text: to this headline")        How to get started using LangChain to create an Language Model application.        - [Quickstart Guide](https://python.langchain.com/en/latest/getting_started/getting_started.html)            Concepts and terminology.        - [Concepts and terminology](https://python.langchain.com/en/latest/getting_started/concepts.html)            Tutorials created by community experts and presented on YouTube.        - [Tutorials](https://python.langchain.com/en/latest/getting_started/tutorials.html)            ## Modules [\#](\#modules "Permalink to this headline")        These modules are the core abstractions which we view as the building blocks of any LLM-powered application.        For each module LangChain provides standard, extendable interfaces. LanghChain also provides external integrations and even end-to-end implementations for off-the-shelf use.        The docs for each module contain quickstart examples, how-to guides, reference docs, and conceptual guides.        The modules are (from least to most complex):        - [Models](https://python.langchain.com/en/latest/modules/models.html): Supported model types and integrations.        - [Prompts](https://python.langchain.com/en/latest/modules/prompts.html): Prompt management, optimization, and serialization.        - [Memory](https://python.langchain.com/en/latest/modules/memory.html): Memory refers to state that is persisted between calls of a chain/agent.        -