langchain/docs/docs/introduction.mdx

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# Introduction

**LangChain** is a framework for developing applications powered by large language models (LLMs).

LangChain simplifies every stage of the LLM application lifecycle:
- **Development**: Build your applications using LangChain's open-source [building blocks](/docs/concepts#langchain-expression-language) and [components](/docs/concepts). Hit the ground running using [third-party integrations](/docs/integrations/platforms/) and [Templates](/docs/templates).
- **Productionization**: Use [LangSmith](https://docs.smith.langchain.com/) to inspect, monitor and evaluate your chains, so that you can continuously optimize and deploy with confidence.
- **Deployment**: Turn any chain into an API with [LangServe](/docs/langserve).

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  title="LangChain Framework Overview"
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Concretely, the framework consists of the following open-source libraries:

- **`langchain-core`**: Base abstractions and LangChain Expression Language.
- **`langchain-community`**: Third party integrations.
  - Partner packages (e.g. **`langchain-openai`**, **`langchain-anthropic`**, etc.): Some integrations have been further split into their own lightweight packages that only depend on **`langchain-core`**.
- **`langchain`**: Chains, agents, and retrieval strategies that make up an application's cognitive architecture.
- **[langgraph](https://langchain-ai.github.io/langgraph)**: Build robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph.
- **[langserve](/docs/langserve)**: Deploy LangChain chains as REST APIs.
- **[LangSmith](https://docs.smith.langchain.com)**: A developer platform that lets you debug, test, evaluate, and monitor LLM applications.


:::note

These docs focus on the Python LangChain library. [Head here](https://js.langchain.com) for docs on the JavaScript LangChain library.

:::

## [Tutorials](/docs/tutorials)

If you're looking to build something specific or are more of a hands-on learner, check out our [tutorials](/docs/tutorials).
This is the best place to get started.

These are the best ones to get started with:
- [Build a Simple LLM Application](/docs/tutorials/llm_chain)
- [Build a Chatbot](/docs/tutorials/chatbot)
- [Build an Agent](/docs/tutorials/agents)

Explore the full list of tutorials [here](/docs/tutorials).


## [How-to guides](/docs/how_to)

[Here](/docs/how_to) you’ll find short answers to “How do I….?” types of questions.
These how-to guides don’t cover topics in depth – you’ll find that material in the [Tutorials](/docs/tutorials) and the [API Reference](https://api.python.langchain.com/en/latest/).
However, these guides will help you quickly accomplish common tasks.

## [Conceptual guide](/docs/concepts)

Introductions to all the key parts of LangChain you’ll need to know! [Here](/docs/concepts) you'll find high level explanations of all LangChain concepts.

## [API reference](https://api.python.langchain.com)
Head to the reference section for full documentation of all classes and methods in the LangChain Python packages.

## Ecosystem

### [🦜🛠️ LangSmith](https://docs.smith.langchain.com)
Trace and evaluate your language model applications and intelligent agents to help you move from prototype to production.

### [🦜🕸️ LangGraph](https://langchain-ai.github.io/langgraph)
Build stateful, multi-actor applications with LLMs, built on top of (and intended to be used with) LangChain primitives.

### [🦜🏓 LangServe](/docs/langserve)
Deploy LangChain runnables and chains as REST APIs.


## Additional resources

### [Security](/docs/security)
Read up on our [Security](/docs/security) best practices to make sure you're developing safely with LangChain.

### [Integrations](/docs/integrations/providers/)
LangChain is part of a rich ecosystem of tools that integrate with our framework and build on top of it. Check out our growing list of [integrations](/docs/integrations/providers/).

### [Contributing](/docs/contributing)
Check out the developer's guide for guidelines on contributing and help getting your dev environment set up.