> ## Documentation Index
> Fetch the complete documentation index at: https://langwatch.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# LangChain Instrumentation

> Instrument LangChain applications with LangWatch to trace chains, RAG flows, and metrics for AI agent evaluations.

LangWatch integrates with Langchain to provide detailed observability into your chains, agents, LLM calls, and tool usage.

## Installation

<CodeGroup>
  ```bash pip theme={null}
  pip install langwatch langchain langchain-openai
  ```

  ```bash uv theme={null}
  uv add langwatch langchain langchain-openai
  ```
</CodeGroup>

## Usage

<Info>
  The LangWatch API key is configured by default via the `LANGWATCH_API_KEY` environment variable.
</Info>

Use LangWatch's callback handler to instrument your Langchain agents and chains. The callback automatically captures LLM calls, tool usage, and chain execution as spans within your trace.

```python  theme={null}
import langwatch
from langchain.agents import create_agent
from langchain_openai import ChatOpenAI
from langchain_core.runnables import RunnableConfig
import os
import asyncio


langwatch.setup()


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


agent = create_agent(
    model="openai:gpt-5",
    tools=[get_weather],
    system_prompt="You are a helpful assistant, that can get the weather.",
)


@langwatch.trace(name="Langchain - Weather Agent")
def main(user_question: str):
    result = agent.invoke(
        {"messages": [{"role": "user", "content": user_question}]},
        config=RunnableConfig(
            callbacks=[langwatch.get_current_trace().get_langchain_callback()]
        ),
    )

    return result["messages"][-1].content


if __name__ == "__main__":
    result = main("What is the weather in Philadelphia?")
    print(result)
```

The `@langwatch.trace()` decorator creates a parent trace, and `get_langchain_callback()` provides a callback handler that captures Langchain events as spans. Pass the callback to your agent or chain's `RunnableConfig` to enable instrumentation.

## Related

* [Capturing RAG](/integration/python/tutorials/capturing-rag) - Learn how to capture RAG data from LangChain retrievers and tools
* [Capturing Metadata and Attributes](/integration/python/tutorials/capturing-metadata) - Add custom metadata and attributes to your traces and spans
* [Capturing Evaluations & Guardrails](/integration/python/tutorials/capturing-evaluations-guardrails) - Log evaluations and implement guardrails in your LangChain applications