https://github.com/45harry/langgraph-tutorials
LangGraph - tutorials
https://github.com/45harry/langgraph-tutorials
langchain langgraph langsmith uv
Last synced: 2 months ago
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LangGraph - tutorials
- Host: GitHub
- URL: https://github.com/45harry/langgraph-tutorials
- Owner: 45Harry
- License: mit
- Created: 2025-06-28T09:40:53.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-06-28T10:05:47.000Z (about 1 year ago)
- Last Synced: 2025-06-28T10:37:29.898Z (about 1 year ago)
- Topics: langchain, langgraph, langsmith, uv
- Language: Jupyter Notebook
- Homepage:
- Size: 0 Bytes
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# LangGraph Tutorials
This repository contains a collection of hands-on tutorials and example scripts for learning and experimenting with [LangGraph](https://github.com/langchain-ai/langgraph) and [LangChain](https://github.com/langchain-ai/langchain) in Python. The tutorials cover a range of topics, from building simple computation graphs to creating chatbots, using tools, and integrating memory and human-in-the-loop (HITL) workflows.
## Author
**45Harry**
## Contents
- **1_simple_graph.ipynb**: Introduction to building a simple computation graph using LangGraph.
- **2_graph_with_condition.ipynb**: Demonstrates conditional logic within a graph.
- **3_chatbot.ipynb**: Shows how to build a basic chatbot using LangGraph and LangChain.
- **4_tool_call.ipynb**: Example of calling external tools from within a graph.
- **5_tool_call_agent.ipynb**: Expands on tool calling with agent-based workflows.
- **6_memory.ipynb / 6_memory.py**: Explores adding memory and stateful behavior to your graphs.
- **7_langsmith.ipynb**: Integrates with LangSmith for experiment tracking and evaluation.
- **8_HITL.py**: Demonstrates human-in-the-loop (HITL) patterns in LangGraph.
## Installation
1. **Clone the repository:**
```bash
git clone https://github.com/45Harry/langgraph-tutorials.git
cd langgraph-tutorials
```
2. **Install dependencies:**
This project uses [Python 3.13+](https://www.python.org/downloads/) and the following main packages:
- `langchain`
- `langchain-google-genai`
- `langgraph`
- `langsmith`
- `notebook`
- `numpy`
- `python-dotenv`
You can install all dependencies using [uv](https://github.com/astral-sh/uv) (recommended for speed) or pip:
```bash
uv pip install -e .
```
or
```bash
pip install -e .
```
The project uses `pyproject.toml` as the requirements file.
3. **Set up environment variables:**
Some tutorials require API keys (e.g., for Google GenAI). Create a `.env` file in the project root and add your keys as needed.
## Usage
- Open any of the `.ipynb` notebooks in Jupyter or VSCode to follow along with the tutorials.
- For `.py` scripts, run them directly with Python:
```bash
python 6_memory.py
```
## License
MIT License