TracesMetricsApp LogsCustom LogsProfiling

This guide walks you through setting up Application Performance Monitoring (APM) on a Python application. These instructions can also be found on the Installation page in your Middleware Account. View example code here.



Infra Agent

Infrastructure Agent (Infra Agent). To install the Infra Agent, see our Installation Guide.


Python Version

Python 3 version 3.8 or above. Check your Python version with the following command:

python3 --version

Pip Version

pip version 23.1.2 or above. Check your pip version with the following command:

pip --version


Step 1: Install Python APM Package

Run the following command in your terminal:

pip install middleware-apm

Check if the middleware-apm has been installed with the following command:

pip list

Step 2: Import Middleware Tracker

Add the following lines to the beginning of your application:

Python 3
import logging 
from middleware import MwTracker

Step 3: Container Variables [Optional]


Applications running in a container require an additional environment variable. If your application is not running in a container, move to Step 4.

Add the following environment variable to your application:

The DOCKER_BRIDGE_GATEWAY_ADDRESS is the IP address of the gateway between the Docker host and bridge network. This is by default. Learn more about Docker bridge networking here

Add the following command to your Dockerfile after the pip install command:

RUN middleware-bootstrap -a install


Identify the namespace where the Infra Agent is running:

kubectl get service --all-namespaces | grep mw-service

Then add the following environment variable to your application deployment YAML file:


Step 4: Capture Application Data

Step 4a: Setup middleware.ini File

Create a middleware.ini file based on the features you want to observe and place it at the root of your app directory. Specify the location of the middleware.ini file with the MIDDLEWARE_CONFIG_FILE environment variable.

service_name and access_token are required for the tracker to send data to Middleware.

# The name of your application as service-name, as it will appear in the UI to filter out your data.
service_name = {APM-SERVICE-NAME}

# This Token binds the Python Agent's data and profiling data to your account.
access_token = {YOUR-ACCESS-TOKEN}

# The service name, where Infra Agent is running, in case of K8s.
mw_agent_service =

# Distributed traces for your application (false = disabled).
collect_traces = true

# Collection of metrics for your application (false = disabled).
collect_metrics = true

# Collection of logs for your application (false = disabled).
collect_logs = true

# Collection of profiling data for your application (false = disabled).
collect_profiling = true

Step 4b: Enable Custom Logs

To ingest custom logs, utilize the following functions based on desired log severity levels:

Python 3"info sample")
logging.warning("Sample Warning Log") 
logging.error("Sample Error Log.", extra={'tester': 'Alex'})

Step 4c: Stack Errors

Use tracker.record_error() method to record a stack trace when an error occurs:

Python 3
    not_possible = 12/0
except ZeroDivisionError as e:

Step 5: Deploy Your Django App [Optional]

If you are not using the Django framework in your Python application, proceed to Step 6.

Step 5a: Instrument Your App

Add the following to your main() function in


Step 5b: Start Your Django Project

Initialize your Django project with the following command:

DJANGO_SETTINGS_MODULE='mysite.settings' middleware-apm run python runserver

Step 5c: Start Your Django Project

After initializing your application, run the following command to start your project:

If the middleware.ini file is not in your root directory, add MIDDLEWARE_CONFIG_FILE=./path/to/middleware.ini to the below command
middleware-apm run python

Step 6: Start Your Project

After deploying your application, run the following command to start your project:

middleware-apm run python

Continuous Profiling

Continuous profiling captures real-time performance insights from your application to enable rapid identification of resource allocation, bottlenecks, and more. Navigate to the Continuous Profiling section to learn more about using Continuous Profiling with the Python APM.

Need assistance or want to learn more about Middleware? Contact us at support[at]