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 [components](/docs/concepts) and [third-party integrations](/docs/integrations/providers/).
Use [LangGraph](/docs/concepts/architecture/#langgraph) to build stateful agents with first-class streaming and human-in-the-loop support.
- **Productionization**: Use [LangSmith](https://docs.smith.langchain.com/) to inspect, monitor and evaluate your applications, so that you can continuously optimize and deploy with confidence.
- **Deployment**: Turn your LangGraph applications into production-ready APIs and Assistants with [LangGraph Platform](https://langchain-ai.github.io/langgraph/cloud/).

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<ThemedImage
  alt="Diagram outlining the hierarchical organization of the LangChain framework, displaying the interconnected parts across multiple layers."
  sources={{
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  title="LangChain Framework Overview"
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LangChain implements a standard interface for large language models and related
technologies, such as embedding models and vector stores, and integrates with
hundreds of providers. See the [integrations](/docs/integrations/providers/) page for
more.

import ChatModelTabs from "@theme/ChatModelTabs";

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```python
model.invoke("Hello, world!")
```

:::note

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

:::

## Architecture

The LangChain framework consists of multiple open-source libraries. Read more in the
[Architecture](/docs/concepts/architecture/) page.

- **`langchain-core`**: Base abstractions for chat models and other components.
- **Integration packages** (e.g. `langchain-openai`, `langchain-anthropic`, etc.): Important integrations have been split into lightweight packages that are co-maintained by the LangChain team and the integration developers.
- **`langchain`**: Chains, agents, and retrieval strategies that make up an application's cognitive architecture.
- **`langchain-community`**: Third-party integrations that are community maintained.
- **`langgraph`**: Orchestration framework for combining LangChain components into production-ready applications with persistence, streaming, and other key features. See [LangGraph documentation](https://langchain-ai.github.io/langgraph/).

## Guides

### [Tutorials](/docs/tutorials)

If you're looking to build something specific or are more of a hands-on learner, check out our [tutorials section](/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)
- [Introduction to LangGraph](https://langchain-ai.github.io/langgraph/tutorials/introduction/)

Explore the full list of LangChain tutorials [here](/docs/tutorials), and check out other [LangGraph tutorials here](https://langchain-ai.github.io/langgraph/tutorials/). To learn more about LangGraph, check out our first LangChain Academy course, *Introduction to LangGraph*, available [here](https://academy.langchain.com/courses/intro-to-langgraph).


### [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://python.langchain.com/api_reference/).
However, these guides will help you quickly accomplish common tasks using [chat models](/docs/how_to/#chat-models),
[vector stores](/docs/how_to/#vector-stores), and other common LangChain components.

Check out [LangGraph-specific how-tos here](https://langchain-ai.github.io/langgraph/how-tos/).

### [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.

For a deeper dive into LangGraph concepts, check out [this page](https://langchain-ai.github.io/langgraph/concepts/).

### [Integrations](integrations/providers/index.mdx)

LangChain is part of a rich ecosystem of tools that integrate with our framework and build on top of it.
If you're looking to get up and running quickly with [chat models](/docs/integrations/chat/), [vector stores](/docs/integrations/vectorstores/),
or other LangChain components from a specific provider, check out our growing list of [integrations](/docs/integrations/providers/).


### [API reference](https://python.langchain.com/api_reference/)
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. Integrates smoothly with LangChain, but can be used without it. LangGraph powers production-grade agents, trusted by Linkedin, Uber, Klarna, GitLab, and many more.

## Additional resources

### [Versions](/docs/versions/v0_3/)
See what changed in v0.3, learn how to migrate legacy code, read up on our versioning policies, and more.

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

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