DSPy - TraceRoot

Automatically capture module executions, signature predictions, and underlying LLM calls in DSPy programs.

Setup

Python

import traceroot
from traceroot import Integration

traceroot.initialize(integrations=[Integration.DSPY])

Usage

Once initialized, every DSPy module call and underlying LLM request is traced automatically:

import dspy
import traceroot
from traceroot import Integration

traceroot.initialize(integrations=[Integration.DSPY])

# DSPy resolves the API key from OPENAI_API_KEY in the environment.
dspy.configure(lm=dspy.LM("openai/gpt-4o-mini", max_tokens=1024))


class CoTQA(dspy.Module):
    """Chain-of-thought question-answering module."""

    def __init__(self):
        super().__init__()
        self.cot = dspy.ChainOfThought("question -> answer")

    def forward(self, question: str):
        return self.cot(question=question)


qa = CoTQA()

# The forward call, the chain-of-thought step, and the LLM call are all captured
result = qa(question="Why does ice float on water?")
print(result.reasoning)
print(result.answer)

traceroot.flush()

What Gets Captured

Attribute	Description
Module calls	Each `Module.__call__` / `Module.forward` invocation
Predictors	`Predict`, `ChainOfThought`, `ReAct`, etc. as nested spans
Signatures	Input/output fields declared on each signature
LLM calls	Raw completion requests to the configured `dspy.LM`
Tokens & Cost	Aggregated token usage and pricing
Latency	Duration per module call and per LLM call

Run the example

Clone the repo and run a complete agent end-to-end.

Python

Run the Python example