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Phoenix provides auto-instrumentation for Haystack

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Launch Phoenix

  • Phoenix Cloud
  • Command Line
  • Docker
  • Notebook
Sign up for Phoenix:
  1. Sign up for an Arize Phoenix account at https://app.phoenix.arize.com/login
  2. Click Create Space, then follow the prompts to create and launch your space.
Install packages:
pip install arize-phoenix-otel
Set your Phoenix endpoint and API Key:From your new Phoenix Space
  1. Create your API key from the Settings page
  2. Copy your Hostname from the Settings page
  3. In your code, set your endpoint and API key: 
import os

os.environ["PHOENIX_API_KEY"] = "ADD YOUR PHOENIX API KEY"
os.environ["PHOENIX_COLLECTOR_ENDPOINT"] = "ADD YOUR PHOENIX HOSTNAME"

# If you created your Phoenix Cloud instance before June 24th, 2025,
# you also need to set the API key as a header:
# os.environ["PHOENIX_CLIENT_HEADERS"] = f"api_key={os.getenv('PHOENIX_API_KEY')}"
Having trouble finding your endpoint? Check out Finding your Phoenix Endpoint

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Install

pip install openinference-instrumentation-haystack haystack-ai

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Setup

Use the register function to connect your application to Phoenix: 
from phoenix.otel import register

# configure the Phoenix tracer
tracer_provider = register(
  project_name="my-llm-app", # Default is 'default'
  auto_instrument=True # Auto-instrument your app based on installed OI dependencies
)

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Run Haystack

Phoenix’s auto-instrumentor collects any traces from Haystack Pipelines. If you are using Haystack but not using Pipelines, you won’t see traces appearing in Phoenix automatically.If you don’t want to use Haystack pipelines but still want tracing in Phoenix, you can use instead of this auto-instrumentor.
From here, you can set up your Haystack app as normal: 
from haystack import Pipeline
from haystack.components.generators import OpenAIGenerator
from haystack.components.builders.prompt_builder import PromptBuilder

prompt_template = """
Answer the following question.
Question: {{question}}
Answer:
"""

# Initialize the pipeline
pipeline = Pipeline()

# Initialize the OpenAI generator component
llm = OpenAIGenerator(model="gpt-3.5-turbo")
prompt_builder = PromptBuilder(template=prompt_template)

# Add the generator component to the pipeline
pipeline.add_component("prompt_builder", prompt_builder)
pipeline.add_component("llm", llm)
pipeline.connect("prompt_builder", "llm")

# Define the question
question = "What is the location of the Hanging Gardens of Babylon?"

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Observe

Now that you have tracing setup, all invocations of pipelines will be streamed to your running Phoenix for observability and evaluation.

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Resources: