Get Started with Python Error Reporting

Python is one of the most industry-oriented languages. It has versatile usability and features that are great for all types of tech teams. You can make both frontend websites and backend APIs with Python. According to a report by Statista, around 48.24% of the developers worldwide use Python as one of their programming languages. 

Python is a backend technology in most cases, so monitoring the application is very important. The backend is the backbone of the application. If your backend fails, you may lose a lot of potential leads for your application. Using python error reporting, you can simply check the issue behind your application’s bad code, and the team of developers and managers could get behind the issue and solve it. 

Python error monitoring also helps to increase productivity by letting them fix the error-heavy code in the application beforehand so that they can solve issues in one release and not need to devote extra time to each of them separately.

In this article, I will explain how you can get started with error monitoring of your python application, what are the important criteria you should consider while monitoring, and the difference between available tools and Scout APM for python error monitoring. You can use the following links to navigate the guide:

Real Benefits of Python Error Monitoring

Let’s understand some of the major benefits of python error monitoring, why we should focus on python error monitoring, and how it will impact our businesses.

Get Real-time Alerts for Your Application

When you integrate error monitoring tools with your python application, you can get real-time alerts about your application’s performance. This means whenever something crashes in your application, you will get notified instantly. The notification can reach you via email, Slack, Jira, Github, etc. 

One of the benefits you get with these real-time alerts is you can solve the issues with your application before a large part of your audience is affected by the bugs. Sometimes the bug can be inside the architecture, like database values or nomenclature.

Error Logs and Details

When there is proper error logging and monitoring mechanism in your python application, you get each and every detail about the bugs, like where they happened, why they happened, and what they impacted. Using these details, you can find out what is the best way to solve that bug. Detailed reports can also be preserved for future reference so that whenever you refer to a previous bug, you can look at the previous solution.

Complete Stack Traces

When your python application fails, sometimes the error statements can be vague, and it can be difficult to catch the exact reason for the failure. Tracing is a tool that helps in seeing the overall flow of the code in one view. Seeing the traces can show you the values that are passed in subsequent functions so you can see which functions actually caused the error. Tracing can be seen in the form of both graphs and logs for ease of readability. You can also get tracing results on various platforms like Slack, Github, etc.

Stop Guessing Bugs

As a developer, it would be a big headache if you had to guess bugs. Python error monitoring helps you shift from just randomly guessing the bugs to making decisions based on proof. Using detailed data and logs, you can see the exact place where bugs are happening, and you can fix them directly without any hit and trial attempts. Python error monitoring also shows how many users have been affected due to the particular bug and gives priority accordingly.

How to Get Started with Python Error Monitoring

After seeing the actual and practical benefits of python error monitoring, we will explore how to get started with python error monitoring using Scout APM. Scout supports many other languages in addition to python for application performance management. Using Scout's error-monitoring system, you can get the exact data that you need and get the benefits of APM and error monitoring tools in a single tool.

Scout APM supports error monitoring with Python 2.7+ only. You can automate error reporting for apps using Django 1.8+, Flask 1+, or Celery 1.3+. Now without wasting time, let us part the process of getting started with python error monitoring with ScoutAPM. 

Step 1: Update to The Latest Version of Scout

First of all, update the Scout APM if you have not done it yet. You can use the following command for updating the Scout agent.

pip install scout-apm --upgrade

Step 2: Update the Configuration

In the Scout config file, you have set the monitor and errors_enabled flag to be true. If you are using a config file, then you can do it like this.

from scout_apm.api import Config


    # ... other configuration options


    errors_enabled=True,  # Defaults to true since v2.23.0


You can see more about the config file in its official documentation here. If you are using environment variables, then you can set the value of environment variables in your local computer.



Step 3: Deploy Your Application

In this step, you will deploy your application after doing all the changes required above. After successful deployment, you will see your application's error report and monitoring in the Scout APM dashboard

In the dashboard, you can see the metrics using different UI elements like graphs, charts, etc. You can see the information about a particular time using the click and zoom method and see the relative behavior with the previous data. If you face any problems while installing the Scout APM error monitoring or application monitoring libraries, then you can simply contact support by emailing

Python Error Monitoring Tools

Before diving into the best python error monitoring tools available in the market, you must decide what the exact requirements of your application are. Let’s discuss what we should look for in such tools.

What to Look For in an Error Monitoring Tool

An ideal python monitoring tool should be capable of monitoring your application end-to-end. It should show you the in-depth metrics for your applications and ultimately help to increase the throughput of your application. A good python error monitoring tool gives you all the traces of errors, which means you should be able to see the path followed by the code which leads to the error. Using this, you can easily figure out where the code went wrong.

Application performance monitoring is also an important feature for error monitoring tools as it can show you the error percentage and number of users affected by a particular user. It should also support python's latest version and various python frameworks like Django or Flask, metrics on customer retention, error percentage, and ways to tackle this error.

Scout Error Monitoring

Scout APM is a set of modern tools integrated with all of the features required for modern businesses for application monitoring. It provides various services like application monitoring, infrastructure monitoring, error tracking, etc. You can use different notification channels like Slack and Github to get real-time updates about your application and fix the bugs as soon as possible. 

For Python applications, the monitoring is really simple using Scout APM. You just have to follow a few basic steps as discussed above to get started. You can automate the error monitoring process with the help of Scout APM.

AppOptics APM

AppOptics APM is a product developed by SolarWinds. It is a ready-to-go tool for full-stack monitoring of python applications. It collects all the necessary metrics, logs, and traces data of your python application. Like many other apps, you can connect AppOptics with your notification channel so that you can get alerts on different handles like slack, WhatsApp, or Github.

Some of the features of AppOptics APM include custom dashboards for metrics, 150+ plugins, and integrations, automatics error monitoring, etc.


Datadog’s APM is also a famous tool for python application monitoring. It provides you with end-to-end monitoring for your application and gives error reports directly on your social media channel, be  It slack, Github, etc. Some of the main features of the Datadog APM include:

Datadog’s products include infrastructure monitoring, log management, APM, synthetic monitoring, real user monitoring, security platform, etc.


Dynatrace is one of the oldest applications in the monitoring industry. It provides various kinds of solutions like AWS, Azure, Google Cloud, etc. It provides various platforms like infrastructure monitoring, application monitoring, and error monitoring. For python applications, it provides various features like:

Some of the special features of Dynatrace include AI-based monitoring, full-stack observability, etc. Their support is excellent, and they have elaborate documentation to help developers integrate Dyntrace solutions. Their customer service, called Dynatrace One, is available for any customer anytime, and they promise to solve their issue within minutes.

Save Time and Money with Scout Error Monitoring

We have seen a lot about python error monitoring as well as what we should care about while monitoring our python application. Python error monitoring can help in expanding businesses in many ways, like predicting customer behavior, detecting and solving bugs easily, and making the cost of managing the application low. For choosing the best error monitoring tool for your python application, there are many options available, but before choosing anything, you should define your requirements and application type clearly. 

Scout APM makes your life easy in monitoring errors for your python app, and they offer a 14-day free trial without showing any credit card. So what are you waiting for? Go and start your free trial on their home page.