Python observability  
with OpenTelemetry (OTel)

Troubleshooting Python-based microservices can be tough if you don’t have proper observability. One rather simple way to gain observability is by implementing OpenTelemetry (OTel). OTel has become an essential tool for Python developers, enabling them to generate, collect, and export observability data (metrics, logs, and traces) in cloud-native and microservices-based architectures.

OTel auto-instrumentation in Python

we at Helios are leveraging OTel to deliver more value for developers working in distributed systems and microservices. Our approach for OTel auto-instrumentation in Python is easy to implement and maintain and doesn’t require any code changes.

No code changes

No agent required - just `pip install` the Helios OpenTelemetry SDK and add a few environment variables - no need to change anything else.

Get all the context you need

Visualize complex sync and async flows, get a dynamic catalog of your APIs based on real traffic, get visibility into HTTP, gRPC and Kafka messages in the full context of their traces, and more. 

No magic tricks

The library's source code is compatible with OpenTelemetry. Easily understand what’s happening, and debug if necessary. 

The value of Helios for Python developers 

Investigate issues, reproduce scenarios, and generate tests faster with actionable data across your microservices, messaging systems, data pipelines, and databases.

Python Observability 

Visibility See your service map and API catalog easily

Interactions’ Payloads Gain visibility into the communication between various components and inspect payloads

Onboarding Allows new developers and SREs to easily understand your architecture

Troubleshooting in Python

Root Cause Analysis Quickly pinpoint the root cause of your issues using Stack trace, error logs, and trace correlation

Replay Flows Find failed requests, generate snippets, and re-run them with the same or different parameters

Identify bottlenecks in app flows Instantly identify and analyze bottlenecks

Python Testing

Pinpoint Failed Tests Efficiently pinpoint the root cause of failed tests and gain insight into tests running in CI/CD

Trace-based Testing Eliminate the need to build testing infrastructure by creating trace-based tests from real traffic

Autogenerate Tests From traces to improve validation


Share Traces, tests and triggers with your team

Collaborate Generate, test, build and edit tests together

Visibility Give your team visibility of issues in your local environment


Detailed instructions can be found in our docs.

Completely. The Helios OpenTelemetry SDK is built on top of OpenTelemetry’s Python SDK, and uses industry-standard instrumentation techniques.

No. Helios is free to use. 

Our instrumentation is configured to collect all the data you may need to troubleshoot your application and to make sure that context is propagated properly across different services and provides the best way to locate issues and identify their root cause faster than ever.

We support the same versions that are supported by OpenTelemetry, Python 3.7 or later.

We provide several methods of making sure your sensitive data remains confidential. You may collect metadata only (with no payloads at all) or obfuscate your payloads. If you need any more capabilities, feel free to reach out.

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