opentelemetry-go

Sun, 13 Jul 2025 15:29:29 +0800

open-telemetry/opentelemetry-go

OpenTelemetry-Go

OpenTelemetry-Go is the Go implementation of OpenTelemetry. It provides a set of APIs to directly measure performance and behavior of your software and send this data to observability platforms.

Project Status

Signal	Status
Traces	Stable
Metrics	Stable
Logs	Beta¹

Progress and status specific to this repository is tracked in our project boards and milestones.

Project versioning information and stability guarantees can be found in the versioning documentation.

Compatibility

OpenTelemetry-Go ensures compatibility with the current supported versions of the Go language:

Each major Go release is supported until there are two newer major releases. For example, Go 1.5 was supported until the Go 1.7 release, and Go 1.6 was supported until the Go 1.8 release.

For versions of Go that are no longer supported upstream, opentelemetry-go will stop ensuring compatibility with these versions in the following manner:

A minor release of opentelemetry-go will be made to add support for the new supported release of Go.
The following minor release of opentelemetry-go will remove compatibility testing for the oldest (now archived upstream) version of Go. This, and future, releases of opentelemetry-go may include features only supported by the currently supported versions of Go.

Currently, this project supports the following environments.

OS	Go Version	Architecture
Ubuntu	1.24	amd64
Ubuntu	1.23	amd64
Ubuntu	1.24	386
Ubuntu	1.23	386
Ubuntu	1.24	arm64
Ubuntu	1.23	arm64
macOS 13	1.24	amd64
macOS 13	1.23	amd64
macOS	1.24	arm64
macOS	1.23	arm64
Windows	1.24	amd64
Windows	1.23	amd64
Windows	1.24	386
Windows	1.23	386

While this project should work for other systems, no compatibility guarantees are made for those systems currently.

Getting Started

You can find a getting started guide on opentelemetry.io.

OpenTelemetry’s goal is to provide a single set of APIs to capture distributed traces and metrics from your application and send them to an observability platform. This project allows you to do just that for applications written in Go. There are two steps to this process: instrument your application, and configure an exporter.

Instrumentation

To start capturing distributed traces and metric events from your application it first needs to be instrumented. The easiest way to do this is by using an instrumentation library for your code. Be sure to check out the officially supported instrumentation libraries.

If you need to extend the telemetry an instrumentation library provides or want to build your own instrumentation for your application directly you will need to use the Go otel package. The examples are a good way to see some practical uses of this process.

Export

Now that your application is instrumented to collect telemetry, it needs an export pipeline to send that telemetry to an observability platform.

All officially supported exporters for the OpenTelemetry project are contained in the exporters directory.

Exporter	Logs	Metrics	Traces
OTLP	✓	✓	✓
Prometheus		✓
stdout	✓	✓	✓
Zipkin			✓

Contributing

See the contributing documentation.

https://github.com/orgs/open-telemetry/projects/43 ↩︎

opik

Wed, 14 May 2025 15:29:35 +0800

comet-ml/opik

English | 简体中文 | 日本語 | 한국어

Opik
Open source LLM evaluation framework

From RAG chatbots to code assistants to complex agentic pipelines and beyond, build LLM systems that run better, faster, and cheaper with tracing, evaluations, and dashboards.

Website • Slack community • Twitter • Documentation

Important change on version 1.7.0

Please check the change log here.

🚀 What is Opik?

Opik is an open-source platform for evaluating, testing and monitoring LLM applications. Built by Comet.

You can use Opik for:

Development:
- Tracing: Track all LLM calls and traces during development and production (Quickstart, Integrations)
- Annotations: Annotate your LLM calls by logging feedback scores using the Python SDK or the UI.
- Playground: Try out different prompts and models in the prompt playground.
Evaluation: Automate the evaluation process of your LLM application:
- Datasets and Experiments: Store test cases and run experiments (Datasets, Evaluate your LLM Application)
- LLM as a judge metrics: Use Opik’s LLM as a judge metric for complex issues like hallucination detection, moderation and RAG evaluation (Answer Relevance, Context Precision
- CI/CD integration: Run evaluations as part of your CI/CD pipeline using our PyTest integration
Production Monitoring:
- Log all your production traces: Opik has been designed to support high volumes of traces, making it easy to monitor your production applications. Even small deployments can ingest more than 40 million traces per day!
- Monitoring dashboards: Review your feedback scores, trace count and tokens over time in the Opik Dashboard.
- Online evaluation metrics: Easily score all your production traces using LLM as a Judge metrics and identify any issues with your production LLM application thanks to Opik’s online evaluation metrics

[!TIP]
If you are looking for features that Opik doesn’t have today, please raise a new Feature request 🚀

🛠️ Installation

Opik is available as a fully open source local installation or using Comet.com as a hosted solution. The easiest way to get started with Opik is by creating a free Comet account at comet.com.

If you’d like to self-host Opik, you can do so by cloning the repository and starting the platform using Docker Compose:

On Linux or Mac do:

# Clone the Opik repository
git clone https://github.com/comet-ml/opik.git

# Navigate to the repository
cd opik

# Start the Opik platform
./opik.sh

On Windows do:

# Clone the Opik repository
git clone https://github.com/comet-ml/opik.git

# Navigate to the repository
cd opik

# Start the Opik platform
powershell -ExecutionPolicy ByPass -c ".\opik.ps1"

Use the --help or --info options to troubleshoot issues.

Once all is up and running, you can now visit localhost:5173 on your browser!

For more information about the different deployment options, please see our deployment guides:

Installation methods	Docs link
Local instance
Kubernetes

🏁 Get Started

To get started, you will need to first install the Python SDK:

`1`	`pip install opik`

Once the SDK is installed, you can configure it by running the opik configure command:

`1`	`opik configure`

This will allow you to configure Opik locally by setting the correct local server address or if you’re using the Cloud platform by setting the API Key

[!TIP]
You can also call the opik.configure(use_local=True) method from your Python code to configure the SDK to run on the local installation.

You are now ready to start logging traces using the Python SDK.

📝 Logging Traces

The easiest way to get started is to use one of our integrations. Opik supports:

Integration	Description	Documentation
OpenAI	Log traces for all OpenAI LLM calls	Documentation
LiteLLM	Call any LLM model using the OpenAI format	Documentation
LangChain	Log traces for all LangChain LLM calls	Documentation
Haystack	Log traces for all Haystack calls	Documentation
Anthropic	Log traces for all Anthropic LLM calls	Documentation
Bedrock	Log traces for all Bedrock LLM calls	Documentation
CrewAI	Log traces for all CrewAI calls	Documentation
DeepSeek	Log traces for all DeepSeek LLM calls	Documentation
DSPy	Log traces for all DSPy runs	Documentation
Gemini	Log traces for all Gemini LLM calls	Documentation
Groq	Log traces for all Groq LLM calls	Documentation
Guardrails	Log traces for all Guardrails validations	Documentation
Instructor	Log traces for all LLM calls made with Instructor	Documentation
LangGraph	Log traces for all LangGraph executions	Documentation
LlamaIndex	Log traces for all LlamaIndex LLM calls	Documentation
Ollama	Log traces for all Ollama LLM calls	Documentation
Predibase	Fine-tune and serve open-source Large Language Models	Documentation
Pydantic AI	Fine-tune and serve open-source Large Language Models	Documentation
Ragas	PydanticAI is a Python agent framework designed to build production apps	Documentation
watsonx	Log traces for all watsonx LLM calls	Documentation

[!TIP]
If the framework you are using is not listed above, feel free to open an issue or submit a PR with the integration.

If you are not using any of the frameworks above, you can also use the track function decorator to log traces:

import opik

opik.configure(use_local=True) # Run locally

@opik.track
def my_llm_function(user_question: str) -> str:
    # Your LLM code here

    return "Hello"

[!TIP]
The track decorator can be used in conjunction with any of our integrations and can also be used to track nested function calls.

🧑‍⚖️ LLM as a Judge metrics

The Python Opik SDK includes a number of LLM as a judge metrics to help you evaluate your LLM application. Learn more about it in the metrics documentation.

To use them, simply import the relevant metric and use the score function:

from opik.evaluation.metrics import Hallucination

metric = Hallucination()
score = metric.score(
    input="What is the capital of France?",
    output="Paris",
    context=["France is a country in Europe."]
)
print(score)

Opik also includes a number of pre-built heuristic metrics as well as the ability to create your own. Learn more about it in the metrics documentation.

🔍 Evaluating your LLM Application

Opik allows you to evaluate your LLM application during development through Datasets and Experiments.

You can also run evaluations as part of your CI/CD pipeline using our PyTest integration.

⭐ Star Us on GitHub

If you find Opik useful, please consider giving us a star! Your support helps us grow our community and continue improving the product.

🤝 Contributing

There are many ways to contribute to Opik:

Submit bug reports and feature requests
Review the documentation and submit Pull Requests to improve it
Speaking or writing about Opik and letting us know
Upvoting popular feature requests to show your support

To learn more about how to contribute to Opik, please see our contributing guidelines.

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