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> [!NOTE]
> Looking for the JS/TS library? Check out [LangChain.js](https://github.com/langchain-ai/langchainjs).
LangChain is a framework for building LLM-powered applications. It helps you chain
together interoperable components and third-party integrations to simplify AI
application development — all while future-proofing decisions as the underlying
technology evolves.
```bash
pip install -U langchain
```
To learn more about LangChain, check out
[the docs](https://python.langchain.com/docs/introduction/). If you’re looking for more
advanced customization or agent orchestration, check out
[LangGraph](https://langchain-ai.github.io/langgraph/), our framework for building
controllable agent workflows.
## Why use LangChain?
LangChain helps developers build applications powered by LLMs through a standard
interface for models, embeddings, vector stores, and more.
Use LangChain for:
- **Real-time data augmentation**. Easily connect LLMs to diverse data sources and
external / internal systems, drawing from LangChain’s vast library of integrations with
model providers, tools, vector stores, retrievers, and more.
- **Model interoperability**. Swap models in and out as your engineering team
experiments to find the best choice for your application’s needs. As the industry
frontier evolves, adapt quickly — LangChain’s abstractions keep you moving without
losing momentum.
## LangChain’s ecosystem
While the LangChain framework can be used standalone, it also integrates seamlessly
with any LangChain product, giving developers a full suite of tools when building LLM
applications.
To improve your LLM application development, pair LangChain with:
- [LangSmith](http://www.langchain.com/langsmith) - Helpful for agent evals and
observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain
visibility in production, and improve performance over time.
- [LangGraph](https://langchain-ai.github.io/langgraph/) - Build agents that can
reliably handle complex tasks with LangGraph, our low-level agent orchestration
framework. LangGraph offers customizable architecture, long-term memory, and
human-in-the-loop workflows — and is trusted in production by companies like LinkedIn,
Uber, Klarna, and GitLab.
- [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/#langgraph-platform) - Deploy
and scale agents effortlessly with a purpose-built deployment platform for long
running, stateful workflows. Discover, reuse, configure, and share agents across
teams — and iterate quickly with visual prototyping in
[LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/).
## Additional resources
- [Tutorials](https://python.langchain.com/docs/tutorials/): Simple walkthroughs with
guided examples on getting started with LangChain.
- [How-to Guides](https://python.langchain.com/docs/how_to/): Quick, actionable code
snippets for topics such as tool calling, RAG use cases, and more.
- [Conceptual Guides](https://python.langchain.com/docs/concepts/): Explanations of key
concepts behind the LangChain framework.
- [API Reference](https://python.langchain.com/api_reference/): Detailed reference on
navigating base packages and integrations for LangChain.
](https://codespaces.new/langchain-ai/langchain)
[](https://twitter.com/langchainai)
[](https://codspeed.io/langchain-ai/langchain)
> [!NOTE]
> Looking for the JS/TS library? Check out [LangChain.js](https://github.com/langchain-ai/langchainjs).
LangChain is a framework for building LLM-powered applications. It helps you chain
together interoperable components and third-party integrations to simplify AI
application development — all while future-proofing decisions as the underlying
technology evolves.
```bash
pip install -U langchain
```
To learn more about LangChain, check out
[the docs](https://python.langchain.com/docs/introduction/). If you’re looking for more
advanced customization or agent orchestration, check out
[LangGraph](https://langchain-ai.github.io/langgraph/), our framework for building
controllable agent workflows.
## Why use LangChain?
LangChain helps developers build applications powered by LLMs through a standard
interface for models, embeddings, vector stores, and more.
Use LangChain for:
- **Real-time data augmentation**. Easily connect LLMs to diverse data sources and
external / internal systems, drawing from LangChain’s vast library of integrations with
model providers, tools, vector stores, retrievers, and more.
- **Model interoperability**. Swap models in and out as your engineering team
experiments to find the best choice for your application’s needs. As the industry
frontier evolves, adapt quickly — LangChain’s abstractions keep you moving without
losing momentum.
## LangChain’s ecosystem
While the LangChain framework can be used standalone, it also integrates seamlessly
with any LangChain product, giving developers a full suite of tools when building LLM
applications.
To improve your LLM application development, pair LangChain with:
- [LangSmith](http://www.langchain.com/langsmith) - Helpful for agent evals and
observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain
visibility in production, and improve performance over time.
- [LangGraph](https://langchain-ai.github.io/langgraph/) - Build agents that can
reliably handle complex tasks with LangGraph, our low-level agent orchestration
framework. LangGraph offers customizable architecture, long-term memory, and
human-in-the-loop workflows — and is trusted in production by companies like LinkedIn,
Uber, Klarna, and GitLab.
- [LangGraph Platform](https://langchain-ai.github.io/langgraph/concepts/#langgraph-platform) - Deploy
and scale agents effortlessly with a purpose-built deployment platform for long
running, stateful workflows. Discover, reuse, configure, and share agents across
teams — and iterate quickly with visual prototyping in
[LangGraph Studio](https://langchain-ai.github.io/langgraph/concepts/langgraph_studio/).
## Additional resources
- [Tutorials](https://python.langchain.com/docs/tutorials/): Simple walkthroughs with
guided examples on getting started with LangChain.
- [How-to Guides](https://python.langchain.com/docs/how_to/): Quick, actionable code
snippets for topics such as tool calling, RAG use cases, and more.
- [Conceptual Guides](https://python.langchain.com/docs/concepts/): Explanations of key
concepts behind the LangChain framework.
- [API Reference](https://python.langchain.com/api_reference/): Detailed reference on
navigating base packages and integrations for LangChain.