How to Set Up Monitoring with DSPy (Step by Step)

How to Set Up Monitoring with DSPy

In this tutorial, we’ll walk through how to dspy set up monitoring for your projects. Monitoring is key to understanding what’s happening in your application, especially in production systems. DSPy is not just a machine learning framework, it’s your partner in ensuring everything runs smoothly.

Prerequisites

• Python 3.11+
• Pip install dspy==latest_version (Replace latest_version with the latest available)
• Access to a monitoring system or service (like OpenTelemetry or Prometheus)

Step 1: Install DSPy

First, you need to install DSPy. If you haven’t done this step, your entire monitoring setup is doomed to fail. Trust me, I’ve been there.

pip install dspy

Why install DSPy? It gives you pre-built tools for logic that you’re going to need for monitoring. You skip this? You’re basically flying blind.

Step 2: Set Up Basic Logging

Before you can monitor anything, you need to implement logging. Here’s how to initiate DSPy’s built-in logging.

import logging
from dspy import Dspy

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Initialize DSPy
dspy = Dspy()
logger.info("DSPy initialized")

Why logging? Because you need a record of what’s happening. If something goes wrong, those logs will be your only friends.

Step 3: Configure Monitoring Settings

At this stage, it’s time to configure your monitoring settings. You should ensure that your setup meets your application needs.

# Configure monitoring settings
dspy.configure_monitoring({
 'log_level': 'INFO',
 'reporting_interval': 60, # seconds
})

Keep in mind that a too frequent monitoring interval might lead to performance issues. It’s a balancing act; I once set it to every second and my app became slower than a dial-up connection.

Step 4: Implement Metrics Collection

To truly understand how your application behaves, you need to collect metrics. DSPy allows for detailed metrics tracking.

import time

def collect_metrics():
 while True:
 metrics = dspy.get_metrics()
 logger.info(f"Collected Metrics: {metrics}")
 time.sleep(dspy.config['reporting_interval'])

This function runs indefinitely unless you stop it. It’s basically polling. You might run into issues if you forget to set a break condition and end up in an infinite loop — trust me, it happened to me on a Friday evening.

Step 5: Integration with OpenTelemetry

For powerful monitoring and reporting, integrate with OpenTelemetry. This is where the real monitoring magic happens.

from opentelemetry import trace
from opentelemetry.exporter import otlp

# Create an OTLP exporter
otlp_exporter = otlp.OTLPSpanExporter()

# Setup tracing with OpenTelemetry
tracer = trace.get_tracer("dspy.monitoring")
trace.set_tracer_provider(trace.TracerProvider())
logger.info("OpenTelemetry set up for tracing")

You’ll need OpenTelemetry installed: pip install opentelemetry-api opentelemetry-sdk. If you mess this step up, you’ll be begging for insights but won’t get any. That’s usually not a great feeling.

Step 6: Monitor Application Logic

Now it’s time to monitor your application logic effectively.

@tracer.start_as_current_span("processing_event")
def process_event(data):
 logger.info(f"Processing data: {data}")
 # Add your processing logic here
 return True

This code sample wraps around your existing function logic, helping you track performance issues related to your application events. If you’re not covering this, you may miss eventually high-latency processes that annoy users.

The Gotchas

• Logging Levels: Setting your logging level to DEBUG in production is a mistake. Your logs will explode. Keep it at INFO or WARNING.
• Reporting Interval: A short reporting interval can lead to performance degradation. Test and monitor the impact before finalizing.
• Data Overload: Be careful about how much data you log. In the early stages, I logged everything and ended up with gigabytes of logs that were a pain to manage.
• OpenTelemetry Setup: Make sure the OpenTelemetry setup isn’t incomplete; you’ll find yourself missing crucial tracing information.

Full Code

import logging
import time
from dspy import Dspy
from opentelemetry import trace
from opentelemetry.exporter import otlp

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Initialize DSPy
dspy = Dspy()
logger.info("DSPy initialized")

# Configure monitoring settings
dspy.configure_monitoring({
 'log_level': 'INFO',
 'reporting_interval': 60, 
})

def collect_metrics():
 while True:
 metrics = dspy.get_metrics()
 logger.info(f"Collected Metrics: {metrics}")
 time.sleep(dspy.config['reporting_interval'])

# Create an OTLP exporter
otlp_exporter = otlp.OTLPSpanExporter()

# Setup tracing with OpenTelemetry
tracer = trace.get_tracer("dspy.monitoring")
trace.set_tracer_provider(trace.TracerProvider())
logger.info("OpenTelemetry set up for tracing")

@tracer.start_as_current_span("processing_event")
def process_event(data):
 logger.info(f"Processing data: {data}")
 return True

What’s Next

Now that you’ve set up monitoring with DSPy, take a moment to refine your metrics collection strategy. Metrics matter most when they are actionable. Set alerts based on the most critical metrics that matter to your business.

FAQ

• Can I use DSPy with other monitoring tools? Absolutely. You can integrate DSPy with other tools like Prometheus or Grafana for further insights.
• How do I know if my monitoring setup is working? Check the logs. If everything is correctly configured, you should see your metrics being collected based on your reporting interval.
• What if I get errors while running the monitoring? Errors usually stem from incorrect configurations. Double-check the setup and ensure all dependencies are installed correctly.

Data Sources

For additional information, check out the official documentation:

• DSPy GitHub Repository
• OpenLIT Documentation

Here’s a quick look at the current state of DSPy:

Stars	Forks	Open Issues	License	Last Updated
33,397	2,752	468	MIT	April 2, 2026

Last updated April 03, 2026. Data sourced from official docs and community benchmarks.

🕒 Published: April 2, 2026