Langchain Tutorials

Overview

A collection of Jupyter Notebook tutorials that demonstrate how to use the LangChain library for building language model applications. The repository provides step-by-step examples covering chains, agents, memory, and other core LangChain components. It is maintained by the community and serves as a learning resource for developers new to LangChain.

Best for

Best for
Developers who want to learn LangChain through practical, runnable examples.

Use cases

• Learning LangChain fundamentals through hands-on notebooks
• Prototyping chains and agents for LLM-based applications
• Referencing code examples for common LangChain patterns

Notes

A collection of Jupyter Notebook tutorials that demonstrate how to use the LangChain library for building language model applications. The repository provides step-by-step examples covering chains, agents, memory, and other core LangChain components. It is maintained by the community and serves as a learning resource for developers new to LangChain.

7,446 stars on GitHub. Last updated 2024-08-05.

Use cases

• Learning LangChain fundamentals through hands-on notebooks
• Prototyping chains and agents for LLM-based applications
• Referencing code examples for common LangChain patterns

Pros

• Free and open source with over 7,400 stars indicating community trust
• Jupyter Notebook format allows immediate experimentation and modification
• Covers a broad range of LangChain features from basics to advanced

Cons

• Tutorials may become outdated as LangChain evolves rapidly
• No structured curriculum or progression path between notebooks
• Assumes some familiarity with Python and LLM concepts

Indexed from awesome-langchain and enriched against its public facts.