by esurovtsev · Agent Tool · ★ 82
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LangGraph Advanced This repository continues the LangGraph learning journey with a collection of advanced Jupyter notebooks. It focuses on real-world agent architectures, including dynamic tool loading, long-term memory management, human-in-the-loop control, and parallel execution. Designed for developers who already grasp the basics, this series helps you build scalable and production-ready AI workflows with LangGraph and LangChain. Getting Started Clone the Repository Set Up Your Python Environment It is recommended to use a virtual environment to manage dependencies: Install Dependencies Usage Follow the lessons in order (scripts or notebooks). Each lesson will have an accompanying video tutorial. Explanations and code comments will help you understand each concept. Video Tutorials Each lesson will have a dedicated video tutorial. Links will be provided as lessons are released. Contents Prebuilt Agents (01prebuilt-agents.ipynb) Deep dive into the architecture and workflow of prebuilt agents in LangGraph Explains the ReAct agent pattern: act, observe, reason, and how these steps form the backbone of agent logic Demonstrates defining and binding tools
| Stars | 82 |
| Forks | 34 |
| Language | Jupyter Notebook |
| Category | Agent Tool |
| Quality Score | 64.2950017550125/100 |
| Last Updated | 2025-11-22 |
| Created | 2025-07-27 |
| Est. Tokens | ~54k |
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langgraph-advanced is An advanced LangGraph series exploring real-world agent workflows, dynamic tools, parallel execution, long-term memory, and human-in-the-loop designs. Includes hands-on Python notebooks for building s. It is categorized as a Agent Tool with 82 GitHub stars.
langgraph-advanced is primarily written in Jupyter Notebook.
You can find installation instructions and usage details in the langgraph-advanced GitHub repository at github.com/esurovtsev/langgraph-advanced. The project has 82 stars and 34 forks, indicating an active community.
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