Datadog

Learn how to trace your Haystack pipelines with Datadog.

Tracer class	DatadogTracer
How to enable	Enable the tracer with tracing.enable_tracing(DatadogTracer(ddtrace.tracer)), or add the DatadogConnector component to your pipeline
Content tracing	Set HAYSTACK_CONTENT_TRACING_ENABLED to true to trace component inputs and outputs
Package	datadog-haystack
API reference	datadog
GitHub link	https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/datadog

Overview

Trace your Haystack pipelines with Datadog through Datadog's tracing library ddtrace. Haystack captures detailed information about pipeline runs, like API calls, context data, and prompts, so you can see the complete trace of your pipeline execution in Datadog.

Installation

Install the datadog-haystack package:

shell

pip install datadog-haystack

Prerequisites

1. A way to receive traces, such as a running Datadog Agent. ddtrace sends traces to the Datadog Agent at localhost:8126 by default.
2. 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 for more details.

Usage

Enable the DatadogTracer directly to trace any Haystack pipeline, without adding a component to it. Make sure to set the HAYSTACK_CONTENT_TRACING_ENABLED environment variable before importing any Haystack components.

python

import os

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

import ddtrace

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

from haystack_integrations.tracing.datadog import DatadogTracer

# Enable the Datadog tracer
tracing.enable_tracing(DatadogTracer(ddtrace.tracer))

pipe = Pipeline()
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])

Each pipeline run produces a trace that includes the entire execution context, including prompts, completions, and metadata. You can then view the traces in your Datadog dashboard.

Alternative: the DatadogConnector component

If you prefer to manage tracing as part of your pipeline definition (for example, so it serializes to YAML), you can add the DatadogConnector component instead. It enables the same Datadog tracing as soon as it is initialized.

info

See the DatadogConnector documentation page for full usage examples, or check out the integration page.