## 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/docs/modules/model_io/models/): 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.        - [Indexes](https://python.langchain.com/en/latest/modules/data_connection.html): Language models become much more powerful when combined with application-specific data - this module contains interfaces and integrations for loading, querying and updating external data.        - [Chains](https://python.langchain.com/en/latest/modules/chains.html): Chains are structured sequences of calls (to an LLM or to a different utility).        - [Agents](https://python.langchain.com/en/latest/modules/agents.html): An agent is a Chain in which an LLM, given a high-level directive and a set of tools, repeatedly decides an action, executes the action and observes the outcome until the high-level directive is complete.        - [Callbacks](https://python.langchain.com/en/latest/modules/callbacks/getting_started.html): Callbacks let you log and stream the intermediate steps of any chain, making it easy to observe, debug, and evaluate the internals of an application.            ## Use Cases [\#](\#use-cases "Permalink to this headline")        Best practices and
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2markdown service transforms website content into structured markdown files. ->: ## 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/docs/modules/model_io/models/): 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.        - [Indexes](https://python.langchain.com/en/latest/modules/data_connection.html): Language models become much more powerful when combined with application-specific data - this module contains interfaces and integrations for loading, querying and updating external data.        - [Chains](https://python.langchain.com/en/latest/modules/chains.html): Chains are structured sequences of calls (to an LLM or to a different utility).        - [Agents](https://python.langchain.com/en/latest/modules/agents.html): An agent is a Chain in which an LLM, given a high-level directive and a set of tools, repeatedly decides an action, executes the action and observes the outcome until the high-level directive is complete.        - [Callbacks](https://python.langchain.com/en/latest/modules/callbacks/getting_started.html): Callbacks let you log and stream the intermediate steps of any chain, making it easy to observe, debug, and evaluate the internals of an application.            ## Use Cases [\#](\#use-cases "Permalink to this headline")        Best practices and