how LangGraph manages state across multiple workflow steps.
Learn how to persist workflow state for long-running AI applications.
Explore how to integrate external tools within LangGraph workflows.
Examples of connecting Large Language Models with LangGraph.
Interactive notebooks demonstrating each LangGraph concept.
| Category | Technologies |
|---|---|
| Language | Python |
| Framework | LangGraph |
| LLM Framework | LangChain |
| Models | Google Gemini |
| Notebook | Jupyter |
| File | Description |
|---|---|
1-Sequential workflows.ipynb |
Sequential workflow examples |
2-Parallel workflow.ipynb |
Parallel execution examples |
2-upse eassy workflow.ipynb |
Additional workflow examples |
3-conditional workflow.ipynb |
Conditional routing and branching |
3-review reply.ipynb |
Review & reply workflow example |
4-iterative workflow.ipynb |
Iterative workflow execution |
5-persistence.ipynb |
State persistence examples |
6-tools-langgraph.ipynb |
Tool integration with LangGraph |
model.py |
Model configuration |
notes.txt |
Learning notes |
Harshil Kothiya - AI/ML Engineer