Langchain1.0-Langgraph1.0-Learning and langchain-in-action
These are **complements** — A provides structured learning of LangChain/LangGraph fundamentals and agent development patterns, while B offers practical real-world implementation examples and patterns that build upon those foundational concepts.
About Langchain1.0-Langgraph1.0-Learning
BrandPeng/Langchain1.0-Langgraph1.0-Learning
这是一个 LangChain 1.0 和 LangGraph 1.0 的学习仓库，学习如何进行agent开发，涵盖从基础概念到实战项目的完整学习路径。
This project provides a structured learning path for building AI applications powered by large language models. It guides you through creating intelligent agents that can understand complex queries, use tools, manage conversation history, and generate structured outputs. Marketers, researchers, or product managers looking to integrate advanced AI capabilities into their workflows would find this beneficial for automating tasks like data analysis or content generation.
About langchain-in-action
huangjia2019/langchain-in-action
Practical LangChain patterns and implementations for real-world LLM applications. 极客时间：LangChain实战课 - 这是LangChain框架早期设计的一系列重点模块的直接而清晰的示例和讲解。随着LangChain的快速演进，有些代码需要安装新的版本进行迭代。希望大家在快速浏览课程概念（仍有价值）的同时，自行学习LangChain最新的代码和进展。
This project provides practical examples and implementations for building applications that use large language models (LLMs). It helps developers understand how to combine different components to create more complex and capable AI systems. Developers can learn to integrate various AI functions and data sources into their custom applications.
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work