---
title: "LangGraph"
description: "Learn about using Sentry for LangGraph."
url: https://docs.sentry.io/platforms/python/integrations/langgraph/
---

# LangGraph | Sentry for Python

This integration connects Sentry with [LangGraph](https://github.com/langchain-ai/langgraph) in Python.

Once you've installed this SDK, you can use Sentry AI Agents Monitoring, a Sentry dashboard that helps you understand what's going on with your AI requests. Sentry AI Monitoring will automatically collect information about prompts, tools, tokens, and models. Learn more about the [AI Agents Dashboard](https://docs.sentry.io/ai/monitoring/agents.md).

## [Install](https://docs.sentry.io/platforms/python/integrations/langgraph.md#install)

Install `sentry-sdk` from PyPI:

```bash
pip install sentry-sdk
```

## [Configure](https://docs.sentry.io/platforms/python/integrations/langgraph.md#configure)

If you have the `langgraph` package in your dependencies, the LangGraph integration will be enabled automatically when you initialize the Sentry SDK.

```python
import sentry_sdk

sentry_sdk.init(
    dsn="___PUBLIC_DSN___",
    environment="local",
    traces_sample_rate=1.0,
    send_default_pii=True
)
```

## [Verify](https://docs.sentry.io/platforms/python/integrations/langgraph.md#verify)

Verify that the integration works by creating a LangGraph workflow and executing it. In these examples, we're creating a simple agent graph that can use a function tool to roll a die.

```python
import os
import random
from typing import Annotated, Literal, TypedDict

from langchain.chat_models import init_chat_model
from langchain_core.messages import AnyMessage, HumanMessage
from langchain_core.tools import tool
from langgraph.graph import END, StateGraph
from langgraph.graph.message import add_messages
from langgraph.prebuilt import ToolNode


class State(TypedDict):
    messages: Annotated[list[AnyMessage], add_messages]

@tool
def roll_die(sides: int = 6) -> str:
    """Roll a die with a given number of sides"""
    return f"Rolled a {random.randint(1, sides)} on a {sides}-sided die."

def chatbot(state: State):
    model = init_chat_model("gpt-4o-mini", model_provider="openai")
    return {"messages": [model.bind_tools([roll_die]).invoke(state["messages"])]}

def should_continue(state: State) -> Literal["tools", END]:
    last_message = state["messages"][-1]
    return "tools" if getattr(last_message, "tool_calls", None) else END

with sentry_sdk.start_transaction(name="langgraph-openai"):
    graph_builder = StateGraph(State)
    graph_builder.add_node("chatbot", chatbot)
    graph_builder.add_node("tools", ToolNode([roll_die]))
    graph_builder.set_entry_point("chatbot")
    graph_builder.add_conditional_edges("chatbot", should_continue)
    graph_builder.add_edge("tools", "chatbot")
    graph = graph_builder.compile()
    result = graph.invoke({
        "messages": [
            HumanMessage(content="Hello, my name is Alice! Please roll a six-sided die.")
        ]
    })
    print(result)
```

After running this script, the resulting data should show up in the `"AI Spans"` tab on the `"Explore" > "Traces"` page on Sentry.io, and in the [AI Agents Dashboard](https://docs.sentry.io/ai/monitoring/agents.md).

It may take a couple of moments for the data to appear in [sentry.io](https://sentry.io).

## [Behavior](https://docs.sentry.io/platforms/python/integrations/langgraph.md#behavior)

* The LangGraph integration will connect Sentry with all supported LangGraph methods automatically.

* All exceptions are reported.

## [Options](https://docs.sentry.io/platforms/python/integrations/langgraph.md#options)

By adding `LanggraphIntegration` to your `sentry_sdk.init()` call explicitly, you can set options for `LanggraphIntegration` to change its behavior:

```python
import sentry_sdk
from sentry_sdk.integrations.langgraph import LanggraphIntegration

sentry_sdk.init(
    # ...
    # Add data like inputs and responses;
    # see https://docs.sentry.io/platforms/python/data-management/data-collected/ for more info
    send_default_pii=True,
    integrations=[
        LanggraphIntegration(
            include_prompts=False,  # LLM inputs/outputs will be not sent to Sentry, despite send_default_pii=True
        ),
    ],
)
```

You can pass the following keyword arguments to `LanggraphIntegration()`:

* `include_prompts`

  Controls whether LLM inputs and outputs should be sent to Sentry. Sentry considers this data personal identifiable data (PII) by default. If you want to include the data, set `send_default_pii=True` in the `sentry_sdk.init()` call. To explicitly exclude prompts and outputs despite `send_default_pii=True`, configure the integration with `include_prompts=False`.

  The default is `True`.

## [Supported Versions](https://docs.sentry.io/platforms/python/integrations/langgraph.md#supported-versions)

* OpenAI: 1.0+
* Python: 3.9+
* LangGraph: 0.6.6+