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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.

Quick setup? Instead of following these steps manually, copy a prompt into your coding agent and it will set this up for you automatically.
LangWatch integrates with Langchain to provide detailed observability into your chains, agents, LLM calls, and tool usage.

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Installation

pip install langwatch langchain langchain-openai

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Usage

The LangWatch API key is configured by default via the LANGWATCH_API_KEY environment variable.
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. 
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.