This repository provides an open tutorial for LangChain and LangGraph, targeting both beginners and experienced users. It aims to offer practical examples, cover cutting-edge features, and showcase real-world applications, serving as a comprehensive learning resource for the global LangChain community.

How It Works

The tutorial is structured to guide users through LangChain's functionalities, from foundational concepts to advanced applications. It emphasizes practical implementation through code examples, likely leveraging Jupyter Notebooks for interactive learning. The project encourages community contributions, allowing users to add examples, particularly those involving open LLMs, and follow a defined contribution process for quality assurance.

Quick Start & Requirements

• Files are designed to be executable on Google Colab.
• Specific environment requirements must be specified if using open models or additional packages.
• Contribution process includes self-checks for license compliance and template adherence.

Highlighted Details

• Focuses on global use cases and the latest LangChain/LangGraph features.
• Encourages contributions of examples related to open LLMs.
• Features a structured contribution process with peer reviews and proofreading.
• Lists a large number of core contributors with LinkedIn/GitHub profiles.

Maintenance & Community

• The project is actively seeking contributors to expand its content.
• A clear contribution process is outlined, including PR submission and review.
• Links to LangChain and LangGraph documentation are provided.

Licensing & Compatibility

• The codebase is released under the MIT License, covering both code and sample code.
• The MIT license generally permits commercial use and linking with closed-source projects.

Limitations & Caveats

The contribution process is still under development, and specific requirements for open models or additional packages need to be communicated to an "Infra Team."