dspy-debugging-observability

DSPy Debugging & Observability

Goal

Debug, trace, and monitor DSPy programs using built-in inspection, MLflow tracing, and custom callbacks for production observability.

When to Use

• Debugging unexpected outputs
• Understanding multi-step program flow
• Production monitoring (cost, latency, errors)
• Analyzing optimizer behavior
• Tracking LLM API usage

Related Skills

• Optimize programs: dspy-miprov2-optimizer
• Evaluate quality: dspy-evaluation-suite
• Build agents: dspy-react-agent-builder

Inputs

Input	Type	Description
program	dspy.Module	Program to debug/monitor
callback	BaseCallback	Optional custom callback (subclass of dspy.utils.callback.BaseCallback)

Outputs

Output	Type	Description
GLOBAL_HISTORY	list[dict]	Raw execution trace from dspy.clients.base_lm
metrics	dict	Cost, latency, token counts from callbacks

Workflow

Phase 1: Basic Inspection with inspect_history()

The simplest debugging approach:

import dspy

dspy.configure(lm=dspy.LM("openai/gpt-4o-mini"))

# Run program
qa = dspy.ChainOfThought("question -> answer")
result = qa(question="What is the capital of France?")

# Inspect last execution (prints to console)
dspy.inspect_history(n=1)

# To access raw history programmatically:
from dspy.clients.base_lm import GLOBAL_HISTORY
for entry in GLOBAL_HISTORY[-1:]:
    print(f"Model: {entry['model']}")
    print(f"Usage: {entry.get('usage', {})}")
    print(f"Cost: {entry.get('cost', 0)}")

Phase 2: MLflow Tracing

MLflow integration requires explicit setup:

import dspy
import mlflow

# Setup MLflow (4 steps required)
# 1. Set tracking URI and experiment
mlflow.set_tracking_uri("http://localhost:5000")
mlflow.set_experiment("DSPy")

# 2. Enable DSPy autologging
mlflow.dspy.autolog(
    log_traces=True,              # Log traces during inference
    log_traces_from_compile=True, # Log traces when compiling/optimizing
    log_traces_from_eval=True,    # Log traces during evaluation
    log_compiles=True,            # Log optimization process info
    log_evals=True                # Log evaluation call info
)

dspy.configure(lm=dspy.LM("openai/gpt-4o-mini"))

# Configure retriever (required before using dspy.Retrieve)
rm = dspy.ColBERTv2(url="http://20.102.90.50:2017/wiki17_abstracts")
dspy.configure(rm=rm)

class RAGPipeline(dspy.Module):
    def __init__(self):
        self.retrieve = dspy.Retrieve(k=3)
        self.generate = dspy.ChainOfThought("context, question -> answer")

    def forward(self, question):
        context = self.retrieve(question).passages
        return self.generate(context=context, question=question)

pipeline = RAGPipeline()
result = pipeline(question="What is machine learning?")

# View traces in MLflow UI (run in terminal): mlflow ui --port 5000