> ## Documentation Index
> Fetch the complete documentation index at: https://langchain-5e9cc07a-preview-provid-1764959672-4860de7.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# LangChain overview

<Callout icon="bullhorn" color="#DFC5FE" iconType="regular">
  **LangChain v1.x is now available!**

  For a complete list of changes and instructions on how to upgrade your code, see the [release notes](/oss/python/releases/langchain-v1) and [migration guide](/oss/python/migrate/langchain-v1).

  If you encounter any issues or have feedback, please [open an issue](https://github.com/langchain-ai/docs/issues/new?template=01-langchain.yml) so we can improve. To view v0.x documentation, [go to the archived content](https://github.com/langchain-ai/langchain/tree/v0.3/docs/docs) and [API reference](https://reference.langchain.com/v0.3/python/).
</Callout>

LangChain is the easiest way to start building agents and applications powered by LLMs. With under 10 lines of code, you can connect to OpenAI, Anthropic, Google, and [more](/oss/python/integrations/providers/overview). LangChain provides a pre-built agent architecture and model integrations to help you get started quickly and seamlessly incorporate LLMs into your agents and applications.

We recommend you use LangChain if you want to quickly build agents and autonomous applications. Use [LangGraph](/oss/python/langgraph/overview), our low-level agent orchestration framework and runtime, when you have more advanced needs that require a combination of deterministic and agentic workflows, heavy customization, and carefully controlled latency.

LangChain [agents](/oss/python/langchain/agents) are built on top of LangGraph in order to provide durable execution, streaming, human-in-the-loop, persistence, and more. You do not need to know LangGraph for basic LangChain agent usage.

## <Icon icon="wand-magic-sparkles" /> Create an agent

```python theme={null}
# pip install -qU langchain "langchain[anthropic]"
from langchain.agents import create_agent

def get_weather(city: str) -> str:
    """Get weather for a given city."""
    return f"It's always sunny in {city}!"

agent = create_agent(
    model="claude-sonnet-4-5-20250929",
    tools=[get_weather],
    system_prompt="You are a helpful assistant",
)

# Run the agent
agent.invoke(
    {"messages": [{"role": "user", "content": "what is the weather in sf"}]}
)
```

See the [Installation instructions](/oss/python/langchain/install) and [Quickstart guide](/oss/python/langchain/quickstart) to get started building your own agents and applications with LangChain.

## <Icon icon="star" size={20} /> Core benefits

<Columns cols={2}>
  <Card title="Standard model interface" icon="arrows-rotate" href="/oss/python/langchain/models" arrow cta="Learn more">
    Different providers have unique APIs for interacting with models, including the format of responses. LangChain standardizes how you interact with models so that you can seamlessly swap providers and avoid lock-in.
  </Card>

  <Card title="Easy to use, highly flexible agent" icon="wand-magic-sparkles" href="/oss/python/langchain/agents" arrow cta="Learn more">
    LangChain's agent abstraction is designed to be easy to get started with, letting you build a simple agent in under 10 lines of code. But it also provides enough flexibility to allow you to do all the context engineering your heart desires.
  </Card>

  <Card title="Built on top of LangGraph" icon="circle-nodes" href="/oss/python/langgraph/overview" arrow cta="Learn more">
    LangChain's agents are built on top of LangGraph. This allows us to take advantage of LangGraph's durable execution, human-in-the-loop support, persistence, and more.
  </Card>

  <Card title="Debug with LangSmith" icon="eye" href="/langsmith/home" arrow cta="Learn more">
    Gain deep visibility into complex agent behavior with visualization tools that trace execution paths, capture state transitions, and provide detailed runtime metrics.
  </Card>
</Columns>

***

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  [Edit the source of this page on GitHub.](https://github.com/langchain-ai/docs/edit/main/src/oss/langchain/overview.mdx)
</Callout>

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