deepset-ai--haystack/docs-website/versioned_docs/version-2.31/pipeline-components/connectors/datadogconnector.mdx

---
title: "DatadogConnector"
id: datadogconnector
slug: "/datadogconnector"
description: "Learn how to work with Datadog in Haystack."
---

# DatadogConnector

Learn how to work with Datadog in Haystack.

<div className="key-value-table">

|  |  |
| --- | --- |
| **Most common position in a pipeline** | Anywhere, as it’s not connected to other components |
| **Mandatory init variables** | None. The connection to the Datadog backend is created at initialization time |
| **Output variables** | `name`: The name of the tracing component |
| **API reference** | [datadog](/reference/integrations-datadog) |
| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/datadog |
| **Package name** | `datadog-haystack` |

</div>

## Overview

`DatadogConnector` integrates tracing capabilities into Haystack pipelines using [Datadog](https://www.datadoghq.com/), through [Datadog's tracing library `ddtrace`](https://ddtrace.readthedocs.io/en/stable/). It captures detailed information about pipeline runs, like API calls, context data, prompts, and more, so you can see the complete trace of your pipeline execution in Datadog.

Datadog tracing is enabled as soon as the `DatadogConnector` is initialized, so you only need to add it to your pipeline – it does not need to be connected to other components or to run to take effect.

You can optionally pass a `name` to identify this tracing component (it defaults to `datadog`).

### Prerequisites

These are the things that you need before working with the `DatadogConnector`:

1. A way to receive traces, such as a running [Datadog Agent](https://docs.datadoghq.com/agent/). `ddtrace` sends traces to the Datadog Agent at `localhost:8126` by default.
2. Set the `HAYSTACK_CONTENT_TRACING_ENABLED` environment variable to `true` – this will enable content tracing (inputs and outputs) in your pipelines.
3. Configure `ddtrace` through the standard mechanisms, for example the `DD_SERVICE`, `DD_ENV`, and `DD_VERSION` environment variables, or by running your application with the `ddtrace-run` command. See the [ddtrace documentation](https://ddtrace.readthedocs.io/en/stable/) for more details.

### Installation

First, install the `datadog-haystack` package to use the `DatadogConnector`:

```shell
pip install datadog-haystack
```

<br />

:::info[Usage Notice]

To ensure proper tracing, always set environment variables before importing any Haystack components. This is crucial because Haystack initializes its internal tracing components during import. In the example below, we first set the environment variables and then import the relevant Haystack components.

Alternatively, an even better practice is to set these environment variables in your shell before running the script. This approach keeps configuration separate from code and allows for easier management of different environments.
:::

## Usage

In the example below, we are adding `DatadogConnector` to the pipeline as a _tracer_. Each pipeline run will produce a trace that includes the entire execution context, including prompts, completions, and metadata. You can then view the traces in your Datadog dashboard.

```python
import os

os.environ["HAYSTACK_CONTENT_TRACING_ENABLED"] = "true"

from haystack import Pipeline
from haystack.components.builders import ChatPromptBuilder
from haystack.components.generators.chat import OpenAIChatGenerator
from haystack.dataclasses import ChatMessage

from haystack_integrations.components.connectors.datadog import DatadogConnector

pipe = Pipeline()
pipe.add_component("tracer", DatadogConnector("Chat example"))
pipe.add_component("prompt_builder", ChatPromptBuilder())
pipe.add_component("llm", OpenAIChatGenerator())
pipe.connect("prompt_builder.prompt", "llm.messages")

messages = [
    ChatMessage.from_system(
        "Always respond in German even if some input data is in other languages.",
    ),
    ChatMessage.from_user("Tell me about {{location}}"),
]

response = pipe.run(
    data={
        "prompt_builder": {
            "template_variables": {"location": "Berlin"},
            "template": messages,
        },
    },
)
print(response["llm"]["replies"][0])
```

### With an Agent

```python
import os

os.environ["HAYSTACK_CONTENT_TRACING_ENABLED"] = "true"

from typing import Annotated

from haystack.components.agents import Agent
from haystack.components.generators.chat import OpenAIChatGenerator
from haystack.dataclasses import ChatMessage
from haystack.tools import tool
from haystack import Pipeline

from haystack_integrations.components.connectors.datadog import DatadogConnector


@tool
def get_weather(city: Annotated[str, "The city to get weather for"]) -> str:
    """Get current weather information for a city."""
    weather_data = {
        "Berlin": "18°C, partly cloudy",
        "New York": "22°C, sunny",
        "Tokyo": "25°C, clear skies",
    }
    return weather_data.get(city, f"Weather information for {city} not available")


@tool
def calculate(
    operation: Annotated[
        str,
        "Mathematical operation: add, subtract, multiply, divide",
    ],
    a: Annotated[float, "First number"],
    b: Annotated[float, "Second number"],
) -> str:
    """Perform basic mathematical calculations."""
    if operation == "add":
        result = a + b
    elif operation == "subtract":
        result = a - b
    elif operation == "multiply":
        result = a * b
    elif operation == "divide":
        if b == 0:
            return "Error: Division by zero"
        result = a / b
    else:
        return f"Error: Unknown operation '{operation}'"

    return f"The result of {a} {operation} {b} is {result}"


# Create the chat generator
chat_generator = OpenAIChatGenerator()

# Create the agent with tools
agent = Agent(
    chat_generator=chat_generator,
    tools=[get_weather, calculate],
    system_prompt="You are a helpful assistant with access to weather and calculator tools. Use them when needed.",
    exit_conditions=["text"],
)

# Create the DatadogConnector for tracing
datadog_connector = DatadogConnector("Agent Example")

# Build the pipeline
pipe = Pipeline()
pipe.add_component("tracer", datadog_connector)
pipe.add_component("agent", agent)

# Run the pipeline
response = pipe.run(
    data={
        "agent": {
            "messages": [
                ChatMessage.from_user(
                    "What's the weather in Berlin and calculate 15 + 27?",
                ),
            ],
        },
        "tracer": {},
    },
)

# Display results
print("Agent Response:")
print(response["agent"]["last_message"].text)
```

### Configuring the tracing backend directly

Instead of using the `DatadogConnector`, you can configure the Datadog tracing backend directly by enabling a `DatadogTracer`. Make sure to set the `HAYSTACK_CONTENT_TRACING_ENABLED` environment variable before importing any Haystack components.

```python
import ddtrace

from haystack import tracing
from haystack_integrations.tracing.datadog import DatadogTracer

tracing.enable_tracing(DatadogTracer(ddtrace.tracer))
```