DSPy Tracing
Instrument and observe your DSPy application via the DSPyInstrumentor
DSPy is a framework for automatically prompting and fine-tuning language models. It provides composable and declarative APIs that allow developers to describe the architecture of their LLM application in the form of a "module" (inspired by PyTorch's nn.Module
). It them compiles these modules using "teleprompters" that optimize the module for a particular task. The term "teleprompter" is meant to evoke "prompting at a distance," and could involve selecting few-shot examples, generating prompts, or fine-tuning language models.
Phoenix makes your DSPy applications observable by visualizing the underlying structure of each call to your compiled DSPy module.
Launch Phoenix
Sign up for Phoenix:
Sign up for an Arize Phoenix account at https://app.phoenix.arize.com/login
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
Create your API key from the Settings page
Copy your
Hostname
from the Settings pageIn 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')}"
Install
pip install openinference-instrumentation-dspy openinference-instrumentation-litellm dspy
Setup
Connect to your Phoenix instance using the register function.
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
)
Run DSPy
Now run invoke your compiled DSPy module. Your traces should appear inside of Phoenix.
class BasicQA(dspy.Signature):
"""Answer questions with short factoid answers."""
question = dspy.InputField()
answer = dspy.OutputField(desc="often between 1 and 5 words")
if __name__ == "__main__":
turbo = dspy.OpenAI(model="gpt-3.5-turbo")
dspy.settings.configure(lm=turbo)
with using_attributes(
session_id="my-test-session",
user_id="my-test-user",
metadata={
"test-int": 1,
"test-str": "string",
"test-list": [1, 2, 3],
"test-dict": {
"key-1": "val-1",
"key-2": "val-2",
},
},
tags=["tag-1", "tag-2"],
prompt_template_version="v1.0",
prompt_template_variables={
"city": "Johannesburg",
"date": "July 11th",
},
):
# Define the predictor.
generate_answer = dspy.Predict(BasicQA)
# Call the predictor on a particular input.
pred = generate_answer(
question="What is the capital of the united states?" # noqa: E501
) # noqa: E501
print(f"Predicted Answer: {pred.answer}")
Observe
Now that you have tracing setup, all predictions will be streamed to your running Phoenix for observability and evaluation.

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