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import ThemedImage from '@theme/ThemedImage';
import useBaseUrl from '@docusaurus/useBaseUrl';
In this section, you'll find explanations of the key concepts, providing a deeper understanding of core principles.
The conceptual guide will not cover step-by-step instructions or specific implementation details — those are found in the [How-To Guides](/docs/how_to/) and [Tutorials](/docs/tutorials) sections. For detailed reference material, please visit the [API Reference](https://python.langchain.com/api_reference/).
## Architecture
LangChain as a framework consists of a number of packages.
### `langchain-core`
This package contains base abstractions of different components and ways to compose them together.
The interfaces for core components like LLMs, vector stores, retrievers and more are defined here.
No third party integrations are defined here.
The dependencies are kept purposefully very lightweight.
### `langchain`
The main `langchain` package contains chains, agents, and retrieval strategies that make up an application's cognitive architecture.
These are NOT third party integrations.
All chains, agents, and retrieval strategies here are NOT specific to any one integration, but rather generic across all integrations.
### `langchain-community`
This package contains third party integrations that are maintained by the LangChain community.
Key partner packages are separated out (see below).
This contains all integrations for various components (LLMs, vector stores, retrievers).
All dependencies in this package are optional to keep the package as lightweight as possible.
### Partner packages
While the long tail of integrations is in `langchain-community`, we split popular integrations into their own packages (e.g. `langchain-openai`, `langchain-anthropic`, etc).
This was done in order to improve support for these important integrations.
### [`langgraph`](https://langchain-ai.github.io/langgraph)
`langgraph` is an extension of `langchain` aimed at
building robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph.
LangGraph exposes high level interfaces for creating common types of agents, as well as a low-level API for composing custom flows.
### [`langserve`](/docs/langserve)
A package to deploy LangChain chains as REST APIs. Makes it easy to get a production ready API up and running.
### [LangSmith](https://docs.smith.langchain.com)
A developer platform that lets you debug, test, evaluate, and monitor LLM applications.
<ThemedImage
alt="Diagram outlining the hierarchical organization of the LangChain framework, displaying the interconnected parts across multiple layers."
sources={{
light: useBaseUrl('/svg/langchain_stack_062024.svg'),
dark: useBaseUrl('/svg/langchain_stack_062024_dark.svg'),
}}
title="LangChain Framework Overview"
style={{ width: "100%" }}
/>

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# Conceptual guide
import ThemedImage from '@theme/ThemedImage';
import useBaseUrl from '@docusaurus/useBaseUrl';
In this section, you'll find explanations of the key concepts, providing a deeper understanding of core principles.
The conceptual guide will not cover step-by-step instructions or specific implementation details — those are found in the [How-To Guides](/docs/how_to/) and [Tutorials](/docs/tutorials) sections. For detailed reference material, please visit the [API Reference](https://python.langchain.com/api_reference/).
## Architecture
LangChain as a framework consists of a number of packages.
### `langchain-core`
This package contains base abstractions of different components and ways to compose them together.
The interfaces for core components like LLMs, vector stores, retrievers and more are defined here.
No third party integrations are defined here.
The dependencies are kept purposefully very lightweight.
### `langchain`
The main `langchain` package contains chains, agents, and retrieval strategies that make up an application's cognitive architecture.
These are NOT third party integrations.
All chains, agents, and retrieval strategies here are NOT specific to any one integration, but rather generic across all integrations.
### `langchain-community`
This package contains third party integrations that are maintained by the LangChain community.
Key partner packages are separated out (see below).
This contains all integrations for various components (LLMs, vector stores, retrievers).
All dependencies in this package are optional to keep the package as lightweight as possible.
### Partner packages
While the long tail of integrations is in `langchain-community`, we split popular integrations into their own packages (e.g. `langchain-openai`, `langchain-anthropic`, etc).
This was done in order to improve support for these important integrations.
### [`langgraph`](https://langchain-ai.github.io/langgraph)
`langgraph` is an extension of `langchain` aimed at
building robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph.
LangGraph exposes high level interfaces for creating common types of agents, as well as a low-level API for composing custom flows.
### [`langserve`](/docs/langserve)
A package to deploy LangChain chains as REST APIs. Makes it easy to get a production ready API up and running.
### [LangSmith](https://docs.smith.langchain.com)
A developer platform that lets you debug, test, evaluate, and monitor LLM applications.
<ThemedImage
alt="Diagram outlining the hierarchical organization of the LangChain framework, displaying the interconnected parts across multiple layers."
sources={{
light: useBaseUrl('/svg/langchain_stack_062024.svg'),
dark: useBaseUrl('/svg/langchain_stack_062024_dark.svg'),
}}
title="LangChain Framework Overview"
style={{ width: "100%" }}
/>
* Conceptual Guide: [LangChain Architecture](/docs/concepts/architecture)
## Runnable interface
<span data-heading-keywords="invoke,runnable"></span>